AMPLICON-BASED SEQUENCING USING DNA SPIKE-INS

Abstract

Embodiments disclosed herein provide methods of using synthetic DNA spike-ins (SDSIs) to detect, prevent, and quantify contamination in amplicon sequencing. These embodiments may, but are not limited to, reveal sample swaps, intra-batch contamination, and, on a larger scale, intra-laboratory contamination. Embodiments disclosed herein also provide synthetic DNA spike-ins for use in amplicon-based sequencing methods.

Claims

1. A method of detecting and preventing contamination in amplification-based assays comprising: a. adding a synthetic DNA spike-in (SDSI) to one or more samples, wherein each SDSI is capable of amplification simultaneously with one or more cDNA samples, and wherein each SDSI comprises a unique sequence capable of differentiating each SDSI; b. amplifying one or more target sequences and SDSI in the one or more samples; c. sequencing the amplified target sequences and SDSI; and d. determining the presence of SDSI sequences from the one or more samples, wherein detection of a single SDSI per sample indicates contamination-free amplification, and wherein detection of more than one SDSI per sample indicates possible contamination.

2. The method of claim 1, wherein the SDSI contains a unique core region and a primer binding region at the 3′ end and the 5′ end, wherein the SDSI minimizes self-hybridization and cross-hybridization with nucleic acids in the sample.

3. The method of claim 2, wherein the core sequence homology is less than 65%, or less than 60%, or less than 55%, or less than 50%, or less than 45%, or less than 40%, or less than 35%, or less than 30%, or less than 25%, or less than 20%, or less than 15%, or less than 5%, or less than 1% to a sample sequence.

4. The method of claim 2, wherein the core sequence homology is less than 15, or less than 20, or less than 25, or less than 30, or less than 35, or less than 40, or less than 45, or less than 50 contiguous bases in common with the sample sequence.

5. The method of claim 2, wherein the SDSI sequences are 50-5000 nucleotides in length.

6. The method of claim 2, wherein the core sequence of the SDSI sequence is derived from a rare organism, optionally wherein the rare organism is a thermophilic archaea.

7. (canceled)

8. The method of claim 2, wherein the core sequence of the SDSI comprises a sequence as set forth in SEQ ID NOS: 1-96 and 193-291, optionally wherein one or more of the core sequences SEQ ID NOS: 16, 57, and 66 are substituted for SEQ ID NOS: 289, 290, and 291 respectively.

9. The SDSIs of claim 2, wherein the SDSIs comprise one or more of SEQ ID NOS: 97-192 and 292-390, optionally wherein one or more of the core sequences SEQ ID NOS: 112, 153, and 169 are substituted for SEQ ID NOS: 388, 389, 390 respectively.

10. The method of claim 2, wherein the primer binding sites have a Tm between 55-65° C.

11. The SDSIs of claim 2, wherein the primer binding sequences are complementary to the primers having SEQ ID NOS: 391 and 392.

12. (canceled)

13. (canceled)

14. The method of claim 1, wherein the concentration of the SDSI ranges from 0.1 femtomolar-1.0 femtomolar.

15. The method of claim 1, wherein the sample is for sequencing a pathogen or family of pathogens, optionally wherein the pathogen is a virus or a bacteria and the region of the bacteria sequenced is associated with antibiotic resistance.

16. (canceled)

17. (canceled)

18. The method of claim 1, wherein each sample contains a viral nucleic acid sequence.

19. The method of claim 1, wherein the samples are for creating one or more sequencing families/clusters.

20. The method of claim 1, wherein the cDNA and SDSI are simultaneously obtained by reverse transcription from their respective RNA.

21. A set of synthetic DNA spike-ins (SDSIs), each spike-in in the set comprising a primer binding sequence at the 3′ and 5′ end and a unique core sequence between the 3′ and 5′ primer binding sequences, wherein the SDSI minimizes self-hybridization and cross-hybridization with nucleic acids in the sample.

22. The SDSIs of claim 21, wherein the sequence is 50-5000 nucleotides in length.

23. The SDSIs of claim 21, wherein the core sequence homology is less than 65%, or less than 60%, or less than 55%, or less than 50%, or less than 45%, or less than 40%, or less than 35%, or less than 30%, or less than 25%, or less than 20%, or less than 15%, or less than 5%, or less than 1% to a sample sequence.

24. The SDSIs of claim 21, wherein the core sequence homology is less than 15, or less than 20, or less than 25, or less than 30, or less than 35, or less than 40, or less than 45, or less than 50 contiguous bases in common with the sample sequence.

25. The SDSIs of claim 21, wherein the unique core sequence is derived from a rare organism, optionally wherein the rare organism is a thermophilic archaea.

26. (canceled)

27. The SDSIs of claim 21, wherein the set comprises at least 96 spike-ins.

28. The SDSIs of claim 21, wherein the core sequence are the unique sequences as set forth in SEQ ID NOS: 1-96 and 195-293.

29. The SDSIs of claim 21, wherein the SDSIs comprise one or more of SEQ ID NOS: 97-192 and 294-392.

30. The SDSIs of claim 21, wherein the primer binding sites have a Tm between 55-65° C.

31. The SDSIs of claim 21, wherein the primer binding sequences are complementary to the primers having SEQ ID NOS: 391 and 392.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0017] An understanding of the features and advantages of the present invention will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the invention may be utilized, and the accompanying drawings of which:

[0018] FIG. 1—SDSI-ARTIC Amplicon-Sequencing Protocol—Illustrative workflow for 48 samples through the SDSI+ARTIC amplicon-sequencing pipeline. A synthetic DNA spike-ins (SD SI) will be added to each sample to allow for contamination tracking and accurate sample identification.

[0019] FIG. 2A-2C—Synthetic DNA oligos spiked into amp-seq reactions flag contamination and sample swaps—A. Schematic detailing SDSI design. Each oligo contains 140 bp of sui generis sequence flanked by unique primer binding sites. Primers designed to amplify SDSIs are added to ARTIC primer pools, and a unique SDSI is added to each clinical sample. Identification of multiple SDSIs in the same sample indicates contamination. B. In a titration of SDSIs across clinical samples with variable CTs, the number of reads mapping to both SARS-CoV-2 and the SDSI were quantified, and the percentage of each was calculated. C. For each of 48 unique clinical samples (on the horizontal axis), reads mapping to each of 48 unique SDSIs (on the vertical axis) were quantified; the log of this read count is represented by the intensity of color displayed. Samples and SDSIs were ordered such that the intended match is on the diagonal of this matrix, thus any off-diagonal signal would reveal non-specific identification of SDSIs or contamination of SDSIs across samples

[0020] FIG. 3A-3D—Maximizing Genome Recovery and Coverage with SDSI-ARTIC—A. The percent of the target genome covered at various depths of coverage when three reverse transcriptases, Superscript III, Superscript IV, and Superscript VILO were used for cDNA synthesis. Data represents four individual samples. B. Amplicons with at least 0.2× of the mean amplicon coverage with the normal ARTIC v3 primer pools or with a modified primer pool with a 2× concentration of 20 different ARTIC primer pairs. Four samples with low, mid-low, mid-high, and high CTs were used. C. Gini coefficients for two mid-high CT samples and four high CT when using either 35, 40, or 45 cycles for the ARTIC PCR. Error bars represent standard deviation. D. Comparison of Nextera DNA Flex and Nextera XT on the number of SARS-CoV-2 base pairs covered at various depths of coverage for three samples at different CTs.

[0021] FIG. 4A-4C—Improved amp-seq assembles more complete genomes than metagenomic sequencing with few errors across a wide range of samples—A. SDSI+ARTIC (N=81) and metagenomic (N=81) assembly lengths. All samples were downsampled to 975,000 reads. Dotted line indicates median assembly length (SDSI+ARTIC=29,577; Metagenomic=4,389) B. Percent of assemblies with greater than 98% or 80% coverage in different CT bins (SDSI+ARTIC N=81, Metagenomic N=81) (downsampled to 975,000 reads). C. SNP concordance plot between SDSI+ARTIC and metagenomic consensus sequences. Two discordant SNPs, outlined in a red box, were found.

[0022] FIG. 5A-5C—Rapid deployment of optimized amp-seq to determine a nosocomial transmission cluster—A. Phylogenetic tree showing the location of the putative cluster sequences in the context of a global subset of circulating SARS-CoV-2 diversity. Zoom box shows the 10 highly similar cluster genomes. Sample named on the main tree is the one putative cluster sample that was excluded from the cluster based on genome sequence. B. Distance matrix showing pairwise differences between the 17 complete genomes assembled from this sample set. Putative cluster samples are bolded. C. Spike-in counts for each of the 24 samples and water controls in this sequencing batch.

[0023] FIG. 6A-6C—Spike-in validation—A. 100 fmol DNA spike-in amplified under standard ARTIC PCR conditions for 40 cycles run on 2.2% agarose gel image with 188 bp amplified spike-in (SDSI 1-48) B. RT-PCR for Spike-in and spike-in specific primers, Spike-in specific primers water control, Spike-in with COVID positive cDNA and spike-in specific primers, COVID positive cDNA and spike-in specific primers. C. Both SDSIs and ARTIC amplicons avoid extremes of GC content, and the two have generally overlapping distributions. SDSI primers also have a length and GC content similar to the average ARTIC v3 primer, resulting in a compatible TM.

[0024] FIG. 7—SDSI Titration—Coverage plots for four different SDSI concentrations (1fM, 0.1fM, 0.01fM, 0.001fM) at four different CT dilutions (CT=20,25,30,35).

[0025] FIG. 8A-8C—Comparison to alternate amp-seq strategies—A. Three representative coverage plots for CT 20, CT 25, and CT 30 samples. B. SNP detection for the CT 20 and CT 25 sample. ARTIC and Paragon consensus sequences were compared to the metagenomic consensus sequences. The SNP that was not called in Paragon was due to low coverage at that position. Analysis was performed with assemblies generated with a minimum coverage of both 3 and 20, yielding identical results. C. Base pairs of the SARS-CoV-2 genome covered for the modified ARTIC pipeline versus Paragon CleanPlex Panel at different depths of coverage. Five samples at varying CTs were compared.

[0026] FIG. 9A-9D—RT comparisons for cDNA length—A. Read depth across each nucleotides position for the same sample (CT=13.89) when using three different reverse transcriptases (SSIII, SSIV, or SSVILO) for cDNA synthesis. B. Base pairs of the SARS-CoV-2 genome covered at various depths when using different enzymes for the ARTIC PCR. C. Base pairs of the SARS-CoV-2 genome covered at various depths when using either normal ramping speed (3° C./s) for the ARTIC PCR or reduce the ramping (1.5° C./s). D. Read depth across each nucleotides position for normal ARTIC PCR vs an alternate hybridization PCR.

[0027] FIG. 10—Increasing primer concentration 2-fold in regions of low amplicon coverage—Red asterisk indicates amplicons in which the primer pairs were spiked in at 2× the concentration of the others in the pool. Box plots showing the distribution of absolute sequencing coverage (log 10) per amplicon for ARTIC PCR conditions (Normal) and Primer 2× concentrations for 4 representative samples. The boxes are plotted by the Q1, median, and Q3, the whiskers by Q1/Q4, and the outliers by the dots.

[0028] FIG. 11—Modified Flex outperforms XT in coverage depth and evenness at lower cost—Illumina Nextera XT and modified Illumina Nextera Flex library construction on three samples with varying CTs. Asterisks indicate amplicons with large levels of drop out that were improved with the Nextera Flex. Plotted is the mean sequencing depth (log 10) per amplicon.

[0029] FIG. 12A-12C—SDSI+ARTIC over a diverse set of samples is advantageous when compared to metagenomics—A. Time-measured maximum clade credibility tree of 772 genomes from Massachusetts, reported in Lemieux et al., 2020. The 89 samples compared for metagenomic and amplicon sequencing are shown with red dots. B. Genome coverage for metagenomics versus SDSI+ARTIC amplicon sequencing pipeline (N=81, excluded samples had no detectable CT). All samples downsampled to 975,000 reads. C. Gini coefficients grouped by CT (N=70, excluded samples that did not generate assemblies in either one or both methods). Dashed red line represents the median.

[0030] FIG. 13—SDSI+AmpSeq Protocol. Illustrative workflow for 96 samples through the SDSI+AmpSeq amplicon-sequencing pipeline. A unique, synthetic DNA spike-in (SDSI) will be added to each cDNA sample to allow for contamination tracking and accurate sample identification in analysis. Asterisks indicate additional steps to the standard ARTIC pipeline.

[0031] FIG. 14A-14B—Synthetic DNA oligos spiked into amp-seq reactions designed to flag contamination and sample swaps. A. Schematic of SDSI design. Each oligo contains 140 bp of unique sequence flanked by common primer binding sites. Primers designed to amplify all SDSIs are added to ARTIC primer pools, and a unique SDSI is added to each clinical sample. Identification of multiple SDSIs in the same sample indicates contamination. B. Percent of SDSI reads mapping for each of the 96 SDSIs (horizontal axis) were quantified for each of the 96 SDSIs (vertical axis). Any off-diagonal signal would indicate non-specific identification of SDSIs.

[0032] FIG. 15A-15C—SDSI+AmpSeq amplicon coverage and genome concordance. A. Percent of SDSI for SDSI 1-96 in patient samples. B. Log of the mean amplicon coverage for the same clinical samples run with and without an SDSI (n=14). A unique SDSI was used in each sample. The solid blue line represents SDSI+AmpSeq and the solid black line is ARTIC only with no SDSI. Blue and black shading around the solid lines represents the confidence interval. There were no statistical differences (p-value >0.05) in the mean amplicon coverage for each amplicon between the groups (two-tailed Mann Whitney t-test and multiple comparison two-stage step-up Benjamini, Krieger, and Yekutieli test with FDR set to 5%). C. SNV concordance plot between SDSI+AmpSeq and unbiased consensus sequences. Two discordant SNVs, outlined in a red box, were found. Blue dots represent SNVs found in both the unbiased and SDSI+AmpSeq method, whereas black dots indicate the SNV was only present in unbiased.

[0033] FIG. 16A-16C—SDSI+AmpSeq performs well across thousands of samples. A. Sample diversity from two different institutions representing a range of CTs, viral lineages, and states of sample collection from samples where the data was available. B. The percent of SDSI reads out of the sum of all SDSI reads that map to the correct spike-in (Left: JAX, N=3,838, Right: Broad, N=2,903). Error bars represent SEM. C. The percent of SDSI reads over the total of all sequenced reads for all SARS-CoV-2 positive samples (Left: JAX, N=3,093, Right: Broad, N=2,670). Error bars represent SEM.

[0034] FIG. 17A-17C—SDSI+AmpSeq is used to identify sample swaps and contamination. A. Intentional SDSI contamination experiment (run in duplicate) assessing if different ratios of contamination between SDSI 87 and SDSI 94 (SDSI 87:SDSI 94) were detectable with the SDSI+AmpSeq method. B. Examples of experimental errors that were caught using the SDSI+AmpSeq method. C. Top: Distance matrix showing pairwise differences between the 17 complete genomes assembled from this sample set. Putative cluster samples are bolded. Bottom: Spike-in counts for each of the 24 samples and water controls in this sequencing batch.

[0035] FIG. 18A-18B—SDSI core sequence in silico validation. Applicants surveyed the core SDSI sequences by BLASTn to identify significant homology. A. Significant homology between SDSIs and anything in the NCBI database outside the domain archaea was identified and the SDSI and genus were plotted if identity (y-axis) was greater than 90% and query cover (x-axis) was greater than 50 bps. B. For each SDSI, Applicants identified and plotted (see color scale) the maximum alignment score for a significant homology to human (taxid:9606) and viral (taxid:10239) sequences in the NCBI database. Applicants also identified and plotted the alignment score for each pairwise combination of SDSIs.

[0036] FIG. 19A-19E—Spike-in validation. A. RT-PCR for an SDSI in water and a SARS-CoV-2 positive clinical sample background. Mastermix and SDSI specific primers were added to all samples. SARS-CoV-2 positive clinical sample is cDNA generated from a nasopharyngeal (NP) swab. B. The distribution of GC content and length for ARTIC v3 primers. C. The distribution of GC content of SDSI amplicons. D. 100 fmol DNA spike-in amplified under standard ARTIC PCR conditions for 40 cycles run on 2.2% agarose gel image with 188 bp amplified spike-in (SDSI 1-48). E. % SDSI reads over total reads for SDSI (2-48) over a range of SDSI GC % (33%-65.4%) showed no significant read depth bias. Error bars represent 95% CI. Linear regression p-value=0.8160 (Broad, N=2,903).

[0037] FIG. 20A-20B—SDSI Titration. A. In a titration of SDSI 49 across one clinical sample (CT=16) mock diluted to various CTs (CT=20,25,30,35), the number of reads mapping to both SARS-CoV-2 and the SDSI were quantified, and the percentage of each was calculated. SDSI 49 was tested at 600,60,6, and 0.6 copies/uL in each mock diluted sample. B. Coverage plots for the SDSI 49 titration experiment.

[0038] FIG. 21A-21B—ARTIC SARS-CoV-2 amplicon sequencing with and without SDSI and normalization. A. In three different CT bins, Applicants showed coverage plots with confidence intervals for multiple samples sequenced with and without SDSIs (CT<27, n=4; CT 27-29, n=6; CT>30, n=4). The solid blue line represents SDSI+AmpSeq and the solid black line is ARTIC only with no SDSI. Blue and black shading around the solid lines represents the confidence interval. There were no significant differences (p-value >0.05) between the with and without SDSI group for the mean coverage at any of the amplicons (two-tailed Mann Whitney t-test and multiple comparison two-stage step-up Benjamini, Krieger, and Yekutieli test with FDR set to 5%). B. The percentage of SDSI reads for 4 different SDSIs was assessed within 4 clinical samples that were run with and without CT normalization of the cDNA prior to the ARTIC PCR.

[0039] FIG. 22A-22E—SDSI+AmpSeq over a diverse set of samples has superior genome recovery and more coverage uniformity at higher CTs. A. Time-measured maximum clade credibility tree of 772 genomes from Massachusetts, reported in Lemieux et al., 2021. The 89 samples compared for metagenomic and amplicon sequencing are shown with red dots. B. Percent of assemblies with greater than 98% or C. 80% coverage in different CT bins (SDSI+AmpSeq N=81; Unbiased N=81) (downsampled to 975,000 reads). D. Genome coverage for unbiased metagenomic sequencing versus SDSI+AmpSeq amplicon sequencing pipeline (N=81, excluded samples had no detectable CT). All samples downsampled to 975,000 reads. E. Gini coefficients grouped by CT (N=70, excluded samples that did not generate assemblies in either one or both methods). Dashed red line represents the median. Error bars represent standard deviation.

[0040] FIG. 23A-2311—Maximizing Genome Recovery and Coverage with SDSI+AmpSeq. A. The percent of the target genome covered at various depths of coverage for four individual samples (CT=13.9, 23.9, 29.6, 33.6), with each undergoing cDNA with three different reverse transcriptases (SSIII, SSIV, or SSVILO). Yellow bar highlights comparison between the reverse transcriptases at a coverage depth of 10×. B. Read depth across each nucleotide position for the same sample (CT=13.9) when using these reverse transcriptases. C. Base pairs of the SARS-CoV-2 genome covered at various depths when using different enzymes for the ARTIC PCR (n=1). D. Amplicons with at least 0.2× of the mean amplicon coverage with the normal ARTIC v3 primer pools or with a modified primer pool with a 2× concentration of 20 poor-performing ARTIC primer pairs. Six individual samples with different CTs were used. E. Read depth across each nucleotide position for normal ARTIC PCR vs an alternate hybridization PCR (n=1). F. Base pairs of the SARS-CoV-2 genome covered at various depths when using either normal ramping (3° C./s) or reduced ramping (1.5° C./s) speed for the ARTIC PCR (n=1). G. Gini coefficients for two mid-high CT samples and four high CT samples when using either 35, 40, or 45 cycles for the ARTIC PCR. Error bars represent standard deviation. H. Comparison of Nextera DNA Flex and Nextera XT on the number of SARS-CoV-2 base pairs covered at various depths of coverage for three samples with different CTs.

[0041] FIG. 24—Increasing primer concentration 2-fold in regions of low amplicon coverage. Data represents 6 individual samples at different CTs.

[0042] FIG. 25—Unique identification of SDSIs given varying thresholds of SDSI mapping stringency. Applicants considered a range of cutoffs of the percentage of all SDSI-mapped reads mapping to a given SDSI (0.01%-50%, with a step size of 0.01). For an experiment where Applicants sequenced SDSIs without any clinical sample, Applicants calculated, at each cutoff, the number of SDSIs (y-axis) in the set Applicants present (96 total) for which only the expected SDSI had a proportion of mapped reads that exceeded the cutoff (x-axis). Assuming no contamination, all 96 SDSIs should be identified uniquely, i.e. no other SDSI should have a proportion of mapped reads that exceeds the cutoff. The dotted line at x=5% represents the stringency cutoff that Applicants recommend in practice to detect contamination events.

[0043] FIG. 26—Deployment of SDSI+AmpSeq to assess for possible nosocomial transmission. Phylogenetic tree showing the location of the putative cluster sequences in the context of a global subset of circulating SARS-CoV-2 diversity. Zoom box shows the 10 highly similar cluster genomes. Sample named on the main tree is the one putative cluster sample that was excluded from the cluster based on genome sequence.

[0044] FIG. 27A-27B—Modification enables addition of spike-ins to RNA. A. A schematic of how to design, produce, and apply synthetic RNA spike-ins (SRSIs). B. A limited titration experiment where SRSIs of varying concentrations were added to two clinical samples with low and intermediate SARS-CoV-2 Cts. SRSIs were added to the sample at the RNA stage; the sample with a low CT (20) was then normalized to CT 25 at the cDNA stage, whereas the sample with mid CT (26) was not normalized.

[0045] The figures herein are for illustrative purposes only and are not necessarily drawn to scale.

DETAILED DESCRIPTION OF THE EXAMPLE EMBODIMENTS

General Definitions

[0046] Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. Definitions of common terms and techniques in molecular biology may be found in Molecular Cloning: A Laboratory Manual, 2.sup.nd edition (1989) (Sambrook, Fritsch, and Maniatis); Molecular Cloning: A Laboratory Manual, 4.sup.th edition (2012) (Green and Sambrook); Current Protocols in Molecular Biology (1987) (F. M. Ausubel et al. eds.); the series Methods in Enzymology (Academic Press, Inc.): PCR 2: A Practical Approach (1995) (M. J. MacPherson, B. D. Hames, and G. R. Taylor eds.): Antibodies, A Laboratory Manual (1988) (Harlow and Lane, eds.): Antibodies A Laboratory Manual, 2.sup.nd edition 2013 (E. A. Greenfield ed.); Animal Cell Culture (1987) (R. I. Freshney, ed.); Benjamin Lewin, Genes IX, published by Jones and Bartlet, 2008 (ISBN 0763752223); Kendrew et al. (eds.), The Encyclopedia of Molecular Biology, published by Blackwell Science Ltd., 1994 (ISBN 0632021829); Robert A. Meyers (ed.), Molecular Biology and Biotechnology: a Comprehensive Desk Reference, published by VCH Publishers, Inc., 1995 (ISBN 9780471185710); Singleton et al., Dictionary of Microbiology and Molecular Biology 2nd ed., J. Wiley & Sons (New York, N.Y. 1994), March, Advanced Organic Chemistry Reactions, Mechanisms and Structure 4th ed., John Wiley & Sons (New York, N.Y. 1992); and Marten H. Hofker and Jan van Deursen, Transgenic Mouse Methods and Protocols, 2nd edition (2011).

[0047] As used herein, the singular forms “a”, “an”, and “the” include both singular and plural referents unless the context clearly dictates otherwise.

[0048] The term “optional” or “optionally” means that the subsequent described event, circumstance or substituent may or may not occur, and that the description includes instances where the event or circumstance occurs and instances where it does not.

[0049] The recitation of numerical ranges by endpoints includes all numbers and fractions subsumed within the respective ranges, as well as the recited endpoints.

[0050] The terms “about” or “approximately” as used herein when referring to a measurable value such as a parameter, an amount, a temporal duration, and the like, are meant to encompass variations of and from the specified value, such as variations of +/−10% or less, +/−5% or less, +/−1% or less, and +/−0.1% or less of and from the specified value, insofar such variations are appropriate to perform in the disclosed invention. It is to be understood that the value to which the modifier “about” or “approximately” refers is itself also specifically, and preferably, disclosed.

[0051] As used herein, a “biological sample” may contain whole cells and/or live cells and/or cell debris. The biological sample may contain (or be derived from) a “bodily fluid”. The present invention encompasses embodiments wherein the bodily fluid is selected from amniotic fluid, aqueous humour, vitreous humour, bile, blood serum, breast milk, cerebrospinal fluid, cerumen (earwax), chyle, chyme, endolymph, perilymph, exudates, feces, female ejaculate, gastric acid, gastric juice, lymph, mucus (including nasal drainage and phlegm), pericardial fluid, peritoneal fluid, pleural fluid, pus, rheum, saliva, sebum (skin oil), semen, sputum, synovial fluid, sweat, tears, urine, vaginal secretion, vomit and mixtures of one or more thereof. Biological samples include cell cultures, bodily fluids, cell cultures from bodily fluids. Bodily fluids may be obtained from a mammal organism, for example by puncture, or other collecting or sampling procedures.

[0052] The terms “subject,” “individual,” and “patient” are used interchangeably herein to refer to a vertebrate, preferably a mammal, more preferably a human. Mammals include, but are not limited to, murines, simians, humans, farm animals, sport animals, and pets. Tissues, cells and their progeny of a biological entity obtained in vivo or cultured in vitro are also encompassed.

[0053] Various embodiments are described hereinafter. It should be noted that the specific embodiments are not intended as an exhaustive description or as a limitation to the broader aspects discussed herein. One aspect described in conjunction with a particular embodiment is not necessarily limited to that embodiment and can be practiced with any other embodiment(s). Reference throughout this specification to “one embodiment”, “an embodiment,” “an example embodiment,” means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, appearances of the phrases “in one embodiment,” “in an embodiment,” or “an example embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment, but may. Furthermore, the particular features, structures or characteristics may be combined in any suitable manner, as would be apparent to a person skilled in the art from this disclosure, in one or more embodiments. Furthermore, while some embodiments described herein include some but not other features included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the invention. For example, in the appended claims, any of the claimed embodiments can be used in any combination.

[0054] All publications, published patent documents, and patent applications cited herein are hereby incorporated by reference to the same extent as though each individual publication, published patent document, or patent application was specifically and individually indicated as being incorporated by reference.

Overview

[0055] Embodiments disclosed herein provide a method of detecting and preventing contamination during genome profiling using synthetic DNA spike-ins (SDSIs). Embodiments disclosed herein also provide methods to track sample contamination by implementing synthetic DNA spike-ins (SDSIs) for sample verification. Embodiments disclosed herein also provide synthetic DNA spike-ins (SDSIs) and methods for producing synthetic DNA spike-ins (SDSIs). The global spread and continued evolution of SARS-CoV-2 has driven an unprecedented surge in viral genomic surveillance. Amplicon-based sequencing methods provide a sensitive, low-cost and rapid approach but suffer a high potential for contamination, which can undermine laboratory processes and results. This challenge will only increase with expanding global production of sequences by diverse laboratories for epidemiological and clinical interpretation, as well in genomic surveillance in future outbreaks. Applicants present SDSI+AmpSeq, an approach which uses synthetic DNA spike-ins (SDSIs) to track samples and detect inter-sample contamination through the sequencing workflow. Applying SDSIs to the ARTIC Consortium's amplicon design, Applicants demonstrated their utility and efficiency in a real-time investigation of a suspected hospital cluster of SARS-CoV-2 cases and across thousands of diagnostic samples at multiple laboratories. Applicants established that SDSI+AmpSeq provides increased confidence in genomic data by detecting and in some cases correcting for relatively common, yet previously unobserved modes of error without impacting genome recovery.

[0056] The methods described herein add a unique SDSI to each sample (e.g., cDNA) before performing a sequence amplification process during which the samples and SDSIs are amplified in the same reactions. This procedure can be repeated in parallel for each sample undergoing analysis. After the samples have been amplified, the presence of the SDSI is measured. If the SDSI introduced before amplification is the only SDSI present, then the sample is determined to be uncontaminated. However, the presence of any other SDSI immediately reveals contamination of the sample. This method provides a reliable safety measure for pathogen-genome studies and the resulting therapeutic and preventative medicine.

Synthetic DNA Spike-In (SDSI)

[0057] In one aspect, the present invention is directed to SDSI's and uses thereof. An example SDSI comprises, in a 5′ to 3′ direction, a 5′ primer binding sequence, a core sequence, and a 3′ primer binding sequence. In one example embodiment, spike-ins comprise sequences derived from a rare organism. A rare organism is a species that is limited in number or geographic occurrence relative to the distribution and abundance of other species making up the pool of interest. (Raphael, M. et al., Conservation of Rare or Little-Known Species: Biological, Social, and Economic Considerations. Bibliovault OAI Repository (2007) the University of Chicago Press) In some embodiments, the rare organism is an archaea. In some embodiments, the archaea is thermophilic. A thermophilic archaea may exist in environments with temperatures greater than 50° C. In certain embodiments, the present invention includes spike-ins. In certain embodiments, a spike-in comprises a DNA sequence that is not from the target organism. In certain embodiments, a spike in is an RNA molecule that can be added to a sample comprising pathogen RNA. In certain embodiments, the RNA is converted to cDNA concurrently with pathogen RNA. The RNA spike in cDNA can then be amplified with pathogen cDNA using pathogen specific primers and spike-in specific primers.

[0058] In certain embodiments, a spike-in sequence is compared to the target organism and the host for the target organism to limit homology. Limited homology can be determined using a BLAST search of all SDSIs. In one example embodiment, a permissive BLAST search is used (e.g., blastn; 5000 max targets; E=10; ws=11; no mask for low-complexity). Results may be filtered by species of interest, e.g. Homo sapiens. In one example embodiment, results can be filtered for a pathogen of interest (e.g., SARS-CoV-2). The query coverage and sequence identity may each be set for 35-100%, preferably, 50-100%, and sequences having no significant hits can be selected for use as a spike-in. In certain embodiments, a spike-in set comprises different DNA sequences that can be easily distinguished using sequencing.

[0059] In certain embodiments, the GC content of the spike-ins promote similar amplification rates across pathogen targets and the different SDSIs in our set. In one example embodiment, a spike-in comprises a similar GC content as the target organism. In another example embodiment, the GC content of the primer may range from 30%-80%. (Buck, G. A. et al., Design Strategies and Performance of Custom DNA Sequencing Primers, BioTechniques (1999) 27:3, 528-536). In another example embodiment, the GC content of the primer may range from or between 30%-40% nucleotides, or between 40%-50% nucleotides, or between 50%-60% nucleotides, or between 60%-70% nucleotides, or between 70%-80% nucleotides. In general GC content extremes are avoided. For example, sequences may have a median of 50% GC content, preferably, between 35-65%. In another example embodiment, the GC content of the primer may range from or between 40%-70%, or between 30%-50% nucleotides, or between 30%-60% nucleotides, or between 30%-70% nucleotides.

Core Sequence

[0060] Each SDSI in the set is differentiated by its core sequences. The SDSI cores are designed to minimize self-hybridization and cross-hybridization with others nucleic acids in a given sample. Accordingly, core sequences are selected based on the type of target sequence to be amplified and the type of sample the target sequence is to be derived from. For example, in the context of detecting a pathogen in a human sample, core sequence should be selected with minimal homology to the target pathogen, other common microbes and non-target pathogens that might be present in the sample, and human sequences as well. In certain example embodiments, the core sequence has a homology of less than about 65%, or less than 64%, or less than 63%, or less than 62%, or less than 61%, or less than 60%, or less than 59%, or less than 58%, or less than 57%, or less than 56%, or less than 55%, or less than 54%, or less than 53%, or less than 52%, or less than 51%, or less than 50%, or less than 49%, or less than 48%, or less than 47%, or less than 46%, or less than 45%, or less than 44%, or less than 43%, or less than 42%, or less than 41%, or less than 40%, or less than 35%, or less than 30%, or less than 25%, or less than 20%, or less than 15%, or less than 10%, or less than 5%, or less than 1%.

[0061] The core sequence may vary in length between 50-5,000 nucleotides, or between 50-nucleotides, or between 50-4,500 nucleotides, or between 50-4,000 nucleotides, or between 50-4,000 nucleotides, or between 50-3,500 nucleotides, or between 50-3,000 nucleotides, or between 50-2,500 nucleotides, or between 50-2,000 nucleotides, or between 50-1,500 nucleotides, or between 50-1,000 nucleotides, or between 50-500 nucleotides.

[0062] The core sequence may vary in length between 50-60 nucleotides, or between 50-70 nucleotides, or between 50-80 nucleotides, or between 50-90 nucleotides, or between 50-100 nucleotides, or between 50-110 nucleotides, or between 50-120 nucleotides, or between 50-130 nucleotides, or between 50-140 nucleotides, or between 50-150 nucleotides, or between 50-160 nucleotides, or between 50-170 nucleotides, or between 50-180 nucleotides, or between 50-190 nucleotides, or between 50-200 nucleotides, or between 50-210 nucleotides, or between 50-220 nucleotides, or between 50-230 nucleotides, or between 50-240 nucleotides, or between 50-250 nucleotides, or between 50-260 nucleotides, or between 50-270 nucleotides, or between 50-280 nucleotides, or between 50-290 nucleotides, or between 50-300 nucleotides, or between 50-310 nucleotides, or between 50-320 nucleotides, or between 50-330 nucleotides, or between 50-340 nucleotides, or between 50-350 nucleotides, or between 50-360 nucleotides, or between 50-370 nucleotides, or between 50-380 nucleotides, or between 50-390 nucleotides, or between 50-400 nucleotides, or between 50-410 nucleotides, or between 50-420 nucleotides, or between 50-430 nucleotides, or between 50-440 nucleotides, or between 50-450 nucleotides, or between 50-460 nucleotides, or between 50-470 nucleotides, or between 50-480 nucleotides, or between 50-490 nucleotides, or between 50-500 nucleotides, or between 50-510 nucleotides, or between 50-520 nucleotides, or between 50-530 nucleotides, or between 50-540 nucleotides, or between 50-550 nucleotides, or between 50-560 nucleotides, or between 50-570 nucleotides, or between 50-580 nucleotides, or between 50-590 nucleotides, or between 50-600 nucleotides, or between 50-610 nucleotides, or between 50-620 nucleotides, or between 50-630 nucleotides, or between 50-640 nucleotides, or between 50-650 nucleotides, or between 50-660 nucleotides, or between 50-670 nucleotides, or between 50-680 nucleotides, or between 50-690 nucleotides, or between 50-700 nucleotides, or between 50-710 nucleotides, or between 50-720 nucleotides, or between 50-730 nucleotides, or between 50-740 nucleotides, or between 50-750 nucleotides, or between 50-760 nucleotides, or between 50-770 nucleotides, or between 50-780 nucleotides, or between 50-790 nucleotides, or between 50-800 nucleotides, or between 50-810 nucleotides, or between 50-820 nucleotides, or between 50-830 nucleotides, or between 50-840 nucleotides, or between 50-850 nucleotides, or between 50-860 nucleotides, or between 50-870 nucleotides, or between 50-880 nucleotides, or between 50-890 nucleotides, or between 50-900 nucleotides, or between 50-910 nucleotides, or between 50-920 nucleotides, or between 50-930 nucleotides, or between 50-940 nucleotides, or between 50-950 nucleotides, or between 50-960 nucleotides, or between 50-970 nucleotides, or between 50-980 nucleotides, or between 50-990 nucleotides, or between 50-1000 nucleotides, or between 50-1010 nucleotides.

[0063] The core sequence may vary in length between 100-5,000 nucleotides, or between 1,000-5,000 nucleotides, or between 2,000-5,000 nucleotides, or between 3,000-5,000 nucleotides, or between 4,000-5,000 nucleotides.

[0064] The core sequence may vary in length between 75-150 nucleotides, or between 100-150 nucleotides, or between 100-200 nucleotides, or between 100-300 nucleotides, or between 150-200, or between 150-250 nucleotides.

[0065] The homology to a target sequence or non-target sequence in the sample across the size of a given core sequence may be less than 1 nucleotide, or may be less than 2 nucleotides, or may be less than 3 nucleotides, or may be less than 4 nucleotides, or may be less than 5 nucleotides, or may be less than 6 nucleotides, or may be less than 7 nucleotides, or may be less than 8 nucleotides, or may be less than 9 nucleotides, or may be less than 10 nucleotides, or may be less than 11 nucleotides, or may be less than 12 nucleotides, or may be less than 13 nucleotides, or may be less than 14 nucleotides, or may be less than 15 nucleotides, or may be less than 16 nucleotides, or may be less than 17 nucleotides, or may be less than 18 nucleotides, or may be less than 19 nucleotides, or may be less than 20 nucleotides, or may be less than 21 nucleotides, or may be less than 22 nucleotides, or may be less than 23 nucleotides, or may be less than 24 nucleotides, or may be less than 25 nucleotides,

[0066] The homology to a target sequence or non-target sequence in the sample across the size of a given core sequence may vary in length between 1-5 nucleotides, or between 1-10 nucleotides, or between 1-15 nucleotides, or between 1-20 nucleotides, or between 1-25 nucleotides, or between 1-5 nucleotides, or between 5-10 nucleotides, or between 10-15 nucleotides, or between 15-20 nucleotides, or between 20-25 nucleotides, or between 1-10 nucleotides, or between 10-20 nucleotides, or between 20-30 nucleotides.

[0067] These SDSIs can be implemented in a wide range of genome profiling applications including, but not limited to, investigations of SARS-CoV-2 epidemiology and emerging viral variants. Exemplary SDSIs are provided in Table 1.

[0068] Table 1. Sequences of 96 unique SDSIs. The unique core of each SDSIs is 140 bps long (SEQ ID NOS: 1-96 and 193-291). The unique SDSIs including the priming regions (SEQ ID NOS: 97-192 and 292-390). Alternative sequences are also included. SEQ ID NOS: 16, 57, 66, 112, 153, and 162 can be, in the alternative, substituted with 289, 290, 291, 388, 389, and 390 respectively. Sequences for forward and reverse primers for amplifying the SDISs (SEQ ID NOS: 391 and 392 respectively).

TABLE-US-00001 TABLE 1 SEQ ID Core Sequences 1 CAATTGCTCCCTCGTATCCCTTGTACATTATCTCAGCTCCGCTTAATGATATTAATTTTACCTT GAGTGTTTTTGCTAAAGCCTTTGCCATCATCGTTTTACCTACTCCAGGTGGCCCGTAAAGCAAC ACAGCTTTGGCA 2 TTCTCCAAAACCTACCCAGTTCTCCGAGGAACCTCTTAGCATCTGTTAAATCGTTATTAGTATT AGCTTCCACCATCTCAAGTTCCTTTAAGGCGTTACTCACACTCTTCTTACCTATCTTTTAGAGA ACCACTCGTCAG 3 GTTATCAAAGCCCTTAAAGAGTGGTAGGGGCAAAAGTCTGAAGCGTCCTTACTTAACTGGAGTA TCTGAGATGGCCTTAATCCGCTTAGGTCTTTAATTTTATCCCTTAATGAACATTCCCTGCACTC TATGTCTTCGGG 4 GAGATGTAGCAGACGGGCTAAGAGTTTCAAACCCTCTAAGGATCACTACAAACAAGAGAGAGAG ACAATCCTCTCTTTTGTCTTGTCATTGTGTTTCAAACCCTCTAAGGATCACTACAAACATCTTT AACATAGATACC 5 GACCGGACGTTGTGATCACGGGTACCTTGATCTGGTACTCAAAGGTTTGCCCCCGTGAAGTCTG GTACATGGCTAGACACGTCACTCCATTCGAGGGACATTCGAAGTTAGAGAAGGGCAGAGCGATA CATCAGATATAT 6 GTCTTTTCTCTACTAATTCTCCTCACGAGATCTCTAAACATTCTTGCTGAAAGAGGATCCAAAC CTAATGTAGGTTCGTCAAGCAATAAAATTGGAGGATCAGTTATTAATGCTCTTGCTAAGGCTAG TTTCCTCTGCAT 7 GATTTTGCCATCATTAAAAACAACAATTTGATCACCCATAGTCATAGCTTCTAATTGATCGTGA GTTACATAAATACTTGTGGTGTTTAACATACGGTGAATATTTACAATTTCTCTTCGCATGTTTT CTCTTAGTTTAG 8 GTATCTTTCAATTCTCGAAAGAAAAGGTTACAAGTCTCATAGATTTATTCCTCTTCACTGTTGT ACGTTGGCAGCTAGAGAGAGTTTAGATTATGAGAAAATTAAGAGAATATATGAGGATTCGTTTT CTTGGTTTAAGT 9 CTAATTGATTTTCCTGTACCATGTGGTAAAACAACGCTACCTCTTAATTGTTGATCTGCTTTTC TAGTATCAAGATTTAATCTAAAAGCTAAATCAACTGAAGCATCAAATTTTGTATAAGAAGTTTT TTTCACTAATTC 10 TCGGTTTTCCCGTGAACTAATAAACACCTACTGGAGCCAAGAACGGGTCAGAATTGATGGAATA AACGTTGCGGAGAATGAAATTAATTTGTACATCAGAGACATTGATGACAACGGTGACCCTATAC AGTCAACTATAC 11 CTTAATGGAAAGTATGCTTTAGATACCTTCTGGAACGCTATCTCACTTGGCGGGAATTCAGATA TGGAGAGTAAATTAAGGGATCTGGAAGTAAAGTTAATGTCGTTAATCTATTTAAATGAGTCACC ATTAAAATCACC 12 CATAATATGTTAGAGGTAGAATTTCTTTGTGATAGAATATTATTGATGAATGATGGAAGAGAAT TAGCATTAGGAAAACCTAAGGAACTGGTAAAGGATACAGAATCTAAGAATCTTGAAGAGGTTTT CCTTAAACTTGT 13 CCTTACTTCATCTCTCAAGATAAGGGTAATAAGTTCACTTCAAATATCTGGTCTTATCGCAAGT TGATTGAGGCTATAGTGTATAAGCTCTATGAGTATGGTATAAACGTGTTCCTCGTTGTAGAGTA TAACACTTCACG 14 AGTCTAGGTTTTAATTCTTCAACTGCTTCAAATACTAGCTTACTGTAGTTATCTGCCCTCATGT TAGGATATATATCTGGAATATAAGGAGGTTGATGAGTTATAAGAAGTGGATGAAATTGTTGTCA CACACTCCCCTA 15 CTACCTCTTCGGCCTTGTACCAACGTACCCCTGATACAAGTTCCAAGCAGAGATGGAAAACTCG AAGATGGTATCACCCAAGATGAGATACGATATCAATGAAGGCGAGCCTAGGTACAAGTAAAGGG ATACCACGAGAG 16 CTCGTAAGCGTTTCCTACCCTCGAGAGGGCCATCCTGGTGGTGAGGAAGTCGTCGAAGTGGGCT AAGTAAAAAGCGAAGATCTCGACCCACAATTACCTCCTCCTGTACACCAGGAATACCCCTATCA GGATAGAGATAC 17 GCGCGTCCGGGTCGCGGCCGGGGACGACCGTCTTGACGAAGTCGGTCGACCCCTCGTCGGTCGA GATGGTCGTCACCTCGGTGTCGAGGCCGTACGTTTCGAGCGCGTCGCGTACCAGTTCGCCGTCC GCGTCGGGACGG 18 CATGTACTCGTTCCAGAAGGTGAGTTCGCTCCCCTCGATTTCGACCTCGCCCACGTCGAAGCCG CCGGTCGTTTCGAGCGCGAACGACTCGACGGGACCGACGAGCGAAACTTCGCCGCCGAGCACGT CGGCGACGCGTT 19 CTCGATGCGCTCGGGCTTGTAGGACTCCCCGAGGGCGTCCTTGTTGGTGAAGACGTTTTGTTTT CGCTCGAACCGGCGCATTAGCGTCGGTCCGTTGTAGCGTCCCCTTATTTAAAACCCCGATTTCA TCTGATTCATGT 20 TCACGGTCCGCGACGTGAATCGGGCGTTCCAGTCGGCGTTCGGCTACGACGCCGACGACGTGGT CGGAAGCGACCTCCTCGGGCGAATCGTGCCCCCGGTGCCGGACCCGGACCCGGTGCCGGAACCG GGGGACGACGAG 21 GCGTCCGCGAGTTCATCCTGAACGTCGTCCCGCTGTCGCCCGGCGAGGAGCGCGGGGCGGGCTA CGCCATCTACACCGACATCACGGAGCGGAAGACCCGCGAAAGCGAGCTAGAGCGACAGAACGAG CGATTGGAGGAG 22 GCGAGACCGGCGACGAGGTGCGCTTCGACACCGCCGAGCGGGCGCTCGAACAGATGGAGGAACT CATCGACGACCTGCTGTCGCTCGCCCGTCGCGGCCAACTGGTCGACGAGACGGAGCGCGTCGAC CTCGGGGCGGTC 23 ACGAACTCGTCGGTGAACATCTCGTCTTCCGGGGAGCCCGCCGCTCATGGCCTGCCCCCGCCGT AAGCTGCTGCATAAACCCGCTCCAAAATATACGGATCATTCACCCCTTGGAATCGCTCAATCAG ATCAATGTACAC 24 TGCGTACATTCCCCCTAAGCGGCTCCCAATATACAGACGCCGGTTAACGACAGCTGGCGACCCT GTGATCTCAGTACCGGTGTCGAATGACCACATCAGCTTGCCTGTCCGTGCATGGAGTTCGTATA CGTACCCGTCGT 25 AGATAGATGAGCCGATCAGAGATCGCTGGTGAGTTGGTAATTGTCCCGACATAGACACGCCAAC GTTCTGTTCCATCTGCTGCGTCGTAGGTCGCGAGATACGGCCAGCCACCAACATACACAATCCC ATCGACGAGGAC 26 ATACACCACCCCATCAGCAACAACTGAATCATGATTAAGTATCGCACCAGCATCGTAGCGCCAG CGTTCACTGCCAGTGGTGCTATCGAATGCATAGAAGATATGCTCCTAATCGCCAATATCAGTAC TTCACAAAGCCG 27 TCGACGAGGAGAGGGGCGAGTACATCTGCACGCTTACGGGAGAGGTAGTTGAGGAGACGGTTAT AGATACAGGGCCCGAATGGAGGGCTTACACACCTGAGGAGAGGACCCGCAGAAGCCGCGTGGGC AGCCCGCTTACC 28 AGTCGATGGCTGCGGCAGCTGTCTATGCTGCCTGCCGTATACGCGGCATACCCAGGAGTATAGA CGACATAGCGGAGGTCGTGAAGGGTGGCCGTAAGGAGGTTGCCCGCTGCTACCGCCTCATAGTC CGCGAGCTGAAG 29 GTGGAGTCTTTTGTCACACCGCAGAGGCGTAGCGCTGCAGAGCAGGAGCCCAAGCCTACTGCCA ACATAGAGAACATAGTGGCTACAGTATCCCTCGACCAGACTCTAGACCTGAACCTCATAGAGAG GAGCATACTGAC 30 CGTCGCCTGGGTTAAGAGGATGTTCGGCCTCTCCAAGGCGGGTCACGGAGGCACGCTGGACCCG AAGGTCACCGGCGTCCTCCCCGTAGCCCTGGAGGAAGCAACCAAGGTCATAGGCCTGGTGGTGC ACACGAGCAAGG 31 CGTGGGCGAGATCTACCAGAGGCCGCCGCTCCGCAGCAGTGTTAAGAGAAGCCTCCGCGTCAAG AGGATATACGAGATAGAGCTGCTGGAGTACAACGGCAGGTACGCGCTCATGAGGGTGCTCTGCG AGGCCGGCACAT 32 CGCTGGAAGAACGAGGGCAAGGAGGACCTGCTGCGGAGCTACATCAAGCCCGTCGAGTACGCCG TGAGCCACCTGCCCAAGATAGTTATACGCGATACCGCGGTGGACGCCATAGCCCATGGCGCGAA CCTCGCGGTGCC 33 GGGAGACCCCAAGGTGACCGGCGTCCTACCAGTGGGGCTCGCCAACAGCACCAAGGTCATTGGT AATGTTATACATAGTGTTAAAGAATACGTGATGGTTATACAGCTCCACGGCGATGTAGCCGAGC AGGATTTAAGAA 34 TAGAGGGAAAGACTGTAGCTTTCATTCCTAGGCACGGAAAGAGACACAGAATACCTCCACATAA GATAAATTATAGAGCTAATATATGGGCATTAAAAGAACTAGGAGTGAAATGGGTCATCTCAGTT TCTGCCGTAGGA 35 TGAGGGAGCTCAGGAGGACTCGCACGGGGCCCTACAGGGAGGATGAGACACTTGTAAGGCTCCA GGACGTCAGCGAGGCCCTGCTCCTGTGGAGGAGCAACGGGGATGAGAGGTATCTTAGACGCATC GTGCTACCCGTT 36 GAAACATCTATCGCCCACCTCCCGAAGATAATGATCTTGGATACAGCTGTCGACGCCATAGCAC ATGGTGCCAACCTGGCTGCCCCAGGCGTCGCCAGGTTAACCAGGAACATCGCGAAGGGTAGTAC CGTAGCGATCCT 37 TCGCTATCCCCGTGTACAGCATGGTGGGGGTGCCGATGCCCGGGTAGAACTTGGTGACGCTCTC CAGCTTCTCGAGGACGGTTTCCTTGGGGAGGCTCGCGGTGTCCACGAGGGTTATCGCGTCCTCG GCGCCGTCGCCG 38 CGAGGACGCGAAGAGCGCGGTGGATGTGGACGCGCCGCCGCACACGTAGCCGTCGAGGTAGCGC GGAACCATCGGCGACATCAGCCCCACGACGCGACCCGAGGCGTTGCCGAGGATCACGTCGAGCG TCACGCGCGGCA 39 CTCGACACCGTGCCGTTGCCCTCCTCTAAGTAGTCGGAAAGCCTCATCCGCGACTCCAGCTTCG CCACCGGCTCCTCGAGCAGGAGGAGGACGCGGTTGATGCGGTAGGACGCACTGCCCGCCTCCAG CACCGCGCCGTC 40 TCTATGGTGTAGAACGGGTCGTTGCGGAGCCAGCCTGGCGGCACGTACCGGTCGTCCGCTATCG CCAGCGATCTCTCGAAGAGGTCGAGGTAGGCGGACGCGTTGGCGAACGCCCCGTGTATCACGAC GTCTATCCCGCC 41 GTATAGGTTTCAGGTATTGATAATGCATAGGAGGTTTTTAAAACCTTGAGCCGCATAGTCTTCT GGATGGGCGAGAGACATGGTTAAGTATAAGTGCGGCAGGTGCGGATACGTCTTCGACGACGAGG AGATGAAGAGGA 42 CCTACGCCGGGTGCGTAGGAGGGCTCGAGTACATCCATGTCTATACTGATGTATGTTTTACCCA GGTCGCCTAGTGCCAGGGGTCCCTTTAACGCTTCCAGGATAGAGTACACGGTGACGTCTCTAGT CTTCTTCAAGAA 43 CTACTAGCGTGTCAACGGAGCTCTTCAACGCCTTTACTATTGGATAGGTTATAAGGTGCTCGCC TCCGAGGAATCCCAGGAGCATGCCGGGATACTCGTCTACAACGCCTTTCACCACGTCACCTATG ATTCTTAAAGAG 44 CATAGGTGACATGGGGTTTCCCATTGACTCTATAAAGCCGTATCCTTTAAGCGGAGTGCAATTG GTCTACGCTTTGCTTAACAACAGGTATTTCCTACCGGGTAGAGAGGGCTCGCTCATAGCTTTAG GTAGCGTGACGG 45 GGTATCTCACCGCTTGTCACCATAGTATCCCTCAGGTACTCCAGTATTCTTGAGAGAAACGCAC CTAAGCCGGATCTCAGGTTTGAATCCATAAGAACTATGAGTGAAGCGGGATTGAAGCCCCTGCT GTTTCTAAGACC 46 TAAGGGAGATAGAGAAACGCATCAAAATACCCTTGGGGAAACTGCGTGCAGGGGTTCAATATGG AGTAGAGGTCTCAGACATAAAGGAGAAGATAGCTGCTTACGCTAGGAGGAAGGGGCTTAAATAC TTCCCATCGGCA 47 TGTGAACCTCGTGCCCGGCTCTAAGTCGTGAGGGCTTGCAACATAGGTGGGGAGGAACCCGAGC AACGGGTAAGAAGACAGGATAAGCGGTATCGCTATGAAGAGGGCTGAGAAAAGGACATATACTC CTGAGCCCGTCC 48 CGAACATGCCTTCCCCGTCTATATAGACCCAGTAGAGTTTAAAAACTTAACCAGAGACGGCTTG TGAGCCGGATCTCTCCCCCGCTAGGCCCTGGATTGGGCTCGCTCCTCCTGGGACCCCGGCCTCC ACATGCTCGGGA 49 CCTGAAGGGCTCGGCTACCCTGAAGACGGGCTTCTGCGCGACCGCCGCGTACTCCGCCGTGGAG CGGTAGAAGAGCGAGGCTGTCTCCGTGAGCCTGACCATTCCGTACAGGGCGACTGCGACGAGCA CTATGACTGCGA 50 GTCAAGGTGCTGATGCCGAAGGCGACTTTCGACACCGACGATGCCGCCGACGCCCTGGCCATTG CCATCTGCCACGCGCATCACCGGCACAGTGTTGCCTATAGGATGGCGCTGGCCGGATAAGTTTG TTCTTGACCTGT 51 TCTCGGTTCGGCAATAAGTAATACCAACGAGGTATTACCATGCGCGTGACCAGCAAAGGCCAAG TGACGATCCCAAAGGAGATACGGGATCATTTGGGGATTGGGCCGGGCTCCGAGGTGGAGTTCGT GCCCACAGACGA 52 CTCGATCATATGGCCGGCACGTTGGACTTGGGAGGCATGACAACGGACGAGTATATGGAGTGGC TGAGGGGTCCACGTGAAGATCTCGACATTGATTGACACAAATGTCCTGATCGATGTTTGGGGTC CTGCCGGACAGG 53 CAGGTGTATTTTACACACCTGGACAGCCAGCATATGATGCTAGCACTCGGTGTCCCCTTATCAC GGTTTCCCGCATTGTAAAGTTTTCGCGCCTGCTGCGCCCCGTAGGGCCTGGATTCATGTCTCAG AATCCATCTCCG 54 CTGGAGCCTGTTAGTTGTTACAGGTTCACCGGTTGTCGGAGTATTCAGATCATTGAGCCAGCAG TTGATGGCTGCCTGTAGTTCACTGGTTGTGATGTAAGCTGCTCCATCGGAATCAACATCGTTCC ATGGGTTCCAGT 55 ACGGTCTTGCTTTCTCCTGAATCCATTTCACCTGTCCAGACCCATTCATAGCGGTTAGCTTCAC TGAGGTTCTGCTTGAAGACACCGTCATCATTGTTAGATGAGGTTATTGTCCAGCCGGCAGGAAT GACTTCTTCGAA 56 GTCAGCAGCTCTTCATAGAAGTTCTGGTTTGCAATATCCCTCTGGGCAATGACAGGGTAGTCGA CTTCGTTTGCAGTCAGGTGGACTGCATACAGGGACTTGCTGATGTCCGGGGTATATCCACTGTG AGGAGCATAGTA 57 ACCCGTCAGTCGTGACGTCCTCCGCTCCTCCTATGCTATCTCCACACACCCACTCACGTTCTTG CTTCTTTACTACACCCTCTTTATTCAGCTCTTCGAGAACATTATTAATGTGACCCTTAGAGATA TATTCATTATAC 58 GTGCCTCCTCAAGCGACTGCTTAAACCCAATTACATCTGATTTATCCTTTATTTTAGGGCCTAT AGAATCTATGAATAATTCGGCGATTCTTATTATTTCTAAAACCAATTCGTCTGTTTTGAGTGGT GTGCCTTCTTCA 59 CATCCCATGCATTTTCATAATAATCGGAATTCAAATCCTCTATATTGAATTTTATCTTAACATT TGACATAATCATTTTCTCCTTACAGAAGAGATCCAGCTAAGCTTACTCATAAATGGTAGTACCA TGCCAATATTGG 60 CGTAGCCCGCACCTTCCTCTGGTTTAGCACCAGCGGTCCCCACAGAGTACCCATCATCCCGAAG GATATGCTGGCAACAGTGGGCACGGGTCTCGCTCGTTGCCTGACTTAACAGGATGCTTCACAGT ACGAACTGACGA 61 CCTGATAGGCCGCAGATTCATCCTAAGGCGCCGGAGCTTTTGACCACAGAACATTCCAGTATCT ATGGTATATCTGGAATTATCACCAGTTTCCCGGTGTTATGCCAGACCTTAGGGCAGATTATCCA CGTGTTACTGAG 62 TGTTTGGCTTGATACTAATAAAAGCACAGCTAAAATGAAAATAAGCCGATATTTGTGATTCATG CAACTCACCCTTTTCTACATAAACAAAATACTAACCCGAAAACCGAAATTGAAATTAATGCAGA GAAACCAGGTGA 63 TTAACGGCACCAACAGTTATTATATTTTTAGCAGTCCCGGGTGAAGTAATTATGGAATAGTTGT TAGAATTACTGTTCTTATTACCAGCTGATTTGAAAGCAATTATACCTGCATCACGAATTGCAGC ATCATAATATTC 64 GGCTCAGACGACTGAAAAAGCAACGATTGGAATAATAGGGGGTTCTGGGCTCTATGATCCTGGT ATTTTGACTAACAGCAGAGAAATAAAAGTATATACACCCTATGGGGAACCTAGCGATTTGATAA CGATAGGTAACA 65 CGCAGAACAGGTTCCTTCTATTGGATATTCATCTTCGGCTGCAGTTGCAGGAAGAGTAAGGATA TATACTACGGTCTTGCTTTCTCCTGAATCCATTTCACCTGTCCAGACCCATTCATAGCGGTTAG CTTCACTGAGGT 66 CTTCCTCCACGCATTTGTTGTGGTGCTGATGGCGTATTCTCTGGAATTTGGGATGATTCTGGAA ATCCATCCTCAGACACTTCAGATATTTTAGTCTTACTTCCAGCGTTTAATTGAACCTTACCTTT AAAAGCAGTAGT 67 GAAACTTACCTTATCAGTGTCATTAAGCATATTGCTTCCAAGACCCATTGAAGCACTTACATCG TTGATACACAGGTGCCAGGAATAGTATTCCTCAGTCTCACTATAATCCTCGTTGGTGTAGCCTT CAAGAGAGTCAA 68 GTTTAAGCAATTCTTCGGATGAAAGATGGCGCTCTATAGGAATTTGTTCTGGTCTAGCCATAAG GCATTATTTGTACTTAATTAGTAATAAATGTTTAGTTAATGACTATAAATCTGCAATTGGAGTC TCAAATTTTCAA 69 AACATGAAGGATGTGTGTAAGAGGAAACGTTATTAACAGACGTAATCAGGAGGATAGTTATGCC CTAAAAACAGCAGAGTTAAGGTTTAAAAATAAGATAAGAACTCAGTTGAGGTTTATCCATTAAT CCCATTAATCCT 70 ACTTTCTAAAAGCGCTTGGAGCACGTATCAGGTCAAGTCTTTCAACCTTAAATGCTGCCAGTGC CGTAAGTAGTGCAGTTATGTTGCTTATTGAAACAAACAACTTAGCCCACTTATTACCTCTTGTC AGTGTTTTTGAT 71 GTATCCGCTGATATATCCTGGGGATATAGATCGCTCTGAAATGGTTACATCTATCGGTTTTAAG GACAGTTCCAACACTATTGGACCTTGCAGCTATGACAGGAATAATCTGTTTATCGAGCACAGTT GAATTTGACCTA 72 TCAATACCTAATTCTTTCCTTAGAGTGCTATTTTGATTGAATTCCCTCAGGAAAGATTCAAAAT TTAAGTAGCCGAGCTTACATCTTGAAATTTCCATCTTTATTATGTTGCTCAGGCTTAATGCTTC TAAGTATGGGTT 73 AGATATCCTTTGAAATTCTCGTAATTGCTGAAGGCCACTACTTCATCAGGTCTGATGCAATCTT TAATCTGAACATTGCTTTCTGAGGTCTTAGGAATAATCCTGTAAGGGAGTCGGATATTGTTCGT TAAGATGCTCTT 74 ATAGAGGGACCTAGATTTTCAACGAGGGCAGAAAGTAGAATTTGGAGGGAAGTTTATAAAGCCG ATATCATAGGGATGACTTTAGTTCCAGAAGTAAATTTAGCTTGCGAAATGCAAATGTGCTATGC AACAATTGCGAT 75 GTCTTCAGCATAGTACCAGCTTATGTTGTCACCATCGTTCAGTACGTTACCACCAAGTCCACTG CCTGCAGCTACATCATTAATGTACAGGAACCAGGCATAGTAACCGCCAGTTGATATGTAGTCTT CACCTTCGATTC 76 ACTCTCCATCATGACAGCCAGATCGGTCATAGCATCGATTGTGTACTCTTCGTCGGGATTGTTG TATGGAATGAACTTATAGTTCTCACCTGCTACCTGATCCACTGTCATTTCTGCAAGAGTCTGCA CTGTGGTAATTC 77 ATATTCCGTATTTCTTATCAAACCGATCGTGAAGATTTGACAAAGGCTTAACTTTAGGGCTCCA CTTCTCATTATTAGCCTTAGAATATAAAGCGTAACCGTAAGCCTGAGGAACGTAAAGCTTAGGA GATTCAATCCCG 78 TAAAATTAGCCGAAGGCTTCCCATTACCGAAAAAGTCGTTTATTAGCTCTTCATCCTTCTTCTC CACGTCCGCCCATTCCTCTCCTTCCCTTGGAATTTTAAGCTCGTCCCAGCTGACTCTTATGGGC AATTCAATATCC 79 GTATAAACTTTTGATATAACCTTGCCTAATTTGATATCATAGCTTATGTTTGGCGCTATCCCCC ACTTGTAGAGGGTCGCGTTATATTCTCTAATAGCAAGAGAGATACAAGATTCGTTAACGTTATT TATATCACTCTC 80 TCCGGAGGAATCTATCATATTAAACCTCCTCAAAATCGCCTCCTCTTGATTGCTTAAAGGCTGT GAATTACAAAGCTTATTTAATGCGTCCCAAAGCGTTAAGTAATAATTATTTATATTAAACACTA CTATTTCAGTAG 81 GTTCCTCCTCAATTCAATTGGACTGAAGGAGGGTACGTTCTGGAAAACAGAGCGTAAAAGAGAT ATAGAACGTAGTATACACATAGCTGGAAAAAGAACAATCATTAAGACAATAAAGAACTTTATGG AAAAGAGTAGAA 82 TCGTGTAAAGGTTGTATAATTCAAGCCTCAGAACATTTCGAACTCCTTACAAAATCGTTTAAAC TTTCTAAGGCATAAATTTACTAGAAATTGTCATTTATGAGAATGTAACTATATAGATGGTAAAA TTATTAATCCTC 83 GGCTGAAAAATAGGTTCGATCCGCCTCCTCACTTCTTCTCCTTCTTGCCCTCGGCCTCGGAGGA GGCCTCTATTCCCAGCTTCTTGGCCTCCTCCTCGGTCGTCATGAACAGGCTAGTCCTCTGCCTT CCGCCCATGCTC 84 GACCTAGCCTTACGCACAGCCCTCTCCACAACCTCCTCAAGCTTATCCCAGTCAATAGAGCTCA TTACAAGTTAACCACGCCCACCTTTAATATAAACCTTTACCCCTCGTGGCAATTAACTTTAACC GCTACTCCGGTG 85 TGGCCCTTAGACCTCTGCCCATGCTTAGGCGCTTACCCACACCTATTAGTACGGCGCCAATGCC CACGGCCATGAAGTACATTAAGGCACCCATGGTTGCACCGTAGAGTGCCGTGAATGTTCCGTAG AATACACCGGCC 86 TCGGCGAATCTGTCGAGCTCCATGACGTCCACAGAGCCGCCGAACTTGGCCGAGAATCTATCGG CCTGGGCGGTGCGCCTCCCTATCAGCAAAACCCTGGGCGCCGTCAGTAGCGCGACGGCCCTGGC GATTCCCCTGGC 87 TCCAGGTAGGATCTGGCCGAGAGGGAGGACGCCGCGCTGTTGTGCTCCGGGAACCCTAGAGTCA CGACCGCCTTGACGCCTATACGTTCGGCGTATTCAGCGACGGCGGCGCCGGTGCCGCCCGTCAG CGTGACGGCAAG 88 GCAAGAGAATACATTTTTGATGATAAGAGAAGCTTGTGGCATACTTTCTTAGGCTTTATTTCAG CATTCACTTTAGCGTATTCTATCGTTATTTTGCTATTGTTCACATTGTATCAAGTGAGAGAAAG AGAGAAGCCAAC 89 AGAATCAAAGGAGTGGTGTAAAGATGGAGAGAAAAAAAGGTTGGCATCCTATTTATGTGAGTGA AGCGGTTTTAAGTAAGTTAGATAAAGAGAGAGAAGAAATTAAAGAAGAATTAGGTATTCCAAAG GAAGAGAATTTG 90 GTTCAGCATAAAAGACGGTTTCACGGGCCAAAGCCTAAGCGGCGTAACGGTGAAAGAAGGAGAT ACGGTTTTGGGCACGATTGACGACGGCGGGACGCTGGAGCTCACGAGGGGCACTCACACCTTGA CTTTCGAGAAGC 91 CTGATGTTATAGAAGTCCGCAAGGACGGCTCTGTCATCTCGCCCGAGGGTGGGAAATACTATCT CGGCGACATAAGCGGCCCGACACAAATTAGCATCAAGTTCAAGGCCGGCGCGGTGGGAACCCAC GGCTTCACTATC 92 TCTCCCTCAACCTTCGCGGGGAGAACGGCGCGGAGTACTGGACGGGCTACGCGGACGCGCTGGA AGACCTGTTGAAGAAAATCCAGAGGCGGGAGGTGAGGGCATGAGAAGGTATTGTTACATCACGT GGGGATGGATCA 93 GAGCGCCGGGAGGTGAGGGCATGAGTGAGGAATTGATGTTTGGTCGTGTCGTGGAGTATGTTCA GCATAGTTTCTACAAGAAACCGTTTCCTCTTGGCAGTGAGCTCAAGAATGCAGTAGAGAAGGTT ATGGAAACAGGA 94 AGGTCAGAGCCCACGTGGCAACTTTTGAGGTTCTGACAAAAGACTATGTTCGTGAGAAATACAA AGACATCATAGAGTTCATGAGGGAGAAAGGGACAGTATCGAGAAAGGAACTGCGGAAGAAGTTC TTCTTGCTTGCT 95 GTACCTCAAAATACAGAATCATATTTTACAATCGCTTGGAAATATTAATATCAACAATACGCAA GTCCAAATTAACGTCCCTGGCAAACAGGTGACAATTTATACCCACGAAATACTAGATAACGCCA AAAAGGCACTCG 96 CTTTGTATACTTAGATCAGGAAATGGAGCTAAAAGGCACTATCAAGAAGACAAAAGATTCCTGG AGAGAAACATTTAAAGAGTACTCCAAGACAGACAGCGAATATCTAATAAATTACAGACTGTTTT CAATACTCCCTC Primer and Core Sequence 97 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCAATTGCTCCCTCGTATCCCTTGTACATTATCTCA GCTCCGCTTAATGATATTAATTTTACCTTGAGTGTTTTTGCTAAAGCCTTTGCCATCATCGTTT TACCTACTCCAGGTGGCCCGTAAAGCAACACAGCTTTGGCACACATCATGTAGTAGACGACCAA GACAGT 98 ACAGTTCTCCTTCTTAGCTTCGTGAGAACTTCTCCAAAACCTACCCAGTTCTCCGAGGAACCTC TTAGCATCTGTTAAATCGTTATTAGTATTAGCTTCCACCATCTCAAGTTCCTTTAAGGCGTTAC TCACACTCTTCTTACCTATCTTTTAGAGAACCACTCGTCAGCACATCATGTAGTAGACGACCAA GACAGT 99 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGTTATCAAAGCCCTTAAAGAGTGGTAGGGGCAAAA GTCTGAAGCGTCCTTACTTAACTGGAGTATCTGAGATGGCCTTAATCCGCTTAGGTCTTTAATT TTATCCCTTAATGAACATTCCCTGCACTCTATGTCTTCGGGCACATCATGTAGTAGACGACCAA GACAGT 100 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGAGATGTAGCAGACGGGCTAAGAGTTTCAAACCCT CTAAGGATCACTACAAACAAGAGAGAGAGACAATCCTCTCTTTTGTCTTGTCATTGTGTTTCAA ACCCTCTAAGGATCACTACAAACATCTTTAACATAGATACCCACATCATGTAGTAGACGACCAA GACAGT 101 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGACCGGACGTTGTGATCACGGGTACCTTGATCTGG TACTCAAAGGTTTGCCCCCGTGAAGTCTGGTACATGGCTAGACACGTCACTCCATTCGAGGGAC ATTCGAAGTTAGAGAAGGGCAGAGCGATACATCAGATATATCACATCATGTAGTAGACGACCAA GACAGT 102 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGTCTTTTCTCTACTAATTCTCCTCACGAGATCTCT AAACATTCTTGCTGAAAGAGGATCCAAACCTAATGTAGGTTCGTCAAGCAATAAAATTGGAGGA TCAGTTATTAATGCTCTTGCTAAGGCTAGTTTCCTCTGCATCACATCATGTAGTAGACGACCAA GACAGT 103 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGATTTTGCCATCATTAAAAACAACAATTTGATCAC CCATAGTCATAGCTTCTAATTGATCGTGAGTTACATAAATACTTGTGGTGTTTAACATACGGTG AATATTTACAATTTCTCTTCGCATGTTTTCTCTTAGTTTAGCACATCATGTAGTAGACGACCAA GACAGT 104 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGTATCTTTCAATTCTCGAAAGAAAAGGTTACAAGT CTCATAGATTTATTCCTCTTCACTGTTGTACGTTGGCAGCTAGAGAGAGTTTAGATTATGAGAA AATTAAGAGAATATATGAGGATTCGTTTTCTTGGTTTAAGTCACATCATGTAGTAGACGACCAA GACAGT 105 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCTAATTGATTTTCCTGTACCATGTGGTAAAACAAC GCTACCTCTTAATTGTTGATCTGCTTTTCTAGTATCAAGATTTAATCTAAAAGCTAAATCAACT GAAGCATCAAATTTTGTATAAGAAGTTTTTTTCACTAATTCCACATCATGTAGTAGACGACCAA GACAGT 106 ACAGTTCTCCTTCTTAGCTTCGTGAGAACTCGGTTTTCCCGTGAACTAATAAACACCTACTGGA GCCAAGAACGGGTCAGAATTGATGGAATAAACGTTGCGGAGAATGAAATTAATTTGTACATCAG AGACATTGATGACAACGGTGACCCTATACAGTCAACTATACCACATCATGTAGTAGACGACCAA GACAGT 107 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCTTAATGGAAAGTATGCTTTAGATACCTTCTGGAA CGCTATCTCACTTGGCGGGAATTCAGATATGGAGAGTAAATTAAGGGATCTGGAAGTAAAGTTA ATGTCGTTAATCTATTTAAATGAGTCACCATTAAAATCACCCACATCATGTAGTAGACGACCAA GACAGT 108 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCATAATATGTTAGAGGTAGAATTTCTTTGTGATAG AATATTATTGATGAATGATGGAAGAGAATTAGCATTAGGAAAACCTAAGGAACTGGTAAAGGAT ACAGAATCTAAGAATCTTGAAGAGGTTTTCCTTAAACTTGTCACATCATGTAGTAGACGACCAA GACAGT 109 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCCTTACTTCATCTCTCAAGATAAGGGTAATAAGTT CACTTCAAATATCTGGTCTTATCGCAAGTTGATTGAGGCTATAGTGTATAAGCTCTATGAGTAT GGTATAAACGTGTTCCTCGTTGTAGAGTATAACACTTCACGCACATCATGTAGTAGACGACCAA GACAGT 110 ACAGTTCTCCTTCTTAGCTTCGTGAGAACAGTCTAGGTTTTAATTCTTCAACTGCTTCAAATAC TAGCTTACTGTAGTTATCTGCCCTCATGTTAGGATATATATCTGGAATATAAGGAGGTTGATGA GTTATAAGAAGTGGATGAAATTGTTGTCACACACTCCCCTACACATCATGTAGTAGACGACCAA GACAGT 111 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCTACCTCTTCGGCCTTGTACCAACGTACCCCTGAT ACAAGTTCCAAGCAGAGATGGAAAACTCGAAGATGGTATCACCCAAGATGAGATACGATATCAA TGAAGGCGAGCCTAGGTACAAGTAAAGGGATACCACGAGAGCACATCATGTAGTAGACGACCAA GACAGT 112 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCTCGTAAGCGTTTCCTACCCTCGAGAGGGCCATCC TGGTGGTGAGGAAGTCGTCGAAGTGGGCTAAGTAAAAAGCGAAGATCTCGACCCACAATTACCT CCTCCTGTACACCAGGAATACCCCTATCAGGATAGAGATACCACATCATGTAGTAGACGACCAA GACAGT 113 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGCGCGTCCGGGTCGCGGCCGGGGACGACCGTCTTG ACGAAGTCGGTCGACCCCTCGTCGGTCGAGATGGTCGTCACCTCGGTGTCGAGGCCGTACGTTT CGAGCGCGTCGCGTACCAGTTCGCCGTCCGCGTCGGGACGGCACATCATGTAGTAGACGACCAA GACAGT 114 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCATGTACTCGTTCCAGAAGGTGAGTTCGCTCCCCT CGATTTCGACCTCGCCCACGTCGAAGCCGCCGGTCGTTTCGAGCGCGAACGACTCGACGGGACC GACGAGCGAAACTTCGCCGCCGAGCACGTCGGCGACGCGTTCACATCATGTAGTAGACGACCAA GACAGT 115 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCTCGATGCGCTCGGGCTTGTAGGACTCCCCGAGGG CGTCCTTGTTGGTGAAGACGTTTTGTTTTCGCTCGAACCGGCGCATTAGCGTCGGTCCGTTGTA GCGTCCCCTTATTTAAAACCCCGATTTCATCTGATTCATGTCACATCATGTAGTAGACGACCAA GACAGT 116 ACAGTTCTCCTTCTTAGCTTCGTGAGAACTCACGGTCCGCGACGTGAATCGGGCGTTCCAGTCG GCGTTCGGCTACGACGCCGACGACGTGGTCGGAAGCGACCTCCTCGGGCGAATCGTGCCCCCGG TGCCGGACCCGGACCCGGTGCCGGAACCGGGGGACGACGAGCACATCATGTAGTAGACGACCAA GACAGT 117 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGCGTCCGCGAGTTCATCCTGAACGTCGTCCCGCTG TCGCCCGGCGAGGAGCGCGGGGCGGGCTACGCCATCTACACCGACATCACGGAGCGGAAGACCC GCGAAAGCGAGCTAGAGCGACAGAACGAGCGATTGGAGGAGCACATCATGTAGTAGACGACCAA GACAGT 118 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGCGAGACCGGCGACGAGGTGCGCTTCGACACCGCC GAGCGGGCGCTCGAACAGATGGAGGAACTCATCGACGACCTGCTGTCGCTCGCCCGTCGCGGCC AACTGGTCGACGAGACGGAGCGCGTCGACCTCGGGGCGGTCCACATCATGTAGTAGACGACCAA GACAGT 119 ACAGTTCTCCTTCTTAGCTTCGTGAGAACACGAACTCGTCGGTGAACATCTCGTCTTCCGGGGA GCCCGCCGCTCATGGCCTGCCCCCGCCGTAAGCTGCTGCATAAACCCGCTCCAAAATATACGGA TCATTCACCCCTTGGAATCGCTCAATCAGATCAATGTACACCACATCATGTAGTAGACGACCAA GACAGT 120 ACAGTTCTCCTTCTTAGCTTCGTGAGAACTGCGTACATTCCCCCTAAGCGGCTCCCAATATACA GACGCCGGTTAACGACAGCTGGCGACCCTGTGATCTCAGTACCGGTGTCGAATGACCACATCAG CTTGCCTGTCCGTGCATGGAGTTCGTATACGTACCCGTCGTCACATCATGTAGTAGACGACCAA GACAGT 121 ACAGTTCTCCTTCTTAGCTTCGTGAGAACAGATAGATGAGCCGATCAGAGATCGCTGGTGAGTT GGTAATTGTCCCGACATAGACACGCCAACGTTCTGTTCCATCTGCTGCGTCGTAGGTCGCGAGA TACGGCCAGCCACCAACATACACAATCCCATCGACGAGGACCACATCATGTAGTAGACGACCAA GACAGT 122 ACAGTTCTCCTTCTTAGCTTCGTGAGAACATACACCACCCCATCAGCAACAACTGAATCATGAT TAAGTATCGCACCAGCATCGTAGCGCCAGCGTTCACTGCCAGTGGTGCTATCGAATGCATAGAA GATATGCTCCTAATCGCCAATATCAGTACTTCACAAAGCCGCACATCATGTAGTAGACGACCAA GACAGT 123 ACAGTTCTCCTTCTTAGCTTCGTGAGAACTCGACGAGGAGAGGGGCGAGTACATCTGCACGCTT ACGGGAGAGGTAGTTGAGGAGACGGTTATAGATACAGGGCCCGAATGGAGGGCTTACACACCTG AGGAGAGGACCCGCAGAAGCCGCGTGGGCAGCCCGCTTACCCACATCATGTAGTAGACGACCAA GACAGT 124 ACAGTTCTCCTTCTTAGCTTCGTGAGAACAGTCGATGGCTGCGGCAGCTGTCTATGCTGCCTGC CGTATACGCGGCATACCCAGGAGTATAGACGACATAGCGGAGGTCGTGAAGGGTGGCCGTAAGG AGGTTGCCCGCTGCTACCGCCTCATAGTCCGCGAGCTGAAGCACATCATGTAGTAGACGACCAA GACAGT 125 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGTGGAGTCTTTTGTCACACCGCAGAGGCGTAGCGC TGCAGAGCAGGAGCCCAAGCCTACTGCCAACATAGAGAACATAGTGGCTACAGTATCCCTCGAC CAGACTCTAGACCTGAACCTCATAGAGAGGAGCATACTGACCACATCATGTAGTAGACGACCAA GACAGT 126 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCGTCGCCTGGGTTAAGAGGATGTTCGGCCTCTCCA AGGCGGGTCACGGAGGCACGCTGGACCCGAAGGTCACCGGCGTCCTCCCCGTAGCCCTGGAGGA AGCAACCAAGGTCATAGGCCTGGTGGTGCACACGAGCAAGGCACATCATGTAGTAGACGACCAA GACAGT 127 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCGTGGGCGAGATCTACCAGAGGCCGCCGCTCCGCA GCAGTGTTAAGAGAAGCCTCCGCGTCAAGAGGATATACGAGATAGAGCTGCTGGAGTACAACGG CAGGTACGCGCTCATGAGGGTGCTCTGCGAGGCCGGCACATCACATCATGTAGTAGACGACCAA GACAGT 128 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCGCTGGAAGAACGAGGGCAAGGAGGACCTGCTGCG GAGCTACATCAAGCCCGTCGAGTACGCCGTGAGCCACCTGCCCAAGATAGTTATACGCGATACC GCGGTGGACGCCATAGCCCATGGCGCGAACCTCGCGGTGCCCACATCATGTAGTAGACGACCAA GACAGT 129 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGGGAGACCCCAAGGTGACCGGCGTCCTACCAGTGG GGCTCGCCAACAGCACCAAGGTCATTGGTAATGTTATACATAGTGTTAAAGAATACGTGATGGT TATACAGCTCCACGGCGATGTAGCCGAGCAGGATTTAAGAACACATCATGTAGTAGACGACCAA GACAGT 130 ACAGTTCTCCTTCTTAGCTTCGTGAGAACTAGAGGGAAAGACTGTAGCTTTCATTCCTAGGCAC GGAAAGAGACACAGAATACCTCCACATAAGATAAATTATAGAGCTAATATATGGGCATTAAAAG AACTAGGAGTGAAATGGGTCATCTCAGTTTCTGCCGTAGGACACATCATGTAGTAGACGACCAA GACAGT 131 ACAGTTCTCCTTCTTAGCTTCGTGAGAACTGAGGGAGCTCAGGAGGACTCGCACGGGGCCCTAC AGGGAGGATGAGACACTTGTAAGGCTCCAGGACGTCAGCGAGGCCCTGCTCCTGTGGAGGAGCA ACGGGGATGAGAGGTATCTTAGACGCATCGTGCTACCCGTTCACATCATGTAGTAGACGACCAA GACAGT 132 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGAAACATCTATCGCCCACCTCCCGAAGATAATGAT CTTGGATACAGCTGTCGACGCCATAGCACATGGTGCCAACCTGGCTGCCCCAGGCGTCGCCAGG TTAACCAGGAACATCGCGAAGGGTAGTACCGTAGCGATCCTCACATCATGTAGTAGACGACCAA GACAGT 133 ACAGTTCTCCTTCTTAGCTTCGTGAGAACTCGCTATCCCCGTGTACAGCATGGTGGGGGTGCCG ATGCCCGGGTAGAACTTGGTGACGCTCTCCAGCTTCTCGAGGACGGTTTCCTTGGGGAGGCTCG CGGTGTCCACGAGGGTTATCGCGTCCTCGGCGCCGTCGCCGCACATCATGTAGTAGACGACCAA GACAGT 134 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCGAGGACGCGAAGAGCGCGGTGGATGTGGACGCGC CGCCGCACACGTAGCCGTCGAGGTAGCGCGGAACCATCGGCGACATCAGCCCCACGACGCGACC CGAGGCGTTGCCGAGGATCACGTCGAGCGTCACGCGCGGCACACATCATGTAGTAGACGACCAA GACAGT 135 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCTCGACACCGTGCCGTTGCCCTCCTCTAAGTAGTC GGAAAGCCTCATCCGCGACTCCAGCTTCGCCACCGGCTCCTCGAGCAGGAGGAGGACGCGGTTG ATGCGGTAGGACGCACTGCCCGCCTCCAGCACCGCGCCGTCCACATCATGTAGTAGACGACCAA GACAGT 136 ACAGTTCTCCTTCTTAGCTTCGTGAGAACTCTATGGTGTAGAACGGGTCGTTGCGGAGCCAGCC TGGCGGCACGTACCGGTCGTCCGCTATCGCCAGCGATCTCTCGAAGAGGTCGAGGTAGGCGGAC GCGTTGGCGAACGCCCCGTGTATCACGACGTCTATCCCGCCCACATCATGTAGTAGACGACCAA GACAGT 137 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGTATAGGTTTCAGGTATTGATAATGCATAGGAGGT TTTTAAAACCTTGAGCCGCATAGTCTTCTGGATGGGCGAGAGACATGGTTAAGTATAAGTGCGG CAGGTGCGGATACGTCTTCGACGACGAGGAGATGAAGAGGACACATCATGTAGTAGACGACCAA GACAGT 138 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCCTACGCCGGGTGCGTAGGAGGGCTCGAGTACATC CATGTCTATACTGATGTATGTTTTACCCAGGTCGCCTAGTGCCAGGGGTCCCTTTAACGCTTCC AGGATAGAGTACACGGTGACGTCTCTAGTCTTCTTCAAGAACACATCATGTAGTAGACGACCAA GACAGT 139 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCTACTAGCGTGTCAACGGAGCTCTTCAACGCCTTT ACTATTGGATAGGTTATAAGGTGCTCGCCTCCGAGGAATCCCAGGAGCATGCCGGGATACTCGT CTACAACGCCTTTCACCACGTCACCTATGATTCTTAAAGAGCACATCATGTAGTAGACGACCAA GACAGT 140 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCATAGGTGACATGGGGTTTCCCATTGACTCTATAA AGCCGTATCCTTTAAGCGGAGTGCAATTGGTCTACGCTTTGCTTAACAACAGGTATTTCCTACC GGGTAGAGAGGGCTCGCTCATAGCTTTAGGTAGCGTGACGGCACATCATGTAGTAGACGACCAA GACAGT 141 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGGTATCTCACCGCTTGTCACCATAGTATCCCTCAG GTACTCCAGTATTCTTGAGAGAAACGCACCTAAGCCGGATCTCAGGTTTGAATCCATAAGAACT ATGAGTGAAGCGGGATTGAAGCCCCTGCTGTTTCTAAGACCCACATCATGTAGTAGACGACCAA GACAGT 142 ACAGTTCTCCTTCTTAGCTTCGTGAGAACTAAGGGAGATAGAGAAACGCATCAAAATACCCTTG GGGAAACTGCGTGCAGGGGTTCAATATGGAGTAGAGGTCTCAGACATAAAGGAGAAGATAGCTG CTTACGCTAGGAGGAAGGGGCTTAAATACTTCCCATCGGCACACATCATGTAGTAGACGACCAA GACAGT 143 ACAGTTCTCCTTCTTAGCTTCGTGAGAACTGTGAACCTCGTGCCCGGCTCTAAGTCGTGAGGGC TTGCAACATAGGTGGGGAGGAACCCGAGCAACGGGTAAGAAGACAGGATAAGCGGTATCGCTAT GAAGAGGGCTGAGAAAAGGACATATACTCCTGAGCCCGTCCCACATCATGTAGTAGACGACCAA GACAGT 144 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCGAACATGCCTTCCCCGTCTATATAGACCCAGTAG AGTTTAAAAACTTAACCAGAGACGGCTTGTGAGCCGGATCTCTCCCCCGCTAGGCCCTGGATTG GGCTCGCTCCTCCTGGGACCCCGGCCTCCACATGCTCGGGACACATCATGTAGTAGACGACCAA GACAGT 145 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCCTGAAGGGCTCGGCTACCCTGAAGACGGGCTTCT GCGCGACCGCCGCGTACTCCGCCGTGGAGCGGTAGAAGAGCGAGGCTGTCTCCGTGAGCCTGAC CATTCCGTACAGGGCGACTGCGACGAGCACTATGACTGCGACACATCATGTAGTAGACGACCAA GACAGT 146 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGTCAAGGTGCTGATGCCGAAGGCGACTTTCGACAC CGACGATGCCGCCGACGCCCTGGCCATTGCCATCTGCCACGCGCATCACCGGCACAGTGTTGCC TATAGGATGGCGCTGGCCGGATAAGTTTGTTCTTGACCTGTCACATCATGTAGTAGACGACCAA GACAGT 147 ACAGTTCTCCTTCTTAGCTTCGTGAGAACTCTCGGTTCGGCAATAAGTAATACCAACGAGGTAT TACCATGCGCGTGACCAGCAAAGGCCAAGTGACGATCCCAAAGGAGATACGGGATCATTTGGGG ATTGGGCCGGGCTCCGAGGTGGAGTTCGTGCCCACAGACGACACATCATGTAGTAGACGACCAA GACAGT 148 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCTCGATCATATGGCCGGCACGTTGGACTTGGGAGG CATGACAACGGACGAGTATATGGAGTGGCTGAGGGGTCCACGTGAAGATCTCGACATTGATTGA CACAAATGTCCTGATCGATGTTTGGGGTCCTGCCGGACAGGCACATCATGTAGTAGACGACCAA GACAGT 149 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCAGGTGTATTTTACACACCTGGACAGCCAGCATAT GATGCTAGCACTCGGTGTCCCCTTATCACGGTTTCCCGCATTGTAAAGTTTTCGCGCCTGCTGC GCCCCGTAGGGCCTGGATTCATGTCTCAGAATCCATCTCCGCACATCATGTAGTAGACGACCAA GACAGT 150 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCTGGAGCCTGTTAGTTGTTACAGGTTCACCGGTTG TCGGAGTATTCAGATCATTGAGCCAGCAGTTGATGGCTGCCTGTAGTTCACTGGTTGTGATGTA AGCTGCTCCATCGGAATCAACATCGTTCCATGGGTTCCAGTCACATCATGTAGTAGACGACCAA GACAGT 151 ACAGTTCTCCTTCTTAGCTTCGTGAGAACACGGTCTTGCTTTCTCCTGAATCCATTTCACCTGT CCAGACCCATTCATAGCGGTTAGCTTCACTGAGGTTCTGCTTGAAGACACCGTCATCATTGTTA GATGAGGTTATTGTCCAGCCGGCAGGAATGACTTCTTCGAACACATCATGTAGTAGACGACCAA GACAGT 152 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGTCAGCAGCTCTTCATAGAAGTTCTGGTTTGCAAT ATCCCTCTGGGCAATGACAGGGTAGTCGACTTCGTTTGCAGTCAGGTGGACTGCATACAGGGAC TTGCTGATGTCCGGGGTATATCCACTGTGAGGAGCATAGTACACATCATGTAGTAGACGACCAA GACAGT 153 ACAGTTCTCCTTCTTAGCTTCGTGAGAACACCCGTCAGTCGTGACGTCCTCCGCTCCTCCTATG CTATCTCCACACACCCACTCACGTTCTTGCTTCTTTACTACACCCTCTTTATTCAGCTCTTCGA GAACATTATTAATGTGACCCTTAGAGATATATTCATTATACCACATCATGTAGTAGACGACCAA GACAGT 154 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGTGCCTCCTCAAGCGACTGCTTAAACCCAATTACA TCTGATTTATCCTTTATTTTAGGGCCTATAGAATCTATGAATAATTCGGCGATTCTTATTATTT CTAAAACCAATTCGTCTGTTTTGAGTGGTGTGCCTTCTTCACACATCATGTAGTAGACGACCAA GACAGT 155 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCATCCCATGCATTTTCATAATAATCGGAATTCAAA TCCTCTATATTGAATTTTATCTTAACATTTGACATAATCATTTTCTCCTTACAGAAGAGATCCA GCTAAGCTTACTCATAAATGGTAGTACCATGCCAATATTGGCACATCATGTAGTAGACGACCAA GACAGT 156 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCGTAGCCCGCACCTTCCTCTGGTTTAGCACCAGCG GTCCCCACAGAGTACCCATCATCCCGAAGGATATGCTGGCAACAGTGGGCACGGGTCTCGCTCG TTGCCTGACTTAACAGGATGCTTCACAGTACGAACTGACGACACATCATGTAGTAGACGACCAA GACAGT 157 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCCTGATAGGCCGCAGATTCATCCTAAGGCGCCGGA GCTTTTGACCACAGAACATTCCAGTATCTATGGTATATCTGGAATTATCACCAGTTTCCCGGTG TTATGCCAGACCTTAGGGCAGATTATCCACGTGTTACTGAGCACATCATGTAGTAGACGACCAA GACAGT 158 ACAGTTCTCCTTCTTAGCTTCGTGAGAACTGTTTGGCTTGATACTAATAAAAGCACAGCTAAAA TGAAAATAAGCCGATATTTGTGATTCATGCAACTCACCCTTTTCTACATAAACAAAATACTAAC CCGAAAACCGAAATTGAAATTAATGCAGAGAAACCAGGTGACACATCATGTAGTAGACGACCAA GACAGT 159 ACAGTTCTCCTTCTTAGCTTCGTGAGAACTTAACGGCACCAACAGTTATTATATTTTTAGCAGT CCCGGGTGAAGTAATTATGGAATAGTTGTTAGAATTACTGTTCTTATTACCAGCTGATTTGAAA GCAATTATACCTGCATCACGAATTGCAGCATCATAATATTCCACATCATGTAGTAGACGACCAA GACAGT 160 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGGCTCAGACGACTGAAAAAGCAACGATTGGAATAA TAGGGGGTTCTGGGCTCTATGATCCTGGTATTTTGACTAACAGCAGAGAAATAAAAGTATATAC ACCCTATGGGGAACCTAGCGATTTGATAACGATAGGTAACACACATCATGTAGTAGACGACCAA GACAGT 161 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCGCAGAACAGGTTCCTTCTATTGGATATTCATCTT CGGCTGCAGTTGCAGGAAGAGTAAGGATATATACTACGGTCTTGCTTTCTCCTGAATCCATTTC ACCTGTCCAGACCCATTCATAGCGGTTAGCTTCACTGAGGTCACATCATGTAGTAGACGACCAA GACAGT 162 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCTTCCTCCACGCATTTGTTGTGGTGCTGATGGCGT ATTCTCTGGAATTTGGGATGATTCTGGAAATCCATCCTCAGACACTTCAGATATTTTAGTCTTA CTTCCAGCGTTTAATTGAACCTTACCTTTAAAAGCAGTAGTCACATCATGTAGTAGACGACCAA GACAGT 163 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGAAACTTACCTTATCAGTGTCATTAAGCATATTGC TTCCAAGACCCATTGAAGCACTTACATCGTTGATACACAGGTGCCAGGAATAGTATTCCTCAGT CTCACTATAATCCTCGTTGGTGTAGCCTTCAAGAGAGTCAACACATCATGTAGTAGACGACCAA GACAGT 164 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGTTTAAGCAATTCTTCGGATGAAAGATGGCGCTCT ATAGGAATTTGTTCTGGTCTAGCCATAAGGCATTATTTGTACTTAATTAGTAATAAATGTTTAG TTAATGACTATAAATCTGCAATTGGAGTCTCAAATTTTCAACACATCATGTAGTAGACGACCAA GACAGT 165 ACAGTTCTCCTTCTTAGCTTCGTGAGAACAACATGAAGGATGTGTGTAAGAGGAAACGTTATTA ACAGACGTAATCAGGAGGATAGTTATGCCCTAAAAACAGCAGAGTTAAGGTTTAAAAATAAGAT AAGAACTCAGTTGAGGTTTATCCATTAATCCCATTAATCCTCACATCATGTAGTAGACGACCAA GACAGT 166 ACAGTTCTCCTTCTTAGCTTCGTGAGAACACTTTCTAAAAGCGCTTGGAGCACGTATCAGGTCA AGTCTTTCAACCTTAAATGCTGCCAGTGCCGTAAGTAGTGCAGTTATGTTGCTTATTGAAACAA ACAACTTAGCCCACTTATTACCTCTTGTCAGTGTTTTTGATCACATCATGTAGTAGACGACCAA GACAGT 167 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGTATCCGCTGATATATCCTGGGGATATAGATCGCT CTGAAATGGTTACATCTATCGGTTTTAAGGACAGTTCCAACACTATTGGACCTTGCAGCTATGA CAGGAATAATCTGTTTATCGAGCACAGTTGAATTTGACCTACACATCATGTAGTAGACGACCAA GACAGT 168 ACAGTTCTCCTTCTTAGCTTCGTGAGAACTCAATACCTAATTCTTTCCTTAGAGTGCTATTTTG ATTGAATTCCCTCAGGAAAGATTCAAAATTTAAGTAGCCGAGCTTACATCTTGAAATTTCCATC TTTATTATGTTGCTCAGGCTTAATGCTTCTAAGTATGGGTTCACATCATGTAGTAGACGACCAA GACAGT 169 ACAGTTCTCCTTCTTAGCTTCGTGAGAACAGATATCCTTTGAAATTCTCGTAATTGCTGAAGGC CACTACTTCATCAGGTCTGATGCAATCTTTAATCTGAACATTGCTTTCTGAGGTCTTAGGAATA ATCCTGTAAGGGAGTCGGATATTGTTCGTTAAGATGCTCTTCACATCATGTAGTAGACGACCAA GACAGT 170 ACAGTTCTCCTTCTTAGCTTCGTGAGAACATAGAGGGACCTAGATTTTCAACGAGGGCAGAAAG TAGAATTTGGAGGGAAGTTTATAAAGCCGATATCATAGGGATGACTTTAGTTCCAGAAGTAAAT TTAGCTTGCGAAATGCAAATGTGCTATGCAACAATTGCGATCACATCATGTAGTAGACGACCAA GACAGT 171 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGTCTTCAGCATAGTACCAGCTTATGTTGTCACCAT CGTTCAGTACGTTACCACCAAGTCCACTGCCTGCAGCTACATCATTAATGTACAGGAACCAGGC ATAGTAACCGCCAGTTGATATGTAGTCTTCACCTTCGATTCCACATCATGTAGTAGACGACCAA GACAGT 172 ACAGTTCTCCTTCTTAGCTTCGTGAGAACACTCTCCATCATGACAGCCAGATCGGTCATAGCAT CGATTGTGTACTCTTCGTCGGGATTGTTGTATGGAATGAACTTATAGTTCTCACCTGCTACCTG ATCCACTGTCATTTCTGCAAGAGTCTGCACTGTGGTAATTCCACATCATGTAGTAGACGACCAA GACAGT 173 ACAGTTCTCCTTCTTAGCTTCGTGAGAACATATTCCGTATTTCTTATCAAACCGATCGTGAAGA TTTGACAAAGGCTTAACTTTAGGGCTCCACTTCTCATTATTAGCCTTAGAATATAAAGCGTAAC CGTAAGCCTGAGGAACGTAAAGCTTAGGAGATTCAATCCCGCACATCATGTAGTAGACGACCAA GACAGT 174 ACAGTTCTCCTTCTTAGCTTCGTGAGAACTAAAATTAGCCGAAGGCTTCCCATTACCGAAAAAG TCGTTTATTAGCTCTTCATCCTTCTTCTCCACGTCCGCCCATTCCTCTCCTTCCCTTGGAATTT TAAGCTCGTCCCAGCTGACTCTTATGGGCAATTCAATATCCCACATCATGTAGTAGACGACCAA GACAGT 175 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGTATAAACTTTTGATATAACCTTGCCTAATTTGAT ATCATAGCTTATGTTTGGCGCTATCCCCCACTTGTAGAGGGTCGCGTTATATTCTCTAATAGCA AGAGAGATACAAGATTCGTTAACGTTATTTATATCACTCTCCACATCATGTAGTAGACGACCAA GACAGT 176 ACAGTTCTCCTTCTTAGCTTCGTGAGAACTCCGGAGGAATCTATCATATTAAACCTCCTCAAAA TCGCCTCCTCTTGATTGCTTAAAGGCTGTGAATTACAAAGCTTATTTAATGCGTCCCAAAGCGT TAAGTAATAATTATTTATATTAAACACTACTATTTCAGTAGCACATCATGTAGTAGACGACCAA GACAGT 177 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGTTCCTCCTCAATTCAATTGGACTGAAGGAGGGTA CGTTCTGGAAAACAGAGCGTAAAAGAGATATAGAACGTAGTATACACATAGCTGGAAAAAGAAC AATCATTAAGACAATAAAGAACTTTATGGAAAAGAGTAGAACACATCATGTAGTAGACGACCAA GACAGT 178 ACAGTTCTCCTTCTTAGCTTCGTGAGAACTCGTGTAAAGGTTGTATAATTCAAGCCTCAGAACA TTTCGAACTCCTTACAAAATCGTTTAAACTTTCTAAGGCATAAATTTACTAGAAATTGTCATTT ATGAGAATGTAACTATATAGATGGTAAAATTATTAATCCTCCACATCATGTAGTAGACGACCAA GACAGT 179 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGGCTGAAAAATAGGTTCGATCCGCCTCCTCACTTC TTCTCCTTCTTGCCCTCGGCCTCGGAGGAGGCCTCTATTCCCAGCTTCTTGGCCTCCTCCTCGG TCGTCATGAACAGGCTAGTCCTCTGCCTTCCGCCCATGCTCCACATCATGTAGTAGACGACCAA GACAGT 180 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGACCTAGCCTTACGCACAGCCCTCTCCACAACCTC CTCAAGCTTATCCCAGTCAATAGAGCTCATTACAAGTTAACCACGCCCACCTTTAATATAAACC TTTACCCCTCGTGGCAATTAACTTTAACCGCTACTCCGGTGCACATCATGTAGTAGACGACCAA GACAGT 181 ACAGTTCTCCTTCTTAGCTTCGTGAGAACTGGCCCTTAGACCTCTGCCCATGCTTAGGCGCTTA CCCACACCTATTAGTACGGCGCCAATGCCCACGGCCATGAAGTACATTAAGGCACCCATGGTTG CACCGTAGAGTGCCGTGAATGTTCCGTAGAATACACCGGCCCACATCATGTAGTAGACGACCAA GACAGT 182 ACAGTTCTCCTTCTTAGCTTCGTGAGAACTCGGCGAATCTGTCGAGCTCCATGACGTCCACAGA GCCGCCGAACTTGGCCGAGAATCTATCGGCCTGGGCGGTGCGCCTCCCTATCAGCAAAACCCTG GGCGCCGTCAGTAGCGCGACGGCCCTGGCGATTCCCCTGGCCACATCATGTAGTAGACGACCAA GACAGT 183 ACAGTTCTCCTTCTTAGCTTCGTGAGAACTCCAGGTAGGATCTGGCCGAGAGGGAGGACGCCGC GCTGTTGTGCTCCGGGAACCCTAGAGTCACGACCGCCTTGACGCCTATACGTTCGGCGTATTCA GCGACGGCGGCGCCGGTGCCGCCCGTCAGCGTGACGGCAAGCACATCATGTAGTAGACGACCAA GACAGT 184 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGCAAGAGAATACATTTTTGATGATAAGAGAAGCTT GTGGCATACTTTCTTAGGCTTTATTTCAGCATTCACTTTAGCGTATTCTATCGTTATTTTGCTA TTGTTCACATTGTATCAAGTGAGAGAAAGAGAGAAGCCAACCACATCATGTAGTAGACGACCAA GACAGT 185 ACAGTTCTCCTTCTTAGCTTCGTGAGAACAGAATCAAAGGAGTGGTGTAAAGATGGAGAGAAAA AAAGGTTGGCATCCTATTTATGTGAGTGAAGCGGTTTTAAGTAAGTTAGATAAAGAGAGAGAAG AAATTAAAGAAGAATTAGGTATTCCAAAGGAAGAGAATTTGCACATCATGTAGTAGACGACCAA GACAGT 186 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGTTCAGCATAAAAGACGGTTTCACGGGCCAAAGCC TAAGCGGCGTAACGGTGAAAGAAGGAGATACGGTTTTGGGCACGATTGACGACGGCGGGACGCT GGAGCTCACGAGGGGCACTCACACCTTGACTTTCGAGAAGCCACATCATGTAGTAGACGACCAA GACAGT 187 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCTGATGTTATAGAAGTCCGCAAGGACGGCTCTGTC ATCTCGCCCGAGGGTGGGAAATACTATCTCGGCGACATAAGCGGCCCGACACAAATTAGCATCA AGTTCAAGGCCGGCGCGGTGGGAACCCACGGCTTCACTATCCACATCATGTAGTAGACGACCAA GACAGT 188 ACAGTTCTCCTTCTTAGCTTCGTGAGAACTCTCCCTCAACCTTCGCGGGGAGAACGGCGCGGAG TACTGGACGGGCTACGCGGACGCGCTGGAAGACCTGTTGAAGAAAATCCAGAGGCGGGAGGTGA GGGCATGAGAAGGTATTGTTACATCACGTGGGGATGGATCACACATCATGTAGTAGACGACCAA GACAGT 189 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGAGCGCCGGGAGGTGAGGGCATGAGTGAGGAATTG ATGTTTGGTCGTGTCGTGGAGTATGTTCAGCATAGTTTCTACAAGAAACCGTTTCCTCTTGGCA GTGAGCTCAAGAATGCAGTAGAGAAGGTTATGGAAACAGGACACATCATGTAGTAGACGACCAA GACAGT 190 ACAGTTCTCCTTCTTAGCTTCGTGAGAACAGGTCAGAGCCCACGTGGCAACTTTTGAGGTTCTG ACAAAAGACTATGTTCGTGAGAAATACAAAGACATCATAGAGTTCATGAGGGAGAAAGGGACAG TATCGAGAAAGGAACTGCGGAAGAAGTTCTTCTTGCTTGCTCACATCATGTAGTAGACGACCAA GACAGT 191 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGTACCTCAAAATACAGAATCATATTTTACAATCGC TTGGAAATATTAATATCAACAATACGCAAGTCCAAATTAACGTCCCTGGCAAACAGGTGACAAT TTATACCCACGAAATACTAGATAACGCCAAAAAGGCACTCGCACATCATGTAGTAGACGACCAA GACAGT 192 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCTTTGTATACTTAGATCAGGAAATGGAGCTAAAAG GCACTATCAAGAAGACAAAAGATTCCTGGAGAGAAACATTTAAAGAGTACTCCAAGACAGACAG CGAATATCTAATAAATTACAGACTGTTTTCAATACTCCCTCCACATCATGTAGTAGACGACCAA GACAGT Core Sequence 193 CAAATGCTTTCAGTGGTTTCCAATGCCCTGACCAGCCTGTACTCAACTAAGGGATATGACGTAA CATTCAGTGACCTGATCGCCGCCATTCAGGCAATGAAGGGCTACGATGACAGCGCAAACGCTAA ACTCGTCGTGGA 194 AGTCGGAGCTTATAACCACAGATCCTGAAGTATTGAGAAAGAGAAGGGGATGGTGAGATAAACA TGAGGTGTGNNNAAAGCTGACAATATGTACGCGTGCTTGAAGGACACTGTTGTGAAGGAAAGGT ATCCTGTCGCTA 195 ATGCAGTATATGGCAGGTGCGAGCAACTGCTTTACCATGGGTGCCATGGTGCAGAACGGGAGAA GCTCCCTCTACTGGAAGGTTAAGGACGCAAATTTCTGGTCCAAGACTTTCGAGAGCAAGTTGCG CGTTCTGGGGCT 196 GGCTGCGGAAGCCAGCAAGGAACCTTGTCTCTTCAGGAGAGGCAGGATATACGTCACATTCAAT GTAAGGAGGGTGGCACTGAGCGGTGCGGGACAGTTGACTGCCGCCAGGACTTATGCCATAGGCA ACACCGATGCGA 197 GATGAGGTTAAGGGGATATGCGAGAGATTCGGCAAGGTCGTGGATGCCGTGAACTCTCCTGTGC TTACCGAGAGTAATGCCTCGTACAGGAATGCGGGGCTGGTGCGTGCCAGGTTCAACTGGGACTA CATCAGGCCCGA 198 TGGTCTCCACGGAAGGCTACATGGACAGGGCAATAGGCGTCCAGGATATCGGCTACCTGTTCTG GCAGGCAGGTCCCACCGCAATGAAGGATATGAGAATTTACAACGGTCCCGGTGGTCTGATCGTT CTGCCTTTCTAT 199 CTTTCTGCGCGCAACAGGCTGGCCAAGGGACTTCCGAAGAGCCTGGACATGTTTGCCAGCGTGG AAGGTCGTGACCTTGGGTACGATCCGAGGTACATAACAGAGGAAGATTACAAGACCATTATGAC CAAGGCCCGTCT 200 GGCATGATAGATGAGGATGGCTACGAGGTACCGAAGGGTGAAGACCCCAACGACCCCAGAAGTG CACACACCTTTGGTTGGGTCGACCAATCAGATGGAGGCACATCCAATGGTGGCATGCAGTCCGG TGGGAGCTCTCA 201 CTTAAGGACGGGGACGTGACAAAGCTGTATTCTCAGGACGATTACCTCAGGGTCAGCAGGCTCA AGTTCAGCGAGAATCCGATGCTTGGCATCGTCAAGAATACGGATGGCACAGGGGAGGTTATAGG TCCGTCCTTTGC 202 AGGGACCTTCTGTGGAACATAATCTCGGGTGCCCTGAATGCGGGAAGGGAACAGCTCTACGGGG ATGCATTCGGCGGTCCTAAGATAGAGCAGTACGTGAAAGCACTCACGCAGGTGCTGTATGACCT GTCTGTCAACAG 203 TGAAGGAACGCGTCATCGTCCACAAGAGCACTACGAGGAACGAAGTCCTTGAAGAGTTCAAGGC GTCTAAGGAGCCGAAGGTCCTATTTGCGATAAAGATGGAAGAGGGTACGGATTTCAGGGATGAC CAGGCAAGGTGG 204 CAGATATTGGTCAAGACTCCTTACCAGGATCTGGGAGACGAGTGGGTCCGCCTCCATAGGGAGA AGATGGGACGGAGATGGTACGAGATATCCGCCCTCCAGCAGGTCATCCAGGCGAGCGGCAGGAT AATGAGGAACGA 205 CAGGGACTGGGGAGACACCTACGTCCTTGACATGAACGCCATGAAGCTCATCCGCATGTACGAA AAGGAATGCCCCCGCTGGTTTTTGAAGAGGTTGAAACTATGACGCATCACAATATCACCTTCCC CGTCCCTCCCGA 206 GCAATGTCCGCAACAGTTGACAACGTTGCACGGGTCGCGGGATGGCTCAGGGCGACTGCAGTCT CCAGCGATTTCAGGCCCGTCACCCTGAAGAAGTACGTCCTTACCCCCCGCCATATCATGGATGA GAAAGGGGATAC 207 CGAGATACAGCAGATGCTGGGGCGCGCCGGGAGGGCGAAATACGATTCCATGGGCTACGGCTAC ATCTGCTCATCCGACGTTCACCTCCAGGACGTGTATAAGACGTACGTTCATGGCCGTCTGGAGA GCGTAAAATCAA 208 GGCACTGGACGAGTTCTTCTCCACCACGCTGGCACGCCACGAGGGTGCCCGTCTGGAAGAATGG ATAGACAACAGCCTTGTCTTCCTGCAGGACAACGACATGATAGTCGGGGGACGTTCCTTCACGG CTACCCCCTTCG 209 CCATCCTTGCGGACTGGATAGACGAGAAGCCCGAAAGCGACATCGTCAATAAATACAACATCTG GCCTGCCGACCTGAGGAGCAGGGTTGAGTTGGCCGAATGGCTCTCGCATTCCCTTTACGAGATC TCGAGGGTCCTG 210 GGCGACTATTCCTACGTCAGCGTTGCGGAATATTTCTCCAGCTCAAGGATAATAGCCACTACCG CTTCCCCCGGTGGCGACAGGGAGAAGATAAACGAGATCATGCGCCACCTGAGAATAGAGAACCT TGAGGTGAGGGA 211 ATCGACGCTCCAGCTGTTCAGGGACGGTGCGGTCAGGATACTCGTAGCAACGCAGGTTGGGGAG GAAGGACTGGACGTACCGGCTGCAGATACCGTCATATTCTACGAGCCGGTGGCAAGCGAGGTCC GCTCAATCCAGA 212 CGCGGTCCTCTTTCCCAGCTTCAGTCCTTTGGCTTTCCTATGTTCTGCTGGTACTCTTCCCATT GCTCTCTCTGTTGTTTCTCCTGGCTTTTCCTGAAGTTTTCGAGGGCTTCGTCCATGCTGTCATA AGCGTTATGCGA 213 CGAACTCCTTGACGCTCCTGGAGATGACCAGTTTCTCTATCTCCACCCTCCCGCTCTTCATGTC CGATATTATTTTCCTCGCCCTTCTCAGTGCCTCGTCCACGTTCCGGTCGAGCACGAGATTGAAC ATTTCCATGAGT 214 CCCTTTCCTCGAGGTCTTTGTCTATTCCCTTCACCGTTTCCCTGGCCCAAGCAGTGATGCTGGA CCCGATACTGGGATCGGTAAACCTGTAGAAGCTTGATGCAAACACTCCGTAGAACGAATTCATA AGCACCTTCACC 215 CAGTTCTCCTTCCACCCCGTCCGCCTCTTCTATGACGGTCGCACTTTCCGTCGAGCAGACGCCC TACGGCTGGATGGATCAGTATCCCTCGTCGGTTGTTGCCCATGTCACGGGAGGCATCCCTCCCT ACGCCTATCACT 216 CAATCTGGAGGCTTTCGCCGTATCCGTCGAAGTCACGGACTCATCGGGGCATTCAGTCTCAGGT GCAATCATGATCAACTACGGTTCCATAGACCTCTCCCCGTTTGGATACATGGTAACTTTGATTT TTCCGGTGATCA 217 GAACATCCATTGCTGACCATCACGATCGATGGCTCAAAGGACACGTTCAAAACTGGCGATGTCC TGGAATGGTTGACCGAAAGTGACATCTCAAACATGCACAATGTTGCGTCCTTCACAAAATCTCT CCTGAGGATAGT 218 GCATGCTGCCCGATGGCCAATGGTTCGGGGAGGTCATTGGGAAGGACGTGCAGGGAAATCCCTA CGGCATTGATTATACAATGTGGTTGCCGTTTAACACCTACGTTAGGGATAAGCTCAGTTACAAT AGTTGGGGGAAG 219 CCAGCATTCCTCCTCTGAGGGAGTTCGGAAGGTAAAATCTCCTGATGACATCTGAGTCCCTGGC GCCCATTGTCTTGGCTGAGTAGACTTCCATTTTACTTGTCGTGCTGCCAGTTGAAAATGCAAGT ACTGCTATCATC 220 GTTCCCTGCAGGAATGATTGTTCAACTGCACTCGTCAGTACATGATAGAACAATCTTGAAGCTG ACAGATGGAGCGCATAGAAGATAAGGAATGCAAGAGGTACCAGTACTAGTACCACTGCATATAT TCCTGCGTATAG 221 CGATTGCAGGAACGTGGAAAGTGTGCGGTTAATGTATGACTCATTGCTCACATCGAATCGATAC AGAAGACCGCTGTTTGCTGCCAGGTAGGAATCTATGTTATTCAGGCCAACAACCACATCGTATC CCGCCCCCTTTG 222 AGGATTGAACCGGTTGGTGTCAACGTAGAATCCTCATTGACCCGCCACATAAAACTGAACATTC CAATTGTGTCGTCTCCTATGGATACGGTCTCTGAGGCAGATATGGCAATTGCACTAGCAAGACT CGGTGGTATTGG 223 CTTATTATACGCGACCTTTACACTGTAAGCCCGGAAACACCTGTTGACGATGCAATCCGTACTA TGAGGGAGAAGCGAATCGCTGGGCTCCCAGTGATATTGAACGGCAAACTTGTCGGAATACTTAC GAACAGGGACAT 224 GGTACGGCAAGATAGGCTCAGGGAAATTTGTACCAGAGGGAGTTGAAGGAGCAGTTCCGTACAA AGGTAAAGTTGCAGATGCAGTCTTTCAATTGATCGGGGGCCTGAAGTCGGGGATGGGGTATACT GGCTCGCCCACA 225 GGTGGAAGCGTTGAGGAGTTTGTCACTCTATCGAGGAGAGTGGAGGCAGCGGGATTCGACAAGG TCGAGCTCAATTTGTCCTGCCCACACGTTCAGGGAGTTGGATCCGAGGTAGGACAGGATGTAGG TCTTGTAGAAGA 226 GACACTTATAGACAGGCTAGACAAGAAGACGAAGACAAGGATATTCTTCTCACTTGAGCGATTG ATGAAGTGCGGCATAGGGATTTGTGACAGTTGCAGCATCAACGGCATCCGGGTATGCAAGGACG GAACAATTTTCG 227 CTTCGCAACTGCAAAGAGGTAGCTTCTGGATGCTTCCCTGGAACTATCCCTACATTGCTGTTAT CTTACTAGTGGTACTGATTTATGCAGCAATAGAGGACCTTAGGAAGAGGAAAATAACAACTATA ACCTTCCTTGCA 228 GTGACAGTTGGAACTGGTCTATCTCCCCGGTATTTTAATAAGTTTATAGGCGTAGCAAAGGCAT ATACGACAAGAGTAGGGGAGGGGATATTTCCTACTGAGATGTTTGGGGAAGAGGCAGATAGACT TAGAACCCTAGG 229 GAAGAAGACTTAAAGGATTTAGGTAGAGAGCTTAAGGTACCAAGAAGACCGTTCAAAAAGTTAA CGCATAGAGAAGCTGTTNATATATTGAGATCTCATGGCATAAAAGCAAGTTATGAACATGAGAT ACCTTGGGAAGC 230 ACGGGGAGGCTGTCTCAGGAGCTGAAAGAGAATATAGAGCGGAGAAGGTTATTGAGAGGATGAG AGCTACTGGTGAGAACCCTGCAAAATACGGTTGGTACATTGAAATGTTGAAATATGGTATTCCG CCGAGTGCAGGG 231 ATATGCAGATTTAGATGAGATTATAGGGGTTGCATCTAAGGCAGGAATAGATTGCATAACTATA GATGGGTCAGAAGGTGGAACAGGTATGAGCCCTATAGCTGCGATGAGAGAACTAGGATATCCAA CGCTAGTATGTC 232 GGACACGAAATTGCTGAAGCAGCTGGCTCAACATGGTATATCGACAATTTCTGGGATAAACTCA AAGAGGGCTGTGTAGCATATCTAAACATAGATTCACCTGGATTAAAAGATGCAACAAGATATAT CGCTTACGCGTC 233 GTAACTTCTGGAAACGCCCAATCAAAACAGATCATGACACCAAAGCTAAAATTATCTTCCCTAA TAGCTTCTATAGGTGTATCTCCAGGTTGAAATATTAGCTTCTCTTTGGCAAATAAGTGAAGTTT CCTATACTTTCC 234 CCAGATAGCCCAATAGCATCAATTTCCGTTGCAATAATAGGTACAGTACACAAAGAACACGTAA TTTTCAGCGACACTGCAAATACAGGCGACTTAATAATTTTTGCCATAGATCTCGATGGAACATT TCACCCTAAGTT 235 GTTCTAATTCCTCTCTTACAGCTTTAAAAGCAATCACAGCAGATTCCAAAATATCATCCATATC ATCCAGAGCTATAATAACACCTCTTGAAGTTTTCCCAATCTTATGCCCACTTCTTCCAACTCTT TGAACCAAACGA 236 GTAACTTGTCTGGGAGACATATATTGGACAACTAAATCAACGGTTCCTACATCAATCCCTAACT CCATAGATGATGTACAAATAAGACCTTTCAACTCACCGTCTTTAAATAACCTTTCAACTTCTAT ACGAACATCTCT 237 ACTCAATGAACCATGATGCACATCAATACTTAGATTAGGATCGTATAAGTGAAGCCTAGAAGCT AGTATCTCAGCTATTTCACGAGTGTTTACAAAAGTAAGCATAGAGCGGCTCTTTTCTAATAACT CAACCAATACCC 238 CAGTTAAATCATCTTAACTCACAAATATTAAGGCTTTAATTTCTGAGGGAGTGCAAAATGAAAA CTGACGTAGTAATAGTAGGTGCAGGGCCCGCAGGCATGTTTGCTGCACATGAATTGGCAACTAA ATCTAATCTGAA 239 AAAAATAGCCAAGGATCCAAAATTCCGTGTATATACAAAAACCTTCGATGACCTTACACGTGTA TTTTGCGTTAATTATCGAGGCTTCGTCGTCCAAGAAGTCTACGGAGATATCGTTGGTGTTAACG GCCACACTCTAA 240 TCAAACAAAAATCTGAAAATGCCAATTTTGCATTTCTAGTTCGAGTTGAACTCACCGAACCGCT TGAAGACACAACCGCCTACGGATTCTCAATAGCCAAATTAGCAACTACCATAGGTGGAGGAAAA CCAATTCTTCAA 241 CGAGATACTGAATTTCCAAAACTCAAAGGATATAGAATTGTTAGAATCGCAACACATCCGCAAG TTATGAGCATGGGACTAGGAAGTGAAGGGTTGTCAAAACTTTGCCAAGAAGCCGAAAAGAGAGG ACTAGATTGGGT 242 CGAAGTTTTTATCCTCCTCGGTCCAAGTCACACTGGTTACCCAGGCGTTGGAATAATGACAGAA GGCATCTGGAAAACTTCTTTAGGAGAAATATCAATAGATGAAACTCTCTCGAATACTATTTTAA ATAATTGTGACC 243 TGACACACTACGGCACCTACTATGGATACACACCAGCTGGTGTTGAACCATTAACCAAAGTTTT AGAATGGATATACCAGACGGACAAACAAGTTATTGAGAGAATTAAAAGATTAGATGGAGCAGGA GTAATAGAATAT 244 CTGAAAAGTTCATTCCAATTGTTAAATCGCCATCTTGGAAACACGGCACAAGAAAAGGGAAAGG ATTTAGCATCGGTGAGATTAAAGCAGCCGAGATAGATATTAGTATGGCAGTTAAACTCGGTATA CCCATTGATAAA 245 GGGAATAATAATTAAAATAATGTGGCACACCTTTTAGCTTCTTTTCATCTCATATTTTCAAAGA AGCCTTCCAGGTGTGCCTCATCGGTGTCCCCCGCTGCGGAGACACGGTATCATCGTATCCGCCG AAGGAAACTCAA 246 GACATTGCCTATCAATTACTTCAAGCCGGAATGCAAGTTCCCGGTTTCAGAAGGTCGCCAAAGA TAATAGAAAGAATTTTAGAAAGATATATTCCAACAGTCACCGTACTAGGCGGCATTATTGTAGG ATTAATAGCTGC 247 TGTCGTTCAGGGAGGTATAAAAATGCCAGAACCACGCTACCGGTCAAGGTCTTTAAGAAGACGA TACGTACACACACCTGGAGGAAAAACCGTCATCCATTACAGGAGAAAAAAACCTGACGTTGCAA AATGCGCATTAT 248 GTGGTCAACCTCTCAGAGGAATTCCCAGACTAAGGCCAGGAGAATTCAGAAAGTTGACAAAAAG TCAACGAAGACCAGAGAGACCTTTCGGTGGATATCTATGCCACAAATGCTTAGCAATGGAAATC AAGAAAGCTGTT 249 ATAGGATGAATCTAACTGGGGCGACCCGGTAGATAACTGAGAGTGTAGGAGGTGAAATAATTGA GCGCAATAGAAGTAGGTAGAATATGTGTTAAAACTAGTGGAAGAGAAGCAGGAAGAAAGTGCGT TATTGTTGAAAT 250 ACACCATTTCCTAATATTTTAGTAACTAGATATGTTTGTTATAGTATTAGGGTGAAGTATTTGT ATGAAAGAAAGTTGCCATCAGACATTAAAAGAGAGATTCTAGTAAAAAGTGAAGCAGAAACTGA CCCTGCTTATGG 251 CACATGAGAGAACTTAGAAGAACACGTACAGGACCCTTTAAAGAAGATGAAACCCTAGTAACTC TTCACGATGTAGTTGATGCTTACTATTTTTGGAAGGAAGATGGAGAAGAAGAATTTCTACGAAA AGTCATACAACC 252 AATGGAAAAGGGTTTAGAACACCTACCTCACATTTGGATTAGAGATTCTGCTGTAGATGCAATA TGCCATGGGGCAAACTTAGCAGCTCCTGGTGTTGTAAAACTTCATGACGGTATATCACCTGGAG ACTTAATAGTAA 253 CGCTGATCATACATGTGCATTGTCTTTAAATACACTAGTAACGTTAATAATATCTAGCAATTTT AGATAAAAATAACTAGCAGTGCCGGGGTAGCCAAGTGGACTACAGGCCTTATACCGGTTAGGGC GCGGGCCTGGAG 254 CATGCCTTAACGAGAGGCATGGGATGGGGGAGCTGTGAGCCCCCCGAACCGGCAGATGAGGGGA AGGGTGCAAAGCATCCCTTAACGCCGGAAGCTCCCGACTTCAGTCGTGGAGCAGCTCACTGCTT TGACGAAAGGTT 255 GAACTTGCAAGGAAGGCCGGTGTTGATTATGAGACAAAGCTGTTGGTCAGGGGCAAGGAACCGG CTGAGGACATAATAGAATTTGCTGACGAGATCAGGGCAAGTCTCATTGTAATAGGGGTTAGGAA GAGGAGACCCGC 256 TCCAGAAGAGATTCAAAGCTCTCGTATTCAATGTCCCCACCAAATTTCTGGTCGCGCTCAATTT TGACTTTACCAAAAGCGGGGAAAACGTAGTGCTTTGCTAGGTCTATTATCGGATTTCCTTCTAC AACCTTTGGCGG 257 GATTTGCTCATTTTCTCCCCGTCGAGTCCTGAGATTATCGGCGTATGGATGCAGATCGGTGCCT TGTAACCGAGGGCCGGCAGATTCTCCCTTGCGAGCATGTGGATCTTTCTCTGATCTATTCCACC AACCGCCACATC 258 TCCGGGAGTTGCAGAACCAAGCATGGAAATTGCTAGAGATCCCGAAAAGGTTTACGAGTACACG AATAAGTGGAACACGGTTGCAATTATCACTGATGGCTCGAGGGTCTTGGGACTGGGCAACATCG GTGCGATGGCTT 259 GTGGTGTTATCAAGAGGGAATATATTGCTCAGATGGCAGAGGATCCGATAGTCTTTGCCTTATC AAACCCGGTGCCTGAGATCTATCCGCAGGAGGCAAAGGAAGCCGGAGCCAGGATCGTAGGAACT GGTAGGAGCGAC 260 GGGATCTGTTAGTATGGCATTCAGAGCCTTTATGTCCTCATCGGTAAGCTTGTCCGATGGCAGA TCGTATTTCACGATGTCTGAAGGAGTAACTCCGAGAAACTTCGCTTCTGGTGTCGCAAGATACT CCGAGAGATGCG 261 TGAGTGCGGCTTACTCTGCACTGTGCGAGATCGATGAGGTCGTTGTTGTTGCCCCCATAACGCA GATGAGCGGAGTGGGGAGGAGCATATCCATAATGCGGCCGGTTCGTTTTTTCGAGCTCGAAATA GATGGCATGAGG 262 AGGGGAAGGGAGTACTACTGGATTCATGGGGTGGAAGTCGAAAGCGCTGAGCCTGGAACGGACA TACACGCACTCAGAAACGGGTATGTCTCCATTACACCGATATCCTTAAATGCAACTTCGGACTG CGAAGCTTTAAG 263 ATAGTTTTATGGAGGGTGGTTGGACATGAATGAAAGGGCAAAGAAGGTCATTCTTATTGTGGAT GACGATTTGGCTCTGCTTGAAGCTCTTGAACTGATGCTTCGAGGCAAGTATGAGGTTGTGAAGG TGACAAATGGGA 264 ATGTCGATTCCGAAATAGCAGGGAGCAATTATCGGTGGGCTTCCGACCCTTAAATGGATTTCCT TCGCTCCCGCCTTTCTTATCATGTCGACTATTCTTTTGGATGTTGTTGCCCGCACAATGCTGTC GTCAACCAGCAC 265 ACTTTCTGAGGGAAAAACATTGTTGCTTATCCTAAAGAGTTTACAAGCAAGAAGCTGGAAACAA ACTCTGGATGTTATTAATTTAGAGCCTGCAGCAGCATATACAATGTTTAGAGCGGCAATAAAGA AACTATACAAAG 266 GTGGTTGAGAGGCTGCTTGAAGGCATTGCAAAGAATGAAAGGGTAGCTTACGGATTGGAGGAGG TTAGGAGGGCAAAAGAGTATGGAGCAATTGAGGTTCTGTTGGTTTCAGATGACTTCCTGCTCAC CGAGCGTGAGAA 267 TCGCTTCGAGATTCCTGATAGGAGTGGGAGTTGCCGGGGTTTACGTGCCTACGATAAAAATAAT ATCCGTCTGGTTCAGGCAGAATGAGTTTGCAACTGCTACTGGGATTCTTTTCGCGATTGGAAAT CTAGGAGCGATT 268 GAGGTATCGCCTACTTAGAGAGTTCGTAAAGTCGGAGATATTGGAGGAAGTTAAATTTGAAAAC GTTGTGGACGAGTACTGGGTTGCGGAACCATTCATAAAGATCATAATTTTTGAGGATCTCGAAA ACCAGAAATTGA 269 CTAATCCGATTATCGATTCTACGCTTCCTGATGGTAGCAGGCTTCAGGCTACCCTAGGAACAGA AATTACACCTAGAGGCTCGAGCTTCACGGTGAGAAAATTTACAACCCAGCCACTGACCCCGTTA GATCTAGTGAGG 270 CAAAATTATATCGATAGAGGATACCAGAGAGATAAAGCTCCATCATGAGAACTGGCTGGCTCAG GTGACGAGAACGGGGATAGGAGAGCAGGAAATTGACATGTATGACCTTCTCAAAGCCGCCTTGA GACAGAGACCGG 271 GAATCAGTTTGTTAAATGGGATGCGAAGAAAAATTCGCATGTTGAGGTAGGGATTCCGAAAAAG CTAGAGAAAATCGCGATGTCGAGAGTGGACGATGCTTACGCGGAGCTGGAAAGAAGAAGGAGGT ATTTGGAGTGGA 272 TCAGTGAAGTTAGCACGGAATTCGAAAGGATAGTGGTTCTCGTTGAAATGGGAGAGGATTTGGA AAGCGCAATGAGGTTTGTTGCAGAAACAACTCCCTCAGAGAGGCTCAGGGTTTTTCTGGAGAAC TTTATTGATGTG 273 GCTGGAGCGGGAGGCGTATCAACGCTTGCCCTCAATCCGTTACCCGAAGTTCCAGAATACTTTG AGTATTTCCAGTCCGAATAGAAGCAGAGCACCTCTCGATCGACTAGAGTCTTTCTGCTAGCTCT TGCACCCTCATC 274 GCGGAAATCTCTGCTGAAAACACCTTGACTTTTTCTTCGTATATCTCCCATTCCATCAGGCACC ACCAACTTTGGTCCTGCAAAGAGTCATCGGTGCCCCATCTGCTACGGGAACGATCTGAAAGGCT TTACCACAGAAT 275 TCCGGTTGCAGGATTGGTCTCCCCACCTCTCGAGCCTATGAGGAATACCCCATTCCTGCAGAGC TCGAGAAGCTCTTCGAATTCAAGATCCCCCTTCTGCAGGAATGTGTTGCTCATTCTGACAATCG GAAAAGCAACTC 276 AACTCCTCGATTGTTGGGTCATCGATTATTGTCACGTTCTCTCCTGCAATTCTCTCTCCAATCT TTCCAGCAAGAACGCTGTTTTCCTGCAGAACGTGATCTGCCTCGACCGCATGCCCGAAAGCTTC GTGAATAAAAAC 277 CTTTCTTCAAGAATGCTTTCTGCGGCGATAAGCCCAGTAACAGCCGCTCCAACTATTCCCCTGC TTATTCCGGCTCCATCGCCAATTGCATAGATGTACGGTATGCTTGTCCTCATCTTCTCGTCAAC CTTAAGCTTCAA 278 TGCTGGATTTTTCTTTGGCCTTGCTGTGGCCGTTGACTAGACAAAAGTCGCCGTACTCCTCTCT TATAACCCAGCCCCTCGGGCAGGTGCAGAACGTGCGCATGTAGTCGTCATGCCTCTGTGTGATT ATTCTCAGCTTT 279 TTGCCTTGGAATTTTCCGCCACTTCGATCTTATACTTTTTTACCCATTTTTCCAGCCAGTCGGC ACCGCTCCTCCCAACTGCAATTATGAGTTTGTCGTAGCCGAACTTGTCCCCATCGTTCGTCTTC ACGATCTTTTCT 280 AATCACCACCGACTGTGAAGCTCGAAGGATAGTTGGGGTTGGCATAATTCAGCTTTCCATCCGA AAGTCCTCCAGCACCACCCACACCAGAAGTAATGTTGCAGGGATCGCATTTCTTGCAATAGCTT TGCGAAAGGTCA 281 TTAACCAACCTCTTTCGCATCAAAATCCCAACTGCGGCATCCGTTATCAGCGTTACATCGATTC CATCTTTCATAAGCTCGTAGCAGGTGAGCCTAGAGCCTTGGTTCAGCGGCCTCGTTTCGCAGGC GAAAACCTTTAC 282 TTATCGAGTTAATAGCTATCAGTGTTGCTATTACGATCGTTGCGATCCCATCAAAGATGTTATG ACCGAAGGAGATAGCAATAATGCCAAATATCGCTGCAAGCGTCGATAAGGAGTCGTTAAAACTC TCAAACATCACT 283 CTCCCTTCTAAGCTTCGTGATATCTGCATTGCCAATATCAACTAGAAATTCGATTGAGATAAGC TTGTCTCTTGCGGTTAAGCTTGTTCTCTCGATATTTATACCGAAATTTAGCAATACACCCGTGA TATCTCTCACGA 284 AAGCGGGGCTTTTGCCTTTCCAATTCCGCCGCAACCAACCGTTGGACTTATCAAACCGGAACCT TTCAACTCCGAGATTAAAGAGCCTGGCTCCTTATCGTGCTTAATAGCAATTTCTACAATGTCTT CCCCGCATACAA 285 CTAGTTCTTGGTTTTCGTCGACGTTGACCTTGTAGAACTCTACATCTGGAAACTCCTTTGAAAG CTTTTCGAGCACTGGGCTGAGATACCTGCACGGCATGCACCAGTCGGCGTAGAAGTCAACAACA ACAAGCTTATCC 286 CTCCGATCGTCTTTAAAGCTTGCAAGTCTAAATCCTCGCCCCAGGGAATTTCCTGGGATTTTCT CGCAATCTCTATCGCCGAAGTATAGGTTATCCTCGGGAATGGTATCTCGGGGACTTCGAGCTTT AGTTCGAGAATA 287 GAGGTTTTGTCCCTATTGGGTTTCTCATTGCCTGCAGCATTTCTTCTCTGCTCAGAGCTCTGCA GCCATCGCCTTTCATTCTTAAAATGCTAACCTCCCAATCATCCGGAAAATCGAGCTCTATTTCC CTATCCTGCCAG 288 TGTTTACAGGCTGGTGGGTGGGGAAAGGAGTGTTAAGGGCAAAAGGAGTGTAAGCAAGTTCAGG GTTGCGATTGCGATTCTTCTGGCATTCATTCTGATATATCCTACATACCGCATAGCCGAGATTC AAAGCAGTGGGG 289 CAGGGTCAGGAGGATTCACGAGATAGAAGTCCTCGAGGTGAGAGGCAGGTTCGCGCTTATAAGG GTTCTCAGCGACCCCGGCACGTACATGAGGAAGCTGGCCCACGACATCGGGCTATTGCTCGGAG TAGGTGCACACA 290 GAAGTCAGTCATAGAATCAATGGTGTATTCTTCATCAGGGTTATTATACGGAATGAACTTATAG TTCTCACCTGCTACCTGATCCACTGTCATTTCTGCAAGAGTCTGCACTGTGGTAATTCCACCTT CTTCCATCCGGG 291 AGTAAGGGAATCAATGTCTTCCATTGCTGTAAGGGTTACTGTTACCTTTGTAGAAGTCAGACCG TAATTGGTCAGCAGCTCTTCATAGAAGTTCTGGTTTGCAATATCCCTCTGGGCAATGACAGGGT AGTCGACTTCGT Primer and Core Sequence 292 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCAAATGCTTTCAGTGGTTTCCAATGCCCTGACCAG CCTGTACTCAACTAAGGGATATGACGTAACATTCAGTGACCTGATCGCCGCCATTCAGGCAATG AAGGGCTACGATGACAGCGCAAACGCTAAACTCGTCGTGGACACATCATGTAGTAGACGACCAA GACAGT 293 ACAGTTCTCCTTCTTAGCTTCGTGAGAACAGTCGGAGCTTATAACCACAGATCCTGAAGTATTG AGAAAGAGAAGGGGATGGTGAGATAAACATGAGGTGTGNNNAAAGCTGACAATATGTACGCGTG CTTGAAGGACACTGTTGTGAAGGAAAGGTATCCTGTCGCTACACATCATGTAGTAGACGACCAA GACAGT 294 ACAGTTCTCCTTCTTAGCTTCGTGAGAACATGCAGTATATGGCAGGTGCGAGCAACTGCTTTAC CATGGGTGCCATGGTGCAGAACGGGAGAAGCTCCCTCTACTGGAAGGTTAAGGACGCAAATTTC TGGTCCAAGACTTTCGAGAGCAAGTTGCGCGTTCTGGGGCTCACATCATGTAGTAGACGACCAA GACAGT 295 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGGCTGCGGAAGCCAGCAAGGAACCTTGTCTCTTCA GGAGAGGCAGGATATACGTCACATTCAATGTAAGGAGGGTGGCACTGAGCGGTGCGGGACAGTT GACTGCCGCCAGGACTTATGCCATAGGCAACACCGATGCGACACATCATGTAGTAGACGACCAA GACAGT 296 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGATGAGGTTAAGGGGATATGCGAGAGATTCGGCAA GGTCGTGGATGCCGTGAACTCTCCTGTGCTTACCGAGAGTAATGCCTCGTACAGGAATGCGGGG CTGGTGCGTGCCAGGTTCAACTGGGACTACATCAGGCCCGACACATCATGTAGTAGACGACCAA GACAGT 297 ACAGTTCTCCTTCTTAGCTTCGTGAGAACTGGTCTCCACGGAAGGCTACATGGACAGGGCAATA GGCGTCCAGGATATCGGCTACCTGTTCTGGCAGGCAGGTCCCACCGCAATGAAGGATATGAGAA TTTACAACGGTCCCGGTGGTCTGATCGTTCTGCCTTTCTATCACATCATGTAGTAGACGACCAA GACAGT 298 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCTTTCTGCGCGCAACAGGCTGGCCAAGGGACTTCC GAAGAGCCTGGACATGTTTGCCAGCGTGGAAGGTCGTGACCTTGGGTACGATCCGAGGTACATA ACAGAGGAAGATTACAAGACCATTATGACCAAGGCCCGTCTCACATCATGTAGTAGACGACCAA GACAGT 299 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGGCATGATAGATGAGGATGGCTACGAGGTACCGAA GGGTGAAGACCCCAACGACCCCAGAAGTGCACACACCTTTGGTTGGGTCGACCAATCAGATGGA GGCACATCCAATGGTGGCATGCAGTCCGGTGGGAGCTCTCACACATCATGTAGTAGACGACCAA GACAGT 300 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCTTAAGGACGGGGACGTGACAAAGCTGTATTCTCA GGACGATTACCTCAGGGTCAGCAGGCTCAAGTTCAGCGAGAATCCGATGCTTGGCATCGTCAAG AATACGGATGGCACAGGGGAGGTTATAGGTCCGTCCTTTGCCACATCATGTAGTAGACGACCAA GACAGT 301 ACAGTTCTCCTTCTTAGCTTCGTGAGAACAGGGACCTTCTGTGGAACATAATCTCGGGTGCCCT GAATGCGGGAAGGGAACAGCTCTACGGGGATGCATTCGGCGGTCCTAAGATAGAGCAGTACGTG AAAGCACTCACGCAGGTGCTGTATGACCTGTCTGTCAACAGCACATCATGTAGTAGACGACCAA GACAGT 302 ACAGTTCTCCTTCTTAGCTTCGTGAGAACTGAAGGAACGCGTCATCGTCCACAAGAGCACTACG AGGAACGAAGTCCTTGAAGAGTTCAAGGCGTCTAAGGAGCCGAAGGTCCTATTTGCGATAAAGA TGGAAGAGGGTACGGATTTCAGGGATGACCAGGCAAGGTGGCACATCATGTAGTAGACGACCAA GACAGT 303 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCAGATATTGGTCAAGACTCCTTACCAGGATCTGGG AGACGAGTGGGTCCGCCTCCATAGGGAGAAGATGGGACGGAGATGGTACGAGATATCCGCCCTC CAGCAGGTCATCCAGGCGAGCGGCAGGATAATGAGGAACGACACATCATGTAGTAGACGACCAA GACAGT 304 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCAGGGACTGGGGAGACACCTACGTCCTTGACATGA ACGCCATGAAGCTCATCCGCATGTACGAAAAGGAATGCCCCCGCTGGTTTTTGAAGAGGTTGAA ACTATGACGCATCACAATATCACCTTCCCCGTCCCTCCCGACACATCATGTAGTAGACGACCAA GACAGT 305 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGCAATGTCCGCAACAGTTGACAACGTTGCACGGGT CGCGGGATGGCTCAGGGCGACTGCAGTCTCCAGCGATTTCAGGCCCGTCACCCTGAAGAAGTAC GTCCTTACCCCCCGCCATATCATGGATGAGAAAGGGGATACCACATCATGTAGTAGACGACCAA GACAGT 306 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCGAGATACAGCAGATGCTGGGGCGCGCCGGGAGGG CGAAATACGATTCCATGGGCTACGGCTACATCTGCTCATCCGACGTTCACCTCCAGGACGTGTA TAAGACGTACGTTCATGGCCGTCTGGAGAGCGTAAAATCAACACATCATGTAGTAGACGACCAA GACAGT 307 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGGCACTGGACGAGTTCTTCTCCACCACGCTGGCAC GCCACGAGGGTGCCCGTCTGGAAGAATGGATAGACAACAGCCTTGTCTTCCTGCAGGACAACGA CATGATAGTCGGGGGACGTTCCTTCACGGCTACCCCCTTCGCACATCATGTAGTAGACGACCAA GACAGT 308 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCCATCCTTGCGGACTGGATAGACGAGAAGCCCGAA AGCGACATCGTCAATAAATACAACATCTGGCCTGCCGACCTGAGGAGCAGGGTTGAGTTGGCCG AATGGCTCTCGCATTCCCTTTACGAGATCTCGAGGGTCCTGCACATCATGTAGTAGACGACCAA GACAGT 309 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGGCGACTATTCCTACGTCAGCGTTGCGGAATATTT CTCCAGCTCAAGGATAATAGCCACTACCGCTTCCCCCGGTGGCGACAGGGAGAAGATAAACGAG ATCATGCGCCACCTGAGAATAGAGAACCTTGAGGTGAGGGACACATCATGTAGTAGACGACCAA GACAGT 310 ACAGTTCTCCTTCTTAGCTTCGTGAGAACATCGACGCTCCAGCTGTTCAGGGACGGTGCGGTCA GGATACTCGTAGCAACGCAGGTTGGGGAGGAAGGACTGGACGTACCGGCTGCAGATACCGTCAT ATTCTACGAGCCGGTGGCAAGCGAGGTCCGCTCAATCCAGACACATCATGTAGTAGACGACCAA GACAGT 311 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCGCGGTCCTCTTTCCCAGCTTCAGTCCTTTGGCTT TCCTATGTTCTGCTGGTACTCTTCCCATTGCTCTCTCTGTTGTTTCTCCTGGCTTTTCCTGAAG TTTTCGAGGGCTTCGTCCATGCTGTCATAAGCGTTATGCGACACATCATGTAGTAGACGACCAA GACAGT 312 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCGAACTCCTTGACGCTCCTGGAGATGACCAGTTTC TCTATCTCCACCCTCCCGCTCTTCATGTCCGATATTATTTTCCTCGCCCTTCTCAGTGCCTCGT CCACGTTCCGGTCGAGCACGAGATTGAACATTTCCATGAGTCACATCATGTAGTAGACGACCAA GACAGT 313 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCCCTTTCCTCGAGGTCTTTGTCTATTCCCTTCACC GTTTCCCTGGCCCAAGCAGTGATGCTGGACCCGATACTGGGATCGGTAAACCTGTAGAAGCTTG ATGCAAACACTCCGTAGAACGAATTCATAAGCACCTTCACCCACATCATGTAGTAGACGACCAA GACAGT 314 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCAGTTCTCCTTCCACCCCGTCCGCCTCTTCTATGA CGGTCGCACTTTCCGTCGAGCAGACGCCCTACGGCTGGATGGATCAGTATCCCTCGTCGGTTGT TGCCCATGTCACGGGAGGCATCCCTCCCTACGCCTATCACTCACATCATGTAGTAGACGACCAA GACAGT 315 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCAATCTGGAGGCTTTCGCCGTATCCGTCGAAGTCA CGGACTCATCGGGGCATTCAGTCTCAGGTGCAATCATGATCAACTACGGTTCCATAGACCTCTC CCCGTTTGGATACATGGTAACTTTGATTTTTCCGGTGATCACACATCATGTAGTAGACGACCAA GACAGT 316 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGAACATCCATTGCTGACCATCACGATCGATGGCTC AAAGGACACGTTCAAAACTGGCGATGTCCTGGAATGGTTGACCGAAAGTGACATCTCAAACATG CACAATGTTGCGTCCTTCACAAAATCTCTCCTGAGGATAGTCACATCATGTAGTAGACGACCAA GACAGT 317 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGCATGCTGCCCGATGGCCAATGGTTCGGGGAGGTC ATTGGGAAGGACGTGCAGGGAAATCCCTACGGCATTGATTATACAATGTGGTTGCCGTTTAACA CCTACGTTAGGGATAAGCTCAGTTACAATAGTTGGGGGAAGCACATCATGTAGTAGACGACCAA GACAGT 318 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCCAGCATTCCTCCTCTGAGGGAGTTCGGAAGGTAA AATCTCCTGATGACATCTGAGTCCCTGGCGCCCATTGTCTTGGCTGAGTAGACTTCCATTTTAC TTGTCGTGCTGCCAGTTGAAAATGCAAGTACTGCTATCATCCACATCATGTAGTAGACGACCAA GACAGT 319 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGTTCCCTGCAGGAATGATTGTTCAACTGCACTCGT CAGTACATGATAGAACAATCTTGAAGCTGACAGATGGAGCGCATAGAAGATAAGGAATGCAAGA GGTACCAGTACTAGTACCACTGCATATATTCCTGCGTATAGCACATCATGTAGTAGACGACCAA GACAGT 320 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCGATTGCAGGAACGTGGAAAGTGTGCGGTTAATGT ATGACTCATTGCTCACATCGAATCGATACAGAAGACCGCTGTTTGCTGCCAGGTAGGAATCTAT GTTATTCAGGCCAACAACCACATCGTATCCCGCCCCCTTTGCACATCATGTAGTAGACGACCAA GACAGT 321 ACAGTTCTCCTTCTTAGCTTCGTGAGAACAGGATTGAACCGGTTGGTGTCAACGTAGAATCCTC ATTGACCCGCCACATAAAACTGAACATTCCAATTGTGTCGTCTCCTATGGATACGGTCTCTGAG GCAGATATGGCAATTGCACTAGCAAGACTCGGTGGTATTGGCACATCATGTAGTAGACGACCAA GACAGT 322 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCTTATTATACGCGACCTTTACACTGTAAGCCCGGA AACACCTGTTGACGATGCAATCCGTACTATGAGGGAGAAGCGAATCGCTGGGCTCCCAGTGATA TTGAACGGCAAACTTGTCGGAATACTTACGAACAGGGACATCACATCATGTAGTAGACGACCAA GACAGT 323 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGGTACGGCAAGATAGGCTCAGGGAAATTTGTACCA GAGGGAGTTGAAGGAGCAGTTCCGTACAAAGGTAAAGTTGCAGATGCAGTCTTTCAATTGATCG GGGGCCTGAAGTCGGGGATGGGGTATACTGGCTCGCCCACACACATCATGTAGTAGACGACCAA GACAGT 324 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGGTGGAAGCGTTGAGGAGTTTGTCACTCTATCGAG GAGAGTGGAGGCAGCGGGATTCGACAAGGTCGAGCTCAATTTGTCCTGCCCACACGTTCAGGGA GTTGGATCCGAGGTAGGACAGGATGTAGGTCTTGTAGAAGACACATCATGTAGTAGACGACCAA GACAGT 325 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGACACTTATAGACAGGCTAGACAAGAAGACGAAGA CAAGGATATTCTTCTCACTTGAGCGATTGATGAAGTGCGGCATAGGGATTTGTGACAGTTGCAG CATCAACGGCATCCGGGTATGCAAGGACGGAACAATTTTCGCACATCATGTAGTAGACGACCAA GACAGT 326 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCTTCGCAACTGCAAAGAGGTAGCTTCTGGATGCTT CCCTGGAACTATCCCTACATTGCTGTTATCTTACTAGTGGTACTGATTTATGCAGCAATAGAGG ACCTTAGGAAGAGGAAAATAACAACTATAACCTTCCTTGCACACATCATGTAGTAGACGACCAA GACAGT 327 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGTGACAGTTGGAACTGGTCTATCTCCCCGGTATTT TAATAAGTTTATAGGCGTAGCAAAGGCATATACGACAAGAGTAGGGGAGGGGATATTTCCTACT GAGATGTTTGGGGAAGAGGCAGATAGACTTAGAACCCTAGGCACATCATGTAGTAGACGACCAA GACAGT 328 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGAAGAAGACTTAAAGGATTTAGGTAGAGAGCTTAA GGTACCAAGAAGACCGTTCAAAAAGTTAACGCATAGAGAAGCTGTTNATATATTGAGATCTCAT GGCATAAAAGCAAGTTATGAACATGAGATACCTTGGGAAGCCACATCATGTAGTAGACGACCAA GACAGT 329 ACAGTTCTCCTTCTTAGCTTCGTGAGAACACGGGGAGGCTGTCTCAGGAGCTGAAAGAGAATAT AGAGCGGAGAAGGTTATTGAGAGGATGAGAGCTACTGGTGAGAACCCTGCAAAATACGGTTGGT ACATTGAAATGTTGAAATATGGTATTCCGCCGAGTGCAGGGCACATCATGTAGTAGACGACCAA GACAGT 330 ACAGTTCTCCTTCTTAGCTTCGTGAGAACATATGCAGATTTAGATGAGATTATAGGGGTTGCAT CTAAGGCAGGAATAGATTGCATAACTATAGATGGGTCAGAAGGTGGAACAGGTATGAGCCCTAT AGCTGCGATGAGAGAACTAGGATATCCAACGCTAGTATGTCCACATCATGTAGTAGACGACCAA GACAGT 331 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGGACACGAAATTGCTGAAGCAGCTGGCTCAACATG GTATATCGACAATTTCTGGGATAAACTCAAAGAGGGCTGTGTAGCATATCTAAACATAGATTCA CCTGGATTAAAAGATGCAACAAGATATATCGCTTACGCGTCCACATCATGTAGTAGACGACCAA GACAGT 332 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGTAACTTCTGGAAACGCCCAATCAAAACAGATCAT GACACCAAAGCTAAAATTATCTTCCCTAATAGCTTCTATAGGTGTATCTCCAGGTTGAAATATT AGCTTCTCTTTGGCAAATAAGTGAAGTTTCCTATACTTTCCCACATCATGTAGTAGACGACCAA GACAGT 333 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCCAGATAGCCCAATAGCATCAATTTCCGTTGCAAT AATAGGTACAGTACACAAAGAACACGTAATTTTCAGCGACACTGCAAATACAGGCGACTTAATA ATTTTTGCCATAGATCTCGATGGAACATTTCACCCTAAGTTCACATCATGTAGTAGACGACCAA GACAGT 334 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGTTCTAATTCCTCTCTTACAGCTTTAAAAGCAATC ACAGCAGATTCCAAAATATCATCCATATCATCCAGAGCTATAATAACACCTCTTGAAGTTTTCC CAATCTTATGCCCACTTCTTCCAACTCTTTGAACCAAACGACACATCATGTAGTAGACGACCAA GACAGT 335 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGTAACTTGTCTGGGAGACATATATTGGACAACTAA ATCAACGGTTCCTACATCAATCCCTAACTCCATAGATGATGTACAAATAAGACCTTTCAACTCA CCGTCTTTAAATAACCTTTCAACTTCTATACGAACATCTCTCACATCATGTAGTAGACGACCAA GACAGT 336 ACAGTTCTCCTTCTTAGCTTCGTGAGAACACTCAATGAACCATGATGCACATCAATACTTAGAT TAGGATCGTATAAGTGAAGCCTAGAAGCTAGTATCTCAGCTATTTCACGAGTGTTTACAAAAGT AAGCATAGAGCGGCTCTTTTCTAATAACTCAACCAATACCCCACATCATGTAGTAGACGACCAA GACAGT 337 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCAGTTAAATCATCTTAACTCACAAATATTAAGGCT TTAATTTCTGAGGGAGTGCAAAATGAAAACTGACGTAGTAATAGTAGGTGCAGGGCCCGCAGGC ATGTTTGCTGCACATGAATTGGCAACTAAATCTAATCTGAACACATCATGTAGTAGACGACCAA GACAGT 338 ACAGTTCTCCTTCTTAGCTTCGTGAGAACAAAAATAGCCAAGGATCCAAAATTCCGTGTATATA CAAAAACCTTCGATGACCTTACACGTGTATTTTGCGTTAATTATCGAGGCTTCGTCGTCCAAGA AGTCTACGGAGATATCGTTGGTGTTAACGGCCACACTCTAACACATCATGTAGTAGACGACCAA GACAGT 339 ACAGTTCTCCTTCTTAGCTTCGTGAGAACTCAAACAAAAATCTGAAAATGCCAATTTTGCATTT CTAGTTCGAGTTGAACTCACCGAACCGCTTGAAGACACAACCGCCTACGGATTCTCAATAGCCA AATTAGCAACTACCATAGGTGGAGGAAAACCAATTCTTCAACACATCATGTAGTAGACGACCAA GACAGT 340 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCGAGATACTGAATTTCCAAAACTCAAAGGATATAG AATTGTTAGAATCGCAACACATCCGCAAGTTATGAGCATGGGACTAGGAAGTGAAGGGTTGTCA AAACTTTGCCAAGAAGCCGAAAAGAGAGGACTAGATTGGGTCACATCATGTAGTAGACGACCAA GACAGT 341 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCGAAGTTTTTATCCTCCTCGGTCCAAGTCACACTG GTTACCCAGGCGTTGGAATAATGACAGAAGGCATCTGGAAAACTTCTTTAGGAGAAATATCAAT AGATGAAACTCTCTCGAATACTATTTTAAATAATTGTGACCCACATCATGTAGTAGACGACCAA GACAGT 342 ACAGTTCTCCTTCTTAGCTTCGTGAGAACTGACACACTACGGCACCTACTATGGATACACACCA GCTGGTGTTGAACCATTAACCAAAGTTTTAGAATGGATATACCAGACGGACAAACAAGTTATTG AGAGAATTAAAAGATTAGATGGAGCAGGAGTAATAGAATATCACATCATGTAGTAGACGACCAA GACAGT 343 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCTGAAAAGTTCATTCCAATTGTTAAATCGCCATCT TGGAAACACGGCACAAGAAAAGGGAAAGGATTTAGCATCGGTGAGATTAAAGCAGCCGAGATAG ATATTAGTATGGCAGTTAAACTCGGTATACCCATTGATAAACACATCATGTAGTAGACGACCAA GACAGT 344 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGGGAATAATAATTAAAATAATGTGGCACACCTTTT AGCTTCTTTTCATCTCATATTTTCAAAGAAGCCTTCCAGGTGTGCCTCATCGGTGTCCCCCGCT GCGGAGACACGGTATCATCGTATCCGCCGAAGGAAACTCAACACATCATGTAGTAGACGACCAA GACAGT 345 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGACATTGCCTATCAATTACTTCAAGCCGGAATGCA AGTTCCCGGTTTCAGAAGGTCGCCAAAGATAATAGAAAGAATTTTAGAAAGATATATTCCAACA GTCACCGTACTAGGCGGCATTATTGTAGGATTAATAGCTGCCACATCATGTAGTAGACGACCAA GACAGT 346 ACAGTTCTCCTTCTTAGCTTCGTGAGAACTGTCGTTCAGGGAGGTATAAAAATGCCAGAACCAC GCTACCGGTCAAGGTCTTTAAGAAGACGATACGTACACACACCTGGAGGAAAAACCGTCATCCA TTACAGGAGAAAAAAACCTGACGTTGCAAAATGCGCATTATCACATCATGTAGTAGACGACCAA GACAGT 347 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGTGGTCAACCTCTCAGAGGAATTCCCAGACTAAGG CCAGGAGAATTCAGAAAGTTGACAAAAAGTCAACGAAGACCAGAGAGACCTTTCGGTGGATATC TATGCCACAAATGCTTAGCAATGGAAATCAAGAAAGCTGTTCACATCATGTAGTAGACGACCAA GACAGT 348 ACAGTTCTCCTTCTTAGCTTCGTGAGAACATAGGATGAATCTAACTGGGGCGACCCGGTAGATA ACTGAGAGTGTAGGAGGTGAAATAATTGAGCGCAATAGAAGTAGGTAGAATATGTGTTAAAACT AGTGGAAGAGAAGCAGGAAGAAAGTGCGTTATTGTTGAAATCACATCATGTAGTAGACGACCAA GACAGT 349 ACAGTTCTCCTTCTTAGCTTCGTGAGAACACACCATTTCCTAATATTTTAGTAACTAGATATGT TTGTTATAGTATTAGGGTGAAGTATTTGTATGAAAGAAAGTTGCCATCAGACATTAAAAGAGAG ATTCTAGTAAAAAGTGAAGCAGAAACTGACCCTGCTTATGGCACATCATGTAGTAGACGACCAA GACAGT 350 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCACATGAGAGAACTTAGAAGAACACGTACAGGACC CTTTAAAGAAGATGAAACCCTAGTAACTCTTCACGATGTAGTTGATGCTTACTATTTTTGGAAG GAAGATGGAGAAGAAGAATTTCTACGAAAAGTCATACAACCCACATCATGTAGTAGACGACCAA GACAGT 351 ACAGTTCTCCTTCTTAGCTTCGTGAGAACAATGGAAAAGGGTTTAGAACACCTACCTCACATTT GGATTAGAGATTCTGCTGTAGATGCAATATGCCATGGGGCAAACTTAGCAGCTCCTGGTGTTGT AAAACTTCATGACGGTATATCACCTGGAGACTTAATAGTAACACATCATGTAGTAGACGACCAA GACAGT 352 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCGCTGATCATACATGTGCATTGTCTTTAAATACAC TAGTAACGTTAATAATATCTAGCAATTTTAGATAAAAATAACTAGCAGTGCCGGGGTAGCCAAG TGGACTACAGGCCTTATACCGGTTAGGGCGCGGGCCTGGAGCACATCATGTAGTAGACGACCAA GACAGT 353 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCATGCCTTAACGAGAGGCATGGGATGGGGGAGCTG TGAGCCCCCCGAACCGGCAGATGAGGGGAAGGGTGCAAAGCATCCCTTAACGCCGGAAGCTCCC GACTTCAGTCGTGGAGCAGCTCACTGCTTTGACGAAAGGTTCACATCATGTAGTAGACGACCAA GACAGT 354 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGAACTTGCAAGGAAGGCCGGTGTTGATTATGAGAC AAAGCTGTTGGTCAGGGGCAAGGAACCGGCTGAGGACATAATAGAATTTGCTGACGAGATCAGG GCAAGTCTCATTGTAATAGGGGTTAGGAAGAGGAGACCCGCCACATCATGTAGTAGACGACCAA GACAGT 355 ACAGTTCTCCTTCTTAGCTTCGTGAGAACTCCAGAAGAGATTCAAAGCTCTCGTATTCAATGTC CCCACCAAATTTCTGGTCGCGCTCAATTTTGACTTTACCAAAAGCGGGGAAAACGTAGTGCTTT GCTAGGTCTATTATCGGATTTCCTTCTACAACCTTTGGCGGCACATCATGTAGTAGACGACCAA GACAGT 356 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGATTTGCTCATTTTCTCCCCGTCGAGTCCTGAGAT TATCGGCGTATGGATGCAGATCGGTGCCTTGTAACCGAGGGCCGGCAGATTCTCCCTTGCGAGC ATGTGGATCTTTCTCTGATCTATTCCACCAACCGCCACATCCACATCATGTAGTAGACGACCAA GACAGT 357 ACAGTTCTCCTTCTTAGCTTCGTGAGAACTCCGGGAGTTGCAGAACCAAGCATGGAAATTGCTA GAGATCCCGAAAAGGTTTACGAGTACACGAATAAGTGGAACACGGTTGCAATTATCACTGATGG CTCGAGGGTCTTGGGACTGGGCAACATCGGTGCGATGGCTTCACATCATGTAGTAGACGACCAA GACAGT 358 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGTGGTGTTATCAAGAGGGAATATATTGCTCAGATG GCAGAGGATCCGATAGTCTTTGCCTTATCAAACCCGGTGCCTGAGATCTATCCGCAGGAGGCAA AGGAAGCCGGAGCCAGGATCGTAGGAACTGGTAGGAGCGACCACATCATGTAGTAGACGACCAA GACAGT 359 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGGGATCTGTTAGTATGGCATTCAGAGCCTTTATGT CCTCATCGGTAAGCTTGTCCGATGGCAGATCGTATTTCACGATGTCTGAAGGAGTAACTCCGAG AAACTTCGCTTCTGGTGTCGCAAGATACTCCGAGAGATGCGCACATCATGTAGTAGACGACCAA GACAGT 360 ACAGTTCTCCTTCTTAGCTTCGTGAGAACTGAGTGCGGCTTACTCTGCACTGTGCGAGATCGAT GAGGTCGTTGTTGTTGCCCCCATAACGCAGATGAGCGGAGTGGGGAGGAGCATATCCATAATGC GGCCGGTTCGTTTTTTCGAGCTCGAAATAGATGGCATGAGGCACATCATGTAGTAGACGACCAA GACAGT 361 ACAGTTCTCCTTCTTAGCTTCGTGAGAACAGGGGAAGGGAGTACTACTGGATTCATGGGGTGGA AGTCGAAAGCGCTGAGCCTGGAACGGACATACACGCACTCAGAAACGGGTATGTCTCCATTACA CCGATATCCTTAAATGCAACTTCGGACTGCGAAGCTTTAAGCACATCATGTAGTAGACGACCAA GACAGT 362 ACAGTTCTCCTTCTTAGCTTCGTGAGAACATAGTTTTATGGAGGGTGGTTGGACATGAATGAAA GGGCAAAGAAGGTCATTCTTATTGTGGATGACGATTTGGCTCTGCTTGAAGCTCTTGAACTGAT GCTTCGAGGCAAGTATGAGGTTGTGAAGGTGACAAATGGGACACATCATGTAGTAGACGACCAA GACAGT 363 ACAGTTCTCCTTCTTAGCTTCGTGAGAACATGTCGATTCCGAAATAGCAGGGAGCAATTATCGG TGGGCTTCCGACCCTTAAATGGATTTCCTTCGCTCCCGCCTTTCTTATCATGTCGACTATTCTT TTGGATGTTGTTGCCCGCACAATGCTGTCGTCAACCAGCACCACATCATGTAGTAGACGACCAA GACAGT 364 ACAGTTCTCCTTCTTAGCTTCGTGAGAACACTTTCTGAGGGAAAAACATTGTTGCTTATCCTAA AGAGTTTACAAGCAAGAAGCTGGAAACAAACTCTGGATGTTATTAATTTAGAGCCTGCAGCAGC ATATACAATGTTTAGAGCGGCAATAAAGAAACTATACAAAGCACATCATGTAGTAGACGACCAA GACAGT 365 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGTGGTTGAGAGGCTGCTTGAAGGCATTGCAAAGAA TGAAAGGGTAGCTTACGGATTGGAGGAGGTTAGGAGGGCAAAAGAGTATGGAGCAATTGAGGTT CTGTTGGTTTCAGATGACTTCCTGCTCACCGAGCGTGAGAACACATCATGTAGTAGACGACCAA GACAGT 366 ACAGTTCTCCTTCTTAGCTTCGTGAGAACTCGCTTCGAGATTCCTGATAGGAGTGGGAGTTGCC GGGGTTTACGTGCCTACGATAAAAATAATATCCGTCTGGTTCAGGCAGAATGAGTTTGCAACTG CTACTGGGATTCTTTTCGCGATTGGAAATCTAGGAGCGATTCACATCATGTAGTAGACGACCAA GACAGT 367 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGAGGTATCGCCTACTTAGAGAGTTCGTAAAGTCGG AGATATTGGAGGAAGTTAAATTTGAAAACGTTGTGGACGAGTACTGGGTTGCGGAACCATTCAT AAAGATCATAATTTTTGAGGATCTCGAAAACCAGAAATTGACACATCATGTAGTAGACGACCAA GACAGT 368 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCTAATCCGATTATCGATTCTACGCTTCCTGATGGT AGCAGGCTTCAGGCTACCCTAGGAACAGAAATTACACCTAGAGGCTCGAGCTTCACGGTGAGAA AATTTACAACCCAGCCACTGACCCCGTTAGATCTAGTGAGGCACATCATGTAGTAGACGACCAA GACAGT 369 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCAAAATTATATCGATAGAGGATACCAGAGAGATAA AGCTCCATCATGAGAACTGGCTGGCTCAGGTGACGAGAACGGGGATAGGAGAGCAGGAAATTGA CATGTATGACCTTCTCAAAGCCGCCTTGAGACAGAGACCGGCACATCATGTAGTAGACGACCAA GACAGT 370 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGAATCAGTTTGTTAAATGGGATGCGAAGAAAAATT CGCATGTTGAGGTAGGGATTCCGAAAAAGCTAGAGAAAATCGCGATGTCGAGAGTGGACGATGC TTACGCGGAGCTGGAAAGAAGAAGGAGGTATTTGGAGTGGACACATCATGTAGTAGACGACCAA GACAGT 371 ACAGTTCTCCTTCTTAGCTTCGTGAGAACTCAGTGAAGTTAGCACGGAATTCGAAAGGATAGTG GTTCTCGTTGAAATGGGAGAGGATTTGGAAAGCGCAATGAGGTTTGTTGCAGAAACAACTCCCT CAGAGAGGCTCAGGGTTTTTCTGGAGAACTTTATTGATGTGCACATCATGTAGTAGACGACCAA GACAGT 372 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGCTGGAGCGGGAGGCGTATCAACGCTTGCCCTCAA TCCGTTACCCGAAGTTCCAGAATACTTTGAGTATTTCCAGTCCGAATAGAAGCAGAGCACCTCT CGATCGACTAGAGTCTTTCTGCTAGCTCTTGCACCCTCATCCACATCATGTAGTAGACGACCAA GACAGT 373 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGCGGAAATCTCTGCTGAAAACACCTTGACTTTTTC TTCGTATATCTCCCATTCCATCAGGCACCACCAACTTTGGTCCTGCAAAGAGTCATCGGTGCCC CATCTGCTACGGGAACGATCTGAAAGGCTTTACCACAGAATCACATCATGTAGTAGACGACCAA GACAGT 374 ACAGTTCTCCTTCTTAGCTTCGTGAGAACTCCGGTTGCAGGATTGGTCTCCCCACCTCTCGAGC CTATGAGGAATACCCCATTCCTGCAGAGCTCGAGAAGCTCTTCGAATTCAAGATCCCCCTTCTG CAGGAATGTGTTGCTCATTCTGACAATCGGAAAAGCAACTCCACATCATGTAGTAGACGACCAA GACAGT 375 ACAGTTCTCCTTCTTAGCTTCGTGAGAACAACTCCTCGATTGTTGGGTCATCGATTATTGTCAC GTTCTCTCCTGCAATTCTCTCTCCAATCTTTCCAGCAAGAACGCTGTTTTCCTGCAGAACGTGA TCTGCCTCGACCGCATGCCCGAAAGCTTCGTGAATAAAAACCACATCATGTAGTAGACGACCAA GACAGT 376 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCTTTCTTCAAGAATGCTTTCTGCGGCGATAAGCCC AGTAACAGCCGCTCCAACTATTCCCCTGCTTATTCCGGCTCCATCGCCAATTGCATAGATGTAC GGTATGCTTGTCCTCATCTTCTCGTCAACCTTAAGCTTCAACACATCATGTAGTAGACGACCAA GACAGT 377 ACAGTTCTCCTTCTTAGCTTCGTGAGAACTGCTGGATTTTTCTTTGGCCTTGCTGTGGCCGTTG ACTAGACAAAAGTCGCCGTACTCCTCTCTTATAACCCAGCCCCTCGGGCAGGTGCAGAACGTGC GCATGTAGTCGTCATGCCTCTGTGTGATTATTCTCAGCTTTCACATCATGTAGTAGACGACCAA GACAGT 378 ACAGTTCTCCTTCTTAGCTTCGTGAGAACTTGCCTTGGAATTTTCCGCCACTTCGATCTTATAC TTTTTTACCCATTTTTCCAGCCAGTCGGCACCGCTCCTCCCAACTGCAATTATGAGTTTGTCGT AGCCGAACTTGTCCCCATCGTTCGTCTTCACGATCTTTTCTCACATCATGTAGTAGACGACCAA GACAGT 379 ACAGTTCTCCTTCTTAGCTTCGTGAGAACAATCACCACCGACTGTGAAGCTCGAAGGATAGTTG GGGTTGGCATAATTCAGCTTTCCATCCGAAAGTCCTCCAGCACCACCCACACCAGAAGTAATGT TGCAGGGATCGCATTTCTTGCAATAGCTTTGCGAAAGGTCACACATCATGTAGTAGACGACCAA GACAGT 380 ACAGTTCTCCTTCTTAGCTTCGTGAGAACTTAACCAACCTCTTTCGCATCAAAATCCCAACTGC GGCATCCGTTATCAGCGTTACATCGATTCCATCTTTCATAAGCTCGTAGCAGGTGAGCCTAGAG CCTTGGTTCAGCGGCCTCGTTTCGCAGGCGAAAACCTTTACCACATCATGTAGTAGACGACCAA GACAGT 381 ACAGTTCTCCTTCTTAGCTTCGTGAGAACTTATCGAGTTAATAGCTATCAGTGTTGCTATTACG ATCGTTGCGATCCCATCAAAGATGTTATGACCGAAGGAGATAGCAATAATGCCAAATATCGCTG CAAGCGTCGATAAGGAGTCGTTAAAACTCTCAAACATCACTCACATCATGTAGTAGACGACCAA GACAGT 382 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCTCCCTTCTAAGCTTCGTGATATCTGCATTGCCAA TATCAACTAGAAATTCGATTGAGATAAGCTTGTCTCTTGCGGTTAAGCTTGTTCTCTCGATATT TATACCGAAATTTAGCAATACACCCGTGATATCTCTCACGACACATCATGTAGTAGACGACCAA GACAGT 383 ACAGTTCTCCTTCTTAGCTTCGTGAGAACAAGCGGGGCTTTTGCCTTTCCAATTCCGCCGCAAC CAACCGTTGGACTTATCAAACCGGAACCTTTCAACTCCGAGATTAAAGAGCCTGGCTCCTTATC GTGCTTAATAGCAATTTCTACAATGTCTTCCCCGCATACAACACATCATGTAGTAGACGACCAA GACAGT 384 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCTAGTTCTTGGTTTTCGTCGACGTTGACCTTGTAG AACTCTACATCTGGAAACTCCTTTGAAAGCTTTTCGAGCACTGGGCTGAGATACCTGCACGGCA TGCACCAGTCGGCGTAGAAGTCAACAACAACAAGCTTATCCCACATCATGTAGTAGACGACCAA GACAGT 385 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCTCCGATCGTCTTTAAAGCTTGCAAGTCTAAATCC TCGCCCCAGGGAATTTCCTGGGATTTTCTCGCAATCTCTATCGCCGAAGTATAGGTTATCCTCG GGAATGGTATCTCGGGGACTTCGAGCTTTAGTTCGAGAATACACATCATGTAGTAGACGACCAA GACAGT 386 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGAGGTTTTGTCCCTATTGGGTTTCTCATTGCCTGC AGCATTTCTTCTCTGCTCAGAGCTCTGCAGCCATCGCCTTTCATTCTTAAAATGCTAACCTCCC AATCATCCGGAAAATCGAGCTCTATTTCCCTATCCTGCCAGCACATCATGTAGTAGACGACCAA GACAGT 387 ACAGTTCTCCTTCTTAGCTTCGTGAGAACTGTTTACAGGCTGGTGGGTGGGGAAAGGAGTGTTA AGGGCAAAAGGAGTGTAAGCAAGTTCAGGGTTGCGATTGCGATTCTTCTGGCATTCATTCTGAT ATATCCTACATACCGCATAGCCGAGATTCAAAGCAGTGGGGCACATCATGTAGTAGACGACCAA GACAGT 388 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCAGGGTCAGGAGGATTCACGAGATAGAAGTCCTCG AGGTGAGAGGCAGGTTCGCGCTTATAAGGGTTCTCAGCGACCCCGGCACGTACATGAGGAAGCT GGCCCACGACATCGGGCTATTGCTCGGAGTAGGTGCACACACACATCATGTAGTAGACGACCAA GACAGT 389 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGAAGTCAGTCATAGAATCAATGGTGTATTCTTCAT CAGGGTTATTATACGGAATGAACTTATAGTTCTCACCTGCTACCTGATCCACTGTCATTTCTGC AAGAGTCTGCACTGTGGTAATTCCACCTTCTTCCATCCGGGCACATCATGTAGTAGACGACCAA GACAGT 390 ACAGTTCTCCTTCTTAGCTTCGTGAGAACAGTAAGGGAATCAATGTCTTCCATTGCTGTAAGGG TTACTGTTACCTTTGTAGAAGTCAGACCGTAATTGGTCAGCAGCTCTTCATAGAAGTTCTGGTT TGCAATATCCCTCTGGGCAATGACAGGGTAGTCGACTTCGTCACATCATGTAGTAGACGACCAA GACAGT Forward and Reverse Primers 391 TCTCCTTCTTAGCTTCGTGAGAAC 392 CTTGGTCGTCTACTACATGATGTG

Primer Binding Sequences

[0069] The 5′ and 3′ primer binding sequences are selected to be complementary to a SDSI 5′ and 3′ primer which is included in an amplification reaction and used to amplify SDSIs present in a given sample. The primer binding sites may be optimized for multiplex amplification with a set of primers used to amplify a genome for sequencing. In one example embodiments, the 5′ and 3′ primer binding sites have a Tm of between 55-65° C. In one example embodiment, the 5′ and 3′ primer binding site are complementary to primers having SEQ ID NOS: 391 and 392.

Methods of Detecting and Preventing Sample Contamination

[0070] In one example embodiment, a method of detecting and preventing contamination in one or more amplification reactions comprises adding a SDSI according to the example embodiments disclosed above to a one or more samples to be assayed. An amplification reaction is then used to amplify a target sequence in the samples. The amplification reaction will include probes and primers needed to amplify the target sequence and to amplify the SDSI. The amplicons generated from the amplification step are then used the one or more samples, sequencing the amplified samples and determining the number of reads of the SDSI from the one or more samples, wherein detection of only a single SDSI in the sample indicates contamination free amplification of the same, and wherein detection of multiple SDSI's indicates possible contamination of the sample. Samples identified as potentially contaminated may then be discarded or marked for repeat to confirm accuracy of results.

Amplification

[0071] The present invention solves this problem by providing for the sequencing of spike-DNA sequences at concentrations that can be amplified concurrently with the nucleic acids of interest. In one example embodiment, sequencing includes extracting total RNA or DNA from a biological sample, such as a sample collected with a swab (e.g., nasal, rectal, vaginal). Methods of extracting total RNA or DNA are known in the art and commercial kits are available. The presence of a pathogen may be confirmed in a sample. Exemplary methods for confirming include PCR, RT-PCR and RT-qPCR. In certain embodiments, sequencing includes DNase treatment to remove residual DNA. In certain example embodiments, sequencing may include depletion of ribosomal RNA (rRNA). In certain example embodiments, cDNA may be prepared from total RNA using RT-PCR. In certain example embodiments, RT-PCR may be performed using random hexamer priming. In one example embodiments, a SDSI is added to each cDNA sample. The SDSI can be added to the total cDNA sample. In certain example embodiments, cDNA samples may be normalized to a constant amplification level. In certain example embodiments, real time PCR may be performed on the cDNA using one or more standard primers and a Ct value is used to normalize cDNA samples. As used herein, standard primers refer to a primer set that is used for every sample. In certain embodiments, the standard primers are directed to a region of the pathogen to be sequenced. The samples can be diluted such that all of the samples for amplification have the same Ct value in the amplification reaction. In certain embodiments, each sample is normalized to a Ct value less than 35, 34, 33, 32, 30, 29, 28, 27, 26, 25, or 24. In preferred embodiments, the samples are normalized to a Ct value of 26 to 28, preferably 27. In one example embodiment, a SDSI is added to the normalized sample used for PCR amplification of the pathogen. The cDNA may be amplified in the same reaction with pathogen specific primers and primers specific to the SDSI. Amplification may be performed in a multi-well plate (e.g., a standard PCR plate).

[0072] In certain example embodiments, the primer concentration is 100 μM. In certain example embodiments, the primer concentration is between 50 μM-150 or between 50 μM-200 μM, or between 50 μM-250 μM, or between 50 μm-250 μM or between 50 μm-300 μM or between 50 μm-350 μM or between 50 μm-400 μM or between 50 μm-450 μM or between 50 μm-500 μM. In certain example embodiments, the primer concentrations is between 50 μm-70 μM or between 70 μm-90 μM or between 90 μm-110 μM or between 110 μm-130 μM or between 130 μm-150 μM or between 150 μm-170 μM or between 170 μm-190 μM or between 190 μm-210 μM or between 210 μm-230 μM or between 230 μm-250 μM or between 250 μm-270 μM or between 270 μm-290 μM or between 290 μM-310 μM or between 310 μM-330 μM or between 330 μm-350 μM or between 350 μm-370 μM or between 370 μm-390 μM or between 390 μm-410 μM or between 410 μm-430 μM or between 430 μm-450 μM or between 450 μm-470 μM or between 470 μm-490 μM. In certain example embodiments, the primer concentration is between 50 μm-100 μM, M or between 100 μm-150 μM or between 150 μm-200 μM or between 200 μm-250 μM or between 250 μm-300 μM or between 300 μm-350 μM or between 350 μm-400 μM or between 400 μm-450 μM or between 450 μm-500 μM.

[0073] In certain example embodiments, a spike-in may be relatively the same length as the amplicons generated for the target organism. In one example embodiment, spike-ins are the same size and share the same priming region to ensure similar amplification performance. In certain embodiments, a spike-in for MNase-seq, ChIP-seq, and genomic DNA are around 150 nucleotides in length. In one example embodiment, a spike-in accounts for 0.1%-3.5% reads. A spike-in to total sample ratio may be from 1,000:1 to 50:1. In one example embodiment, a spike-in includes primer binding sites on the 3′ end and/or the 5′ end. (Chen K., et al., The overlooked fact: fundamental need for spike-in control for virtually all genome-wide analyses. Mol Cell Biol (2016) 36:662-667) The primers and primer binding sites on the SDSI may range between 15-40 nucleotides in length. The primer's melting temperature (T.sub.m) may range from 40° C.-95° C., preferably between 55-65° C.

Sequencing

[0074] After amplification of cDNA, standard sequence library generation can be performed. In certain embodiments, sequencing comprises high-throughput (formerly “next-generation”) technologies to generate sequencing reads. In DNA sequencing, a read is an inferred sequence of base pairs (or base pair probabilities) corresponding to all or part of a single DNA fragment. A typical sequencing experiment involves fragmentation of the genome into millions of molecules or generating complementary DNA (cDNA) fragments, which are size-selected and ligated to adapters. The set of fragments is referred to as a sequencing library, which is sequenced to produce a set of reads. Methods for constructing sequencing libraries are known in the art (see, e.g., Head et al., Library construction for next-generation sequencing: Overviews and challenges. Biotechniques. 2014; 56(2): 61-77). A “library” or “fragment library” may be a collection of nucleic acid molecules derived from one or more nucleic acid samples, in which fragments of nucleic acid have been modified, generally by incorporating terminal adapter sequences comprising one or more primer binding sites and identifiable sequence tags. In certain embodiments, the library members (e.g., genomic DNA, cDNA) may include sequencing adaptors that are compatible with use in, e.g., Illumina's reversible terminator method, long read nanopore sequencing, Roche's pyrosequencing method (454), Life Technologies' sequencing by ligation (the SOLiD platform) or Life Technologies' Ion Torrent platform. Examples of such methods are described in the following references: Margulies et al (Nature 2005 437: 376-80); Schneider and Dekker (Nat Biotechnol. 2012 Apr. 10; 30(4):326-8); Ronaghi et al. (Analytical Biochemistry 1996 242: 84-9); Shendure et al. (Science 2005 309: 1728-32); Imelfort et al. (Brief Bioinform. 2009 10:609-18); Fox et al. (Methods Mol. Biol. 2009; 553:79-108); Appleby et al. (Methods Mol. Biol. 2009; 513:19-39); and Morozova et al. (Genomics. 2008 92:255-64), which are incorporated by reference for the general descriptions of the methods and the particular steps of the methods, including all starting products, reagents, and final products for each of the steps.

[0075] In one example embodiment, any suitable RNA or DNA amplification technique may be used to amplify a sample and SDSI. In one example embodiment, the RNA or DNA amplification is an isothermal amplification. The isothermal amplification may be nucleic-acid sequenced-based amplification (NASBA), recombinase polymerase amplification (RPA), loop-mediated isothermal amplification (LAMP), strand displacement amplification (SDA), helicase-dependent amplification (HDA), or nicking enzyme amplification reaction (NEAR). In certain example embodiments, non-isothermal amplification methods may be used which include, but are not limited to, PCR, multiple displacement amplification (MDA), rolling circle amplification (RCA), ligase chain reaction (LCR), or ramification amplification method (RAM).

Example Applications

[0076] In one example embodiment, the present invention is used to improve any method of sequencing wherein the nucleic acids to be sequenced are amplified (i.e., amplicon-based methods). In certain example embodiments, the amplification method preferentially amplifies a contaminant nucleic acid if it is present in a sample. In preferred embodiments, samples comprising a pathogen of interest are sequenced. In more preferred embodiments, the pathogen of interest includes variants that can be clustered into families or a lineage. As used herein, the term “variant” refers to any virus having one or more mutations as compared to a known virus. A strain is a genetic variant or subtype of a virus. The terms ‘strain’, ‘variant’, and ‘isolate’ may be used interchangeably. In certain embodiments, a variant has developed a “specific group of mutations” that causes the variant to behave differently than that of the strain it originated from. In certain example embodiments, the families of variants are important for tracking and responding to epidemics and pandemics. For example, sequencing can be used to determine variants that are emerging as the dominant variants causing disease or are spreading more quickly. In another example, sequencing variants can be used to track community transmission and superspreading events (see e.g., Lemieux et al., 2020). Variants may also include those that are resistant to a specific treatment, such as drug resistance. In certain embodiments, variants are associated with more severe disease. As used herein, the term “epidemic” refers to the rapid spread of disease to a large number of people in a given population within a short period of time or the occurrence of more cases of disease, injury, or other health condition than expected in a given area or among a specific group of persons during a particular period. For example, in meningococcal infections, an attack rate in excess of 15 cases per 100,000 people for two consecutive weeks is considered an epidemic. Epidemics of infectious disease are generally caused by several factors including a change in the ecology of the host population (e.g., increased stress or increase in the density of a vector species), a genetic change in the pathogen reservoir or the introduction of an emerging pathogen to a host population (by movement of pathogen or host). Generally, an epidemic occurs when host immunity to either an established pathogen or newly emerging novel pathogen is suddenly reduced below that found in the endemic equilibrium and the transmission threshold is exceeded. An epidemic may be restricted to one location; however, if it spreads to other countries or continents and affects a substantial number of people, it may be termed a pandemic. Effective preparations for a response to a pandemic are multi-layered. The first layer is a disease surveillance system, which includes sequencing of all variants in a population. In certain embodiments, sequencing contaminants that were amplified from a sample would provide an incorrect identification and clustering of the variants.

[0077] Any method of sequencing variants in pathogens, such as viral pathogens, is applicable to the present invention (see e.g., Lemieux et al., 2020). Current sequencing methods all suffer from the risk of contamination and the user would be blind to whether the results were accurate.

[0078] In certain example embodiments, a pathogen with a DNA genome is sequenced. Sequencing may include whole genome sequencing. Whole genome sequencing (also known as WGS, full genome sequencing, complete genome sequencing, or entire genome sequencing) is the process of determining the complete DNA sequence of an organism's genome at a single time. This entails sequencing all of an organism's chromosomal DNA as well as DNA contained in the mitochondria and, for plants, in the chloroplast. “Whole genome amplification” (“WGA”) refers to any amplification method that aims to produce an amplification product that is representative of the genome from which it was amplified. In certain embodiments, the SDSIs of the present invention are added at the amplification step. Non-limiting WGA methods include Primer extension PCR (PEP) and improved PEP (I-PEP), Degenerated oligonucleotide primed PCR (DOP-PCR), Ligation-mediated PCR (LMP), T7-based linear amplification of DNA (TLAD), and Multiple displacement amplification (MDA).

[0079] In certain example embodiments, the present invention includes whole exome sequencing. Exome sequencing, also known as whole exome sequencing (WES), is a genomic technique for sequencing all of the protein-coding genes in a genome (known as the exome) (see, e.g., Ng et al., 2009, Nature volume 461, pages 272-276). It consists of two steps: the first step is to select only the subset of DNA that encodes proteins. These regions are known as exons—humans have about 180,000 exons, constituting about 1% of the human genome, or approximately 30 million base pairs. The second step is to sequence the exonic DNA using any high-throughput DNA sequencing technology. In certain embodiments, whole exome sequencing is used to determine germline mutations in genes associated with disease.

[0080] In certain example embodiments, targeted sequencing is used in the present invention (see, e.g., Mantere et al., PLoS Genet 12 e1005816 2016; and Carneiro et al. BMC Genomics, 2012 13:375). Targeted gene sequencing panels are useful tools for analyzing specific mutations in a given sample. Focused panels contain a select set of genes or gene regions that have known or suspected associations with the disease or phenotype under study. In certain embodiments, targeted sequencing is used to detect mutations associated with a disease in a subject in need thereof. Targeted sequencing can increase the cost-effectiveness of variant discovery and detection. In certain embodiments, targeted sequencing includes amplification and the SDSIs of the present invention are added at the amplification step.

[0081] In one example embodiment, the mitochondrial genome from more than one sample is sequenced. In certain embodiments, mitochondrial genome sequencing includes amplification and the SDSIs of the present invention are added at or before the amplification step. An exemplary method includes MitoRCA-seq (see e.g., Ni et al., MitoRCA-seq reveals unbalanced cytocine to thymine transition in Polg mutant mice. Sci Rep. 2015 Jul. 27; 5:12049. doi: 10.1038/srep12049). The method employs rolling circle amplification, which enriches the full-length circular mtDNA by either custom mtDNA-specific primers or a commercial kit and minimizes the contamination of nuclear encoded mitochondrial DNA (Numts). In certain embodiments, RCA-seq is used to detect low-frequency mtDNA point mutations starting with as little as 1 ng of total DNA.

[0082] In another example embodiment, multiple displacement amplification (MDA) is used to generate a sequencing library. Multiple displacement amplification (MDA, is a non-PCR-based isothermal method based on the annealing of random hexamers to denatured DNA, followed by strand-displacement synthesis at constant temperature (Blanco et al. J. Biol. Chem. 1989, 264, 8935-8940). It has been applied to samples with small quantities of genomic DNA, leading to the synthesis of high molecular weight DNA with limited sequence representation bias (Lizardi et al. Nature Genetics 1998, 19, 225-232; Dean et al., Proc. Natl. Acad. Sci. U.S.A. 2002, 99, 5261-5266). As DNA is synthesized by strand displacement, a gradually increasing number of priming events occur, forming a network of hyper-branched DNA structures. The reaction can be catalyzed by enzymes such as the Phi29 DNA polymerase or the large fragment of the Bst DNA polymerase. The Phi29 DNA polymerase possesses a proofreading activity resulting in error rates 100 times lower than Taq polymerase (Lasken et al. Trends Biotech. 2003, 21, 531-535). In certain embodiments, the SDSIs of the present invention are added to samples and amplified during MDA or in a subsequent amplification step.

[0083] In one example embodiment, is sequencing comprises sequencing of SARS-CoV-2 variants. The scale of the SARS-CoV-2 pandemic has led to a particular focus on reducing the cost and time of amplicon-based methods, often at the cost of slightly reduced sensitivity. However, viral loads of SARS-CoV-2 can vary widely between individuals, in particular when samples are caught early in infection or follow-up sampling is needed. An open-access tiled primer set developed by the ARTIC network is the most widely used method for SARS-CoV-2 specific genome amplification followed by sequencing on either Illumina or nanopore instruments (Quick et al., 2017; Tyson et al., 2020). A wide array of protocols and publications are now available that integrate these ARTIC primers with different amplification and library construction indexing strategies (Baker et al., 2020; Gohl et al., 2020). Approaches such as batching samples by viral load to increase sensitivity are impractical to scale to current needs, resulting in incomplete recovery of viral genomes, especially from low titer samples.

[0084] In certain embodiments, the methods described herein can be used to sequence viral samples with low viral loads. A viral load may also be interchangeably referred to as viral burden or viral titer. A viral load may be expressed in viral particles per mL, infectious particles per mL, copies per mL, or virus per mL. A low viral load may be a cycle threshold (CT)>30 or copies per mL<10.sup.4. A high viral load may be a CT<30 or par or copies per mL >10.sup.5. For example, viral loads lower than 10,000, 1,000, 500, 400, 300, 200, 100, 50, 40, 30, 20, 10 viral particles. In certain embodiments, a single viral particle is sequenced.

[0085] In certain embodiments, the SDSI is used to detect and prevent contamination in genomic analysis samples of pathogens. A pathogen may include viruses, bacteria, fungi, and protozoa. In certain embodiments, a virus may belong to any morphological category including helical, envelope, or icosahedral. In certain embodiments, a virus me comprise of DNA or RNA, may be single stranded or double stranded, and may be linear or circular. In certain embodiments, the genome of the virus may be one nucleic acid molecule or several nucleic acid segments. In certain embodiments a virus may belong to the family: Adenoviridae, Papovaviridae, Parvoviridae, Herpesviridae, Poxviridae, Anelloviridae, Pleolipoviridae, Reoviridae, Picornaviridae, Caliciviridae, Togaviridae, Arenaviridae, Flaviviridae, Orthomyxoviridae, Paramyxoviridae, Bunyaviridae, Rhabdoviridae, Filoviridae, Astroviridae, Bornaviridae, Arteriviridae, Hepeviridae, Retroviridae, Caulimoviridae, Hepadnaviridae, Coronaviridae. In certain embodiment, the virus is SARS-CoV-2. (Gelderblom HR. Structure and Classification of Viruses. In: Baron S, editor. Medical Microbiology. 4th edition. Galveston (Tex.): University of Texas Medical Branch at Galveston; 1996. Chapter 41)

[0086] In an exemplary embodiment, the pathogen sequenced is a coronavirus. As used herein, “coronavirus” refers to enveloped viruses with a positive-sense single-stranded RNA genome and a nucleocapsid of helical symmetry that constitute the subfamily Orthocoronavirinae, in the family Coronaviridae (see, e.g., Woo P C, Huang Y, Lau S K, Yuen K Y. Coronavirus genomics and bioinformatics analysis. Viruses. 2010; 2(8):1804-1820). Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the virus causing the ongoing Coronavirus Disease 19 (COVID19) pandemic (see, e.g., Zhou, et al. (2020). A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature 579, 270-273). In preferred embodiments, the virus is SARS-CoV-2 or variants thereof. In preferred embodiments, the disease treated is COVID-19. SARS-CoV-2 is the third zoonotic betacoronavirus to cause a human outbreak after SARS-CoV in 2002 and Middle East respiratory syndrome coronavirus (MERS-CoV) in 2012 (de Wit et al., 2016, SARS and MERS: recent insights into emerging coronaviruses. Nat Rev Microbiol 14, 523-534). While there are many thousands of variants of SARS-CoV-2, (Koyama, Takahiko Koyama; Platt, Daniela; Parida, Laxmi (June 2020). “Variant analysis of SARS-CoV-2 genomes”. Bulletin of the World Health Organization. 98: 495-504) there are also much larger groupings called clades. Several different clade nomenclatures for SARS-CoV-2 have been proposed. As of December 2020, GISAID, referring to SARS-CoV-2 as hCoV-19 identified seven clades (O, S, L, V, G, GH, and GR) (Alm E, Broberg E K, Connor T, et al. Geographical and temporal distribution of SARS-CoV-2 clades in the WHO European Region, January to June 2020 [published correction appears in Euro Surveill. 2020 August; 25(33):]. Euro Surveill. 2020; 25(32):2001410). Also as of December 2020, Nextstrain identified five (19A, 19B, 20A, 20B, and 20C) (Cited in Alm et al. 2020). Guan et al. identified five global clades (G614, S84, V251, 1378 and D392) (Guan Q, Sadykov M, Mfarrej S, et al. A genetic barcode of SARS-CoV-2 for monitoring global distribution of different clades during the COVID-19 pandemic. Int J Infect Dis. 2020; 100:216-223). Rambaut et al. proposed the term “lineage” in a 2020 article in Nature Microbiology; as of December 2020, there have been five major lineages (A, B, B.1, B.1.1, and B.1.777) identified (Rambaut, A.; Holmes, E. C.; O'Toole, A.; et al. “A dynamic nomenclature proposal for SARS-CoV-2 lineages to assist genomic epidemiology”. 5: 1403-1407).

[0087] Exemplary, non-limiting variants applicable to the present invention are described below. Genetic variants of SARS-CoV-2 have been emerging and circulating around the world throughout the COVID-19 pandemic (see, e.g., The US Centers for Disease Control and Prevention; www.cdc.gov/coronavirus/2019-ncov/variants/variant-info.html). Exemplary, non-limiting variants applicable to the present disclosure include variants of SARS-CoV-2, particularly those having substitutions of therapeutic concern. Table A shows exemplary, non-limiting genetic substitutions in SARS-CoV-2 variants.

TABLE-US-00002 TABLE A Common Pango Lineages with Spike Spike Protein Substitution Protein Substitutions L452R A.2.5, B.1, B.1.429, B.1.427, B.1.617.1, B.1.526.1, B.1.617.2, C.36.3 E484K B.1.1.318, B.1.1.7, B.1.351, B.1.525, B.1.526, B.1.621, B.1.623, P.1, P.1.1, P.1.2, R.1 K417N, E484K, N501Y B.1.351, B.1.351.3 K417T, E484K, N501Y P.1, P.1.1, P.1.2 A67V, del69-70, T95I, del142-144, Y145D, del211, B.1.1.529 and BA lineages L212I, ins214EPE, G339D, S371L, S373P, S375F, K417N, N440K, G446S, S477N, T478K, E484A, Q493R, G496S, Q498R, N501Y, Y505H, T547K, D614G, H655Y, N679K, P681H, N764K, D796Y, N856K, Q954H, N969K, L981F
Phylogenetic Assignment of Named Global Outbreak (PANGO) Lineages is software tool developed by members of the Rambaut Lab. The associated web application was developed by the Centre for Genomic Pathogen Surveillance in South Cambridgeshire and is intended to implement the dynamic nomenclature of SARS-CoV-2 lineages, known as the PANGO nomenclature. It is available at cov-lineages.org.

[0088] In some embodiments, the SARS-CoV-2 variant is and/or includes: B.1.1.7, also known as Alpha (WHO) or UK variant, having the following spike protein substitutions: 69del, 70del, 144del, (E484K*), (S494P*), N501Y, A570D, D614G, P681H, T716I, S982A, and D1118H (K1191N*); B.1.351, also known as Beta (WHO) or South Africa variant, having the following spike protein substitutions: D80A, D215G, 241del, 242del, 243del, K417N, E484K, N501Y, D614G, and A701V; B.1.427, also known as Epsilon (WHO) or US California variant, having the following spike protein substitutions: L452R, and D614G; B.1.429, also known as Epsilon (WHO) or US California variant, having the following spike protein substitutions: S131, W152C, L452R, and D614G; B.1.617.2, also known as Delta (WHO) or India variant, having the following spike protein substitutions: T19R, (G142D), 156del, 157del, R158G, L452R, T478K, D614G, P681R, and D950N; P.1, also known as Gamma (WHO) or Japan/Brazil variant, having the following spike protein substitutions: L18F, T20N, P26S, D138Y, R190S, K417T, E484K, N501Y, D614G, H655Y, and T10271; and B.1.1.529 also known as Omicron (WHO), having the following spike protein substitutions: A67V, del69-70, T95I, del142-144, Y145D, del211, L212I, ins214EPE, G339D, S371L, S373P, S375F, K417N, N440K, G446S, S477N, T478K, E484A, Q493R, G496S, Q498R, N501Y, Y505H, T547K, D614G, H655Y, N679K, P681H, N764K, D796Y, N856K, Q954H, N969K, L981F, or any combination thereof.

[0089] In some embodiments, the SARS-CoV-2 variant is classified and/or otherwise identified as a Variant of Concern (VOC) by the World Health Organization and/or the U.S. Centers for Disease Control. A VOC is a variant for which there is evidence of an increase in transmissibility, more severe disease (e.g., increased hospitalizations or deaths), significant reduction in neutralization by antibodies generated during previous infection or vaccination, reduced effectiveness of treatments or vaccines, or diagnostic detection failures.

[0090] In some embodiments, the SARS-Cov-2 variant is classified and/or otherwise identified as a Variant of High Consequence (VHC) by the World Health Organization and/or the U.S. Centers for Disease Control. A variant of high consequence has clear evidence that prevention measures or medical countermeasures (MCMs) have significantly reduced effectiveness relative to previously circulating variants.

[0091] In some embodiments, the SARS-Cov-2 variant is classified and/or otherwise identified as a Variant of Interest (VOI) by the World Health Organization and/or the U.S. Centers for Disease Control. A VOI is a variant with specific genetic markers that have been associated with changes to receptor binding, reduced neutralization by antibodies generated against previous infection or vaccination, reduced efficacy of treatments, potential diagnostic impact, or predicted increase in transmissibility or disease severity.

[0092] In some embodiments, the SARS-Cov-2 variant is classified and/or is otherwise identified as a Variant of Note (VON). As used herein, VON refers to both “variants of concern” and “variants of note” as the two phrases are used and defined by Pangolin (cov-lineages.org) and provided in their available “VOC reports” available at cov-lineages.org.

[0093] In some embodiments the SARS-Cov-2 variant is a VOC. In some embodiments, the SARS-CoV-2 variant is or includes an Alpha variant (e.g., Pango lineage B.1.1.7), a Beta variant (e.g., Pango lineage B.1.351, B.1.351.1, B.1.351.2, and/or B.1.351.3), a Delta variant (e.g., Pango lineage B.1.617.2, AY.1, AY.2, AY.3 and/or AY.3.1); a Gamma variant (e.g., Pango lineage P.1, P.1.1, P.1.2, P.1.4, P.1.6, and/or P.1.7), a Omicon variant (B.1.1.529) or any combination thereof.

[0094] In some embodiments the SARS-Cov-2 variant is a VOL In some embodiments, the SARS-CoV-2 variant is or includes an Eta variant (e.g., Pango lineage B.1.525 (Spike protein substitutions A67V, 69del, 70del, 144del, E484K, D614G, Q677H, F888L)); an Iota variant (e.g., Pango lineage B.1.526 (Spike protein substitutions LSF, (D80G*), T95I, (Y144-*), (F157S*), D253G, (L452R*), (5477N*), E484K, D614G, A701V, (T859N*), (D950H*), (Q957R*))); a Kappa variant (e.g., Pango lineage B.1.617.1 (Spike protein substitutions (T95I), G142D, E154K, L452R, E484Q, D614G, P681R, Q1071H)); Pango lineage variant B.1.617.2 (Spike protein substitutions T19R, G142D, L452R, E484Q, D614G, P681R, D950N)), Lambda (e.g., Pango lineage C.37); or any combination thereof.

[0095] In some embodiments SARS-Cov-2 variant is a VON. In some embodiments, the SARS-Cov-2 variant is or includes Pango lineage variant P.1 (alias, B.1.1.28.1.) as described in Rambaut et al. 2020. Nat. Microbiol. 5:1403-1407) (spike protein substitutions: T20N, P26S, D138Y, R190S, K417T, E484K, N501Y, H655Y, TI0271)); an Alpha variant (e.g., Pango lineage B.1.1.7); a Beta variant (e.g., Pango lineage B.1.351, B.1.351.1, B.1.351.2, and/or B.1.351.3); Pango lineage variant B.1.617.2 (Spike protein substitutions T19R, G142D, L452R, E484Q, D614G, P681R, D950N)); an Eta variant (e.g., Pango lineage B.1.525); Pango lineage variant A.23.1 (as described in Bugembe et al. medRxiv. 2021. doi: https://doi.org/10.1101/2021.02.08.21251393) (spike protein substitutions: F157L, V367F, Q613H, P681R); or any combination thereof.

[0096] In certain embodiments, the pathogen sequenced is a pathogenic bacteria and may include: spirochetes; Spirilla; vibrios; gram-negative aerobic rods and cocci; enterics; pyogenic cocci; and endospore-forming bacteria; actinomycetes and related bacteria; rickettsias and chlamydiae; mycoplasmas, which are groups defined by some bacteriological criteria. A pathogenic bacteria may include: Escherichia coli, Salmonella enterica, Salmonella typhi, Shigella dysenteriae, Yersina pestis, Pseudomonas aeruginosa, Vibrio cholerae, Bordetella pertussis, Haemophilus influenza, Helicobacter pylori, Campylobacter jejuni, Neisseria gonorrhoeae, Neisseria meningitidis, Brucella abortus, Bacteroides fragilis, Staphylococcus aureus, Streptococcus pyogenes, Streptococcus pneumoniae, Bacillus anthracis, Bacillus cereus, Clostridium tetani, Clostridium perfringens, Clostridium botulinum, Clostridium difficile, Corynebacterium diphtherias, Listeria monocytogenes, Mycobacterium tuberculosis, Mycobacterium leprae, Chlamydia trachomatis, Chlamydia pneumoniae, Mycoplasma pneumoniae, Rickettisas, Treponema pallidum, Borrelia burgdorferi, or a variant thereof (Todar, K. Textbook of Bacteriology (2020) Online)

[0097] In an exemplary embodiment, the pathogen sequenced is a pathogenic fungi and may include: Aspergillus; Blastomyces; Candida; Coccidioides; Cryptococcus; Fusarium; Microsporum; Epidermophyton; Trichophyton; Histoplasma; Rhizopus; Mucor; Rhizomucor; Syncephalastrum; Cunninghamella; Apophysomyces; Lichtheimia (formerly Absidia); Eumycetoma; Pneumocystis; Trichophyton; Microsporum; Epidermophyton; Sporothrix; Paracoccidioides; Talaromyces or a variant or species thereof. (CDC)

[0098] In an exemplary embodiment, the pathogen sequenced is a pathogenic protozoa belonging to the group: Sarcodina; Mastigophora; Ciliophora; or Sporozoa defined by their mode of movement. (CDC) In certain embodiments, the pathogenic protozoa may include: Entamoeba; Trichomonas; Leishmania; Chilomonas; Giardia; Isopora; Sarcocystis; Nosema; Balantidium; Eimeria; Histomonas; Trypanosoma; Plasmodium; Babesia; or Haemoproteus or a variant or species thereof.

[0099] Further embodiments are illustrated in the following Examples which are given for illustrative purposes only and are not intended to limit the scope of the invention.

EXAMPLES

[0100] Here Applicants designed, optimized, and implemented a novel sample identification method using synthetic DNA spike-ins (SDSIs) that is broadly compatible with SARS-CoV-2 sequencing approaches and settings. Applicants implemented these SDSIs for Illumina sequencing with SARS-CoV-2 specific amplification using the ARTIC consortium's amplicon designs. To maximize epidemiological utility by increasing the number of genomes recovered from samples with low viral loads, Applicants benchmarked key amplification and library construction steps. Applicants propose a modified protocol, hereafter termed SDSI+ARTIC, that provides increased confidence in the veracity of genomes with minimal extra cost and time that can be applied to investigations of SARS-CoV-2 epidemiology and emerging viral variants (FIG. 1).

Example 1—Design and in Silico Validation of Novel Amplicon Spike-Ins

[0101] Applicants sought to design a robust system for contamination tracing and sample tracking applicable to a wide-variety of viral sequencing strategies via known synthetic DNA sequences. Applicants envisioned that these novel synthetic DNA spike-ins (SDSIs) would consist of a uniquely identifiable sequence such that each sample in a sequencing batch could be paired with a different SDSI, enabling in-sample labeling. SDSIs should be sufficiently distinct from one another as well as common laboratory or human pathogens to ensure reliable identification. Each unique sequence is then flanked by constant priming regions so that a single additional primer set can be integrated into a multiplexed PCR to co-amplify the SDSI with the sample (FIG. 2A). In labeling all amplified viral genomic material in a laboratory setting, Applicants could track sample swaps and viral contamination with exquisite resolution and accuracy.

[0102] Excerpting DNA sequences from diverse, exotic archaea genomes to serve as the unique portion of the SDSI precludes false detection and cross-identification. To balance common sequencing library construction constraints, DNA synthesis costs, and providing enough sequence to be uniquely identifiable, Applicants generated SDSIs with a 140 bp stretch of variable sequence. Applicants confirmed that the various SDSIs were significantly different from each other to mitigate cross-identification; among all SDSIs, the minimum pairwise Hamming distances of the 140 bp stretch of unique sequence was 84 (mean=105; max=121). Since false detection of SDSI would occur if its sequence shared significant homology with other genetic material in a sample, Applicants based these sequences on archaea, which are divergent from organisms found in typical laboratory or clinical settings (Table 2). A permissive search performed against the entire NCBI database confirmed that 44/48 SDSI sequences had significant homology (>75% sequence identity over >75% query cover) exclusively within the domain archaea; the remaining SDSIs had homology to a handful of bacterial genuses unlikely to be found in laboratories (Table 2). In considering the application of these SDSIs to ARTIC SARS-CoV-2 amplicon sequencing, Applicants also specifically verified that each unique SDSI sequences were unlikely to be confused with expected COVID-19 clinical sample content, confirming that each sequence had very limited homology (nothing >50% sequence identity over >50% query cover) to both Homo sapiens and SARS-CoV-2. In designing these amplicon sequences Applicants also avoided extremes of GC content (range: 35-65%) in order to promote similar amplification rates across different SDSIs, as well as other potential targets of the multiplexed reaction, such as viral amplicons. Applicants specifically ensured that the SDSIs had similar GC content to ARTIC SARS-CoV-2 amplicons (FIG. 6).

[0103] Similarly, the design of common primers for SDSI amplicons enabled compatibility with a broad spectrum of amplicon-based sequencing reactions, including in clinical settings. To preclude off-target priming in the PCR reaction that could outcompete amplification of a primary target, Applicants limited SDSI primer homology to common organisms, particularly on the 3′ end of the primer. Applicants specifically confirmed that primers were unlikely to amplify human or SARS-CoV-2 to promote SDSI primer integration into the ARTIC SARS-CoV-2 amplicon sequencing PCR reaction. Primers were compatible with ARTIC v3 primer sets, with a similar length (24 bps each) and GC content (45.8% each) (FIG. 6).

Example 2—Application of Spike-Ins to ARTIC SARS-CoV-2 Sequencing

[0104] Applicants demonstrated that the addition of SDSIs into the ARTIC multiplexed PCR provided a sample-specific internal control and did not detrimentally affect the amplification of SARS-CoV-2 RNA. SDSI primers did not produce any nonspecific amplification, including in the presence of NP swab RNA, supporting the expectation that primers shared limited homology with genomic material from clinical samples (FIG. 6). All SDSIs amplified in an ARTIC SARS-CoV-2 PCR reaction with SDSI primers included, in each case yielding a single clean product of the expected size (FIG. 6). Applicants next sought to ensure that inclusion of the SDSI oligo and SDSI primers did not limit amplification of SARS-CoV-2 RNA. To prevent SDSIs overtaking the amplification and sequencing of SARS-CoV-2 amplicons, Applicants optimized the amount of SDSI added to each reaction through limited titration (FIG. 7). Applicants found that 1 μl of a 1fM SDSI resulted in the reliable detection of the SDSI across a range of CT values (CT 20, 25, 30, 35) while the majority of reads (>96%) still mapped to SARS-CoV-2 (Table 3; FIG. 2B).

[0105] Applicants performed SDSI+ARTIC sequencing on a batch of 48 SARS-CoV-2+clinical samples to demonstrate its feasibility and utility in tracking samples and identifying contamination. After adding a different SDSI to each sample, Applicants found that 47/48 SDSIs were identified exclusively in the anticipated sample, validating the use of SDSIs as an internal control for sample tracking. One SDSI (SDSI 48) was detected in the sample that it was added to as well as a neighboring sample in the batch (FIG. 2C). Applicants suspect that this represents unintentional within-batch contamination that was likely a consequence of spillover between neighboring wells. This case reveals the insidious nature of commonplace contamination and underscores the importance of this novel method for identifying it.

[0106] As shorter amplicons have been purported to yield superior recovery for low viral load samples (Antonov et al., 2005; No et al., 2019)), Applicants explored extending SDSIs to the Paragon Genomics' CleanPlex SARS-CoV-2 panel, but identified fatal shortcomings. Paragon amplicons are on average half the size of ARTIC (149 bp vs 343 bp), and compatible with the SDSI length 140 bp. (Antonov et al., 2005; No et al., 2019) (SARS-CoV-2 COVID-19 Coronavirus Research and Surveillance, n.d.)(Antonov et al., 2005; No et al., 2019). However, the Paragon panel had dropout regions even in low CT samples which resulted in missed SNP calls compared to ARTIC across 5 samples (CTs=20-37), consistent with other reports (FIG. 8A, 8B) (Klempt et al., 2020). Although this panel did recover more of the genome in very high CT samples (>35), Applicants did not proceed with SDSI integration as the uneven and unreliable genome coverage across most clinical CTs limited Paragon's epidemiological utility (FIG. 8C).

Example 3—Improving Genome Recovery and Coverage for Illumina-Based ARTIC SARS-CoV-2 Sequencing

[0107] Applicants benchmarked various alterations to Illumina-based SDSI+ARTIC sequencing in order to maximize the number of complete, high-quality genomes recovered from clinically diverse samples. Higher CT samples prove especially challenging to sequence but their recovery is still of critical importance to epidemiological and clinical applications of viral genomics. Applicants found that substituting a more processive reverse transcriptase provided the single biggest benefit. Comparing cDNA produced with Superscripts III, IV, or IV-VILO across a range of clinical CTs (low CT: <20, mid-low CT: 20-25, mid-high CT: 25-30, and high CT: >30), SSIV-VILO and SSIV produced the highest number of amplicons with at least 10× coverage across 13 samples (SSIII: 72.64%, SSIV: 81.93%, SSIV-VILO: 86.97%) (FIG. 3A). These processive reverse transcriptases also displayed lower variability as measured by the percent of amplicons with <20% mean coverage (SSIII: 36.89% SSIV: 31.24% SSIV-VILO: 22.45%) (FIG. 9A). Applicants also tested five DNA polymerase and conditions in the SDSI+ARTIC PCR reaction (Methods) and found that Q5 Hot Start High-Fidelity 2× Master Mix and KAPA reactions yielded the highest amplification (average 85.3 nM and 56 nM respectively) (FIG. 9B).

[0108] Applicants also attempted protocol modifications to increase sequence depth uniformity in SDSI+ARTIC, which is crucial for recovering complete genomes in the fewest number of reads. When Applicants increased (2×) primer concentrations (20.8 nM final) for low efficiency amplicons, Applicants observed increased coverage in these amplicons that enabled whole genome recovery for multiple samples, especially those with higher CTs (FIG. 3B; FIG. 10; Table 4). Other groups have also noted that alternative primers or changes in annealing temperature can reduce the formation of certain primer interactions, and Applicants suspect exploration of these avenues would further optimize SDSI+ARTIC (Itokawa et al., 2020). Applicants also attempted to recover high CT samples by increasing the number of PCR cycles and observed greater coverage uniformity with increasing cycles (FIG. 3C). However, at 45 cycles Applicants observed 3 SNPs that were not present in lower-amplified samples. To avoid erroneous SNP calls, Applicants decided to implement and optimize the SDSI's for a 40 cycle PCR. Additional modifications such as DNA-rehybridization steps (Mathieu-Daudé et al., 1996) or slower temperature ramp speeds had no significant effects (FIG. 9C, 9D).

[0109] Applicants reduced the potential for highly amplified library contamination within the laboratory or clinical setting by scaling down (0.5×) the Illumina DNA Flex library construction kit, which also reduced per sample cost without impacting performance (Table 5; Table 6). In benchmarking library construction methods, Applicants confirmed Nextera DNA Flex generated greater coverage depth than DNA XT (FIG. 10). In combination, the final suggested modifications to Illumina ARTIC sequencing include using more processive reverse transcriptases, 40 cycles of PCR, and 2× primer concentration to recover higher CT samples, as well as a 0.5× scale down of Illumina DNA Flex to produce less concentrated, and thus less likely to contaminate libraries at a halved cost. Integrating these modifications into the SDSI approach may enable greater genomic surveillance in a limited number of samples.

Example 4—SDSI-ARTIC Sequencing Benchmarks Well Against Metagenomic Sequencing

[0110] Highlighting the reliability and robustness of this approach, Applicants observed high sequence correlation and superior genome recovery with SDSI+ARTIC compared to an unbiased metagenomics approach, the gold standard in generating error-free viral genomes. Applicants sequenced a small batch of six samples (CTs=16-31) using ARTIC without SDSIs, and generated full length genomes with 100% concordance to those generated with metagenomic sequencing, indicating the accuracy of ARTIC-based sequencing methods (Lemieux et al., 2021). Applicants then resequenced 89 unique patient samples with SDSI+ARTIC that were previously sequenced using the same standard metagenomics approach (Lemieux et al., 2021) to serve as a direct comparison. The 89 samples in the validation batch consisted of diverse viral lineages and a broad range of CTs (range=11.9-37.4; mean=27.4) (FIG. 12A). SDSI+ARTIC outperformed metagenomic sequencing in terms of genome recovery, with increased median assembly lengths (29,577 bp and 4,389 bp respectively) (FIG. 4A), and a higher number of complete (>98%) genomes assembled (50 and 31 respectively). Applicants recovered even more partial (>80%) genomes with SDSI+ARTIC when compared to metagenomic sequencing (75 vs 36 respectively). Notably, 5 complete genomes recovered for SDSI+ARTIC had a CT above 30 (FIG. 4B; FIG. 12B). Applicants also assessed coverage uniformity in both methods, as increasing uniformity reduces the sequencing depth required to generate reliable genomes, thus improving throughput and efficiency. (So et al., 2018). As measured by a gini coefficient for each sample that generated an assembly, uniformity decreased in both methods above a CT of 25 but was markedly worse for metagenomics (FIG. 12C).

[0111] SDSI+ARTIC displayed high concordance in sequence variant identification to metagenomics, producing only two divergent SNP calls out of 331 total SNPs across 38 genomes (FIG. 4C). Notably, this discordance was present with both relaxed (n=3) and conservative (n=20) minimum coverage thresholds. The discordant SNPs, observed in two samples, were present in different regions. However, both were located in ARTIC primer regions and matched the primer sequence even though primer trimming was performed and confirmed by manual inspection. Additionally, the coverage depth in the regions of the SNPs was greater than 1000× for both platforms in both samples. Applicants believe these errors likely arose during the ARTIC PCR, suggesting a discordance rate of 0.6% between amplicon-based and metagenomic sequencing. Notably these few mismatches did not result in lineage misassignment for either sample. To evaluate concordance, Applicants compared consensus sequences without down sampling using only samples that produced a full genome to make the most equivalent comparison (Methods).

Example 5—Rapid Deployment of SDSI-ARTIC Sequencing Confirms a Suspected Nosocomial Transmission Cluster

[0112] SDSI+ARTIC is a powerful method for public health interventions, especially as superspreading events—and clusters of cases linked to close contact settings more broadly—have become a defining feature of the SARS-CoV-2 pandemic ((Adam et al., 2020; Dearlove et al., 2020; Lemieux et al., 2021; Wong & Collins, 2020)). Viral genomes can reveal whether these clusters are linked through transmission, based on shared viral sequences, providing useful information for public health interventions. Such outbreak investigations of single cases leading to many are distinguishable due to low viral sequence variation but requires higher levels of confidence to ensure such a pattern has not occurred due to laboratory contamination. To demonstrate the utility of the novel SDSIs and modified protocol, Applicants applied the method to investigate a putative cluster of 14 SARS-CoV-2 cases from Massachusetts General Hospital (MGH), for which the infection control unit had suspicion of a nosocomial outbreak. Applicants sequenced 24 samples; 14 samples believed to be part of the cluster based on traditional contact-tracing, 8 unlinked samples and 2 negative controls.

[0113] The SDSI+ARTIC method enabled fast and confident identification of a nosocomial cluster, with samples processed within 24 hours and final genomes assembled within 52 hours of bio-sample receipt. Applicants assembled 14 complete genomes (>98% complete) of which 9 were from cluster-associated samples. Those samples that did not yield a full genome were those with lower viral loads (CT>30). Phylogenetic analysis showed that samples from the cluster were genetically highly similar and clustered together (FIG. 5A, 5B) to the exclusion of other samples from Boston around the same time, strongly suggesting that this cluster did reflect transmission within the hospital. One sample, MA-MGH-02834, differed from other cluster-associated samples by 18-19 consensus-level variants suggesting that this infection was likely acquired separately and not as part of the same nosocomial transmission. Analysis of the SDSIs confirmed that genome sequence similarity was not the result of cross-contamination from highly amplified final libraries (FIG. 5C).

Example 6—Discussion on Novel Amplicon Spike-Ins

[0114] As the SARS-CoV-2 pandemic intensifies and new genomic variants continue to emerge, it is imperative to build robust experimental confidence into genomic surveillance data interpretation. Here, Applicants report a novel design and implementation of Synthetic DNA Spike-ins (SDSI) as an essential component for tracking and tracing contamination, a potential confounder in amplicon-based sequencing methods of SARS-CoV-2. The in-silico design generated robust synthetic targets at low costs while mitigating inter-spike-in sequence homology as well as homology with human, SARS-CoV-2, and common laboratory reagents. While broadly applicable to most amplicon-based approaches, as a proof-of-principle Applicants coupled the SDSIs to an improved ARTIC amplicon sequencing protocol yielding faster throughput with an overall reduced cost compared to existing Illumina DNA Flex-based protocols.

[0115] SDSIs can readily be adopted by laboratories and platforms of all sizes with only minor changes to existing methodologies, little additional cost per sample ($0.006), and no interruption to standard workflow methodologies. Additional synthetic targets could be designed using the same principles to expand into 384 well formats and beyond. Primer sites could also be modulated for integration with new advancements in amplicon sequencing, like tailed primer approaches (Gohl et al., 2020). More broadly, standardizing controls across the viral surveillance community will increase accuracy and integrity of SARS-CoV-2 genomic data worldwide. These SDSIs not only enable profiling of in-batch contamination, but also laboratory-wide detection as their presence in other data (amplicon, metagenomic, qPCR, or otherwise) would indicate a tagged amplification and thus contamination. Moreover the approach is applicable to both Illumina and Nanopore sequencing platforms as well as any other existing or future tiled amplicon panel, such as those previously used for Zika, Ebola, and other recent outbreaks (Quick et al., 2016) (Metsky et al., 2017). SDSIs could serve as a broad tool for tracing potential contamination across a plethora of fields that employ amplicon based genomic sequencing, such as food safety, species identification or environmental sampling.

[0116] In optimizing the SDSI+ARTIC protocol Applicants tested and incorporated a number of cost and time saving adjustments. Modifications that can be used include implementing liquid handlers in high volume settings such as public health laboratories. Additional methodological improvements could allow for direct PCR amplification of SARS-CoV-2 using primers with indexing adapter compatible ends (Baker et al., 2020; Gohl et al., 2020) or the inclusion of unique molecular identifiers to understand intra-host variation. The SDSIs were designed to be compatible with such potential future approaches. Applicants note that there is still considerable non-uniformity in per-amplicon coverage for samples with low viral loads highlighting the need for methods that can confidently capture this information. A recent update to the ARTIC protocol for nanopore suggests that a change in the annealing temperature from 65° C. to 63° C. can reduce dropout of amplicon 64 (Tyson et al., 2020), a particularly poorly performing amplicon. The results show that 2× primer concentration for a subset of underperforming amplicons improved performance, and matching primer concentrations with amplicon efficiency would likely yield more uniform coverage (Table 4). Alternative approaches for the recovery of genomes from samples with low viral load include the use of targeted enrichment approaches (Houldcroft et al., 2017; Metsky et al., 2019) are more costly and time-consuming.

[0117] Amplicon based sequencing methods fill a critical need for rapid turn around and full genome recovery for epidemiological surveillance where SNP identification is crucial. While benchmarking the modified protocol against the gold standard approach of metagenomics Applicants observed discordant SNPs were rare (2/331). This emphasizes the need for caution and replication of libraries for highly important samples. Other commercial amplicon-based designs such as those by Paragon Genomics are significantly faster workflows and use smaller size amplicons, but the ARTIC primer set results in better overall coverage for the majority of samples (up to CT=35) and genome accuracy. Applicants believe subsequent generations of amplicon-based sequencing will address this pressing need pushing cost down while increasing genomic surveillance accuracy, which is sorely needed in the public health setting. The rapid deployment of SDSI+ARTIC confirming a nosocomial infection cluster further emphasizes the utility of the SDSIs to confidently identify samples of high genetic similarity.

The potential emergence of SARS-CoV-2 immune and vaccine escape variants underscores the ongoing necessity of accurate, reliable, and accessible genome sequencing. The modifications and suggestions build upon a remarkable global genomic surveillance response that has developed new tools for the rapid sequencing of viral genomes at an unprecedented rate. In light of the latest surges in SARS-CoV-2 cases globally and the emergence of more transmissible lineages and variants of concern that are rising in frequency in multiple continents, continual innovation in these protocols to improve their efficiency, cost-effectiveness and reliability are essential to meet the growing need for genomic surveillance of SARS-CoV-2. Moreover, stringent sample tracking and contamination detection strategies must become a standard practice, maximizing the utility of genomic data and its increasing importance for shaping public health interventions.

Example 7—Design and Characterization of Synthetic DNA Spike-Ins for AmpSeq

[0118] Applicants designed a simple and flexible system for sample tracking and contamination tracing using a core uniquely identifiable DNA sequence flanked by constant priming regions that satisfy several design requirements. This design allows in-sample tracking through the addition of a different SDSI to each sample during sample processing. Following sequencing, the data can be analyzed for both the presence of the expected SDSI and any other SDSI, illuminating both sample misassignment and contamination with high resolution and accuracy (FIG. 13). Applicants focused the initial design on highly stable DNA oligos that would be added to sample cDNA and could capture contamination at or after the critical viral amplification step, including contamination generated during amplification and in handling amplified material. By using a longer unique core sequence, as compared to a short barcode system, these SDSIs are compatible with both tagmentation- and ligation-based sequencing approaches. The constant priming regions mean that only a single primer pair needs to be added into the existing multiplexed PCR step to co-amplify all SDSIs with the primary reaction target(s) (FIG. 14A). In particular, Applicants sought to design a system that could be integrated into diverse amplicon-based viral sequencing approaches. 96 distinct DNA sequences from the genomes of diverse, uncommon archaea serve as the core portion of each SDSI, precluding false detection and cross-identification (Table 1, Methods). By using extremophilic archaea, the designs maximized evolutionary distance from common human pathogens. To avoid false positive results the core SDSI sequences should be sufficiently distinct from one another, as well as sequences commonly found in laboratories and clinical samples. A permissive BLASTn search performed against the entire NCBI database confirmed that the unique SDSI core sequences had limited homology outside the domain archaea, specifically to genera unlikely to be found in laboratories (FIG. 18A). While this limited homology outside of the domain archaea maximized the potential for broad applications, Applicants also confirmed that none of the core sequences shared significant homology with Homo sapiens or known viral genomes (Methods). Applicants considered significant homology as >90% sequence identity over 50 bps, as library construction can result in the generation of small fragments. Similarly, Applicants confirmed that all SDSIs were significantly different from each other to prevent misidentification; among all pairwise combinations of SDSIs, the greatest homology occurred between SDSI 14 and 18, which had 15 mismatches over 66 bps (FIG. 18B). Sequencing of the SDSIs confirmed that each of the 96 constructs resulted in a robust and specific signal of mapped reads (FIG. 14B).

[0119] Applicants selected a pair of primers and corresponding priming regions on each SDSI that are highly specific and show reliable amplification across SDSIs and under standard PCR conditions. Using Primer-BLAST, Applicants predicted that these sequences had limited homology to common organisms and thus were unlikely to amplify nonspecific templates that could outcompete amplification of a primary target. Experimentally Applicants confirmed that the SDSI primers did not produce any nonspecific amplification, including in the presence of cDNA from a nasopharyngeal (NP) swab sample (FIG. 19A). The primer pair also had a common length (24 bps), GC content (45.8%), and melting temperature (62° C. and 63° C., respectively in the SDSI+AmpSeq protocol), ensuring their compatibility with many multiplexed PCR reactions, including the most widely used SARS-CoV-2 amplicon sequencing strategy (artic.network/) (FIG. 19B). Since each SDSI was identically sized and shared a priming region, a similar amplification rate was expected across all SDSIs. Applicants avoided extremes of GC content in SDSI amplicons (range: 33-65%) in order to promote similar amplification rates across different SDSIs and to viral amplicons (e.g., the GC content of the SARS-CoV-2 genome is roughly 37±5%).sup.19 (FIG. 19C). Applicants confirmed experimentally that all SDSIs amplified in an ARTIC SARS-CoV-2 PCR reaction with SDSI primers included, in each case yielding a single clean product of the expected size (FIG. 19D). Furthermore, Applicants observed that GC content did not significantly bias the number of SDSI reads detected in clinical samples (FIG. 19E).

Example 8—Validation of an SDSI+AmpSeq SARS-CoV-2 Sequencing Approach

[0120] Applicants determined that the addition of SDSIs into the ARTIC multiplexed PCR did not detrimentally affect or otherwise alter the amplification of SARS-CoV-2 cDNA from clinical samples. First, to prevent SDSIs from overtaking the amplification and sequencing of SARS-CoV-2 amplicons, Applicants optimized the amount of SDSI added to each reaction through limited titration. Using a randomly selected SDSI (SDSI 49), Applicants found that the highest concentration tested, 600 copies/μL, resulted in reliable SDSI detection with >96% of reads still mapping to SARS-CoV-2 and no apparent alteration in coverage across the genome (FIG. 20A,B). Applicants then validated the specificity of the 96 selected SDSIs in a batch of clinical samples to confirm that there was no unpredicted cross-mapping, misidentification, or significant differences in amplification rate (FIG. 15A). To assess more precisely how the addition of SDSIs would affect SARS-CoV-2 genome sequencing in clinical samples, Applicants processed 14 samples, spanning a range of CT values (CT range=25-33), with both the standard ARTIC and SDSI+AmpSeq methods. For each amplicon, across all samples, there was no significant difference in coverage between the ARTIC and SDSI+AmpSeq conditions (FIG. 15B). Even in samples with low viral loads (CT>30), Applicants found that there were no significant differences in amplicon coverage (FIG. 21A). Additionally, within the 14 samples processed with and without an SDSI, Applicants see a 100% genome concordance rate illustrating the addition of the SDSIs does not impact recovery of accurate genomes.

[0121] As extensive PCR can result in the propagation of numerous types of errors, such as DNA polymerase base substitution errors, PCR recombination events, template switching, and thermocycling induced DNA damage, Applicants further compared SARS-CoV-2 genome concordance between the SDSI+AmpSeq method and unbiased, metagenomic sequencing.sup.9,10,20. Applicants performed SDSI+AmpSeq on a batch of 89 unique patient samples previously sequenced with unbiased metagenomics.sup.21. The samples consisted of diverse viral lineages and a broad range of viral loads (CT range=11.9-37.4; mean=27.4) with the more sensitive amplicon sequencing method generating more complete genomes at higher CTs (FIG. 22A-D). Applicants assessed the coverage uniformity between the methods, as increasing uniformity reduces the sequencing depth required to generate reliable genomes.sup.22. Applicants found that unbiased sequencing had more uniform coverage up to a CT of 25 (N=31, Gini Coefficient=0.240±0.046 (unbiased) vs 0.428±0.026 (SDSI+AmpSeq)), while SDSI+AmpSeq generated more uniform coverage for samples above a CT of 25 (N=39, Gini Coeff=0.766±0.265 (unbiased) vs 0.554±0.124 (SDSI+AmpSeq)) (FIG. 22E). For the 37 samples that assembled a full genome in both methods, only two out of 332 total single nucleotide variants (SNVs) identified compared to the reference (Wuhan-Hu-1) were divergently identified by SDSI+AmpSeq (FIG. 15C). Each SNVs was observed in only one sample, and both fell within an ARTIC primer region, despite primer trimming during analysis; this suggests that PCR error from the ARTIC protocol may have contributed to the discrepancy.sup.23. Manual inspection of one SNV, (a C9565T mutation in unbiased sequencing) indicated the presence of intra-host variation in both methods with a variant allele frequency of 39.4% (SDSI+AmpSeq) and 59.2% (unbiased sequencing). Overall, the discordance rate between SNV calling for SDSI+AmpSeq and unbiased sequencing was 0.6%, a percentage that is reasonable with SNV rates and sequencing based errors. Consistent with previous reports from other groups, ARTIC amplicon sequencing maintains a high level of concordance at the consensus genome level.sup.10, even with the addition of SDSIs.

[0122] Applicants explored a number of other technical modifications to the ARTIC amplicon sequencing protocol in order to improve genome recovery, limit contamination points, and enhance reproducibility of the SDSI approach. Foremost, increasing cDNA length by use of more processive reverse transcriptases improves amplicon coverage (FIG. 23A,B). Amplification of ARTIC amplicons and SDSIs by Q5 Hot Start High-Fidelity 2× Master Mix results in higher amplification (FIG. 23C, Table 7). Applicants found that increasing (2×) primer concentrations (20.8 nM final) for poor performing amplicons increased coverage in these amplicons, even enabling whole genome recovery for multiple samples supporting that primer rebalancing can enable greater coverage.sup.24,25 (FIG. 23D, FIG. 24, Table 4). Applicants then explored the effects of different numbers of PCR cycles, DNA-hybridization steps, and temperature ramp speeds. Both DNA-hybridization steps and temperature ramping provided no significant changes in amplification (FIG. 23E,F). Although it may lead to a potential increase in erroneous SNV additional cycles of PCR can be beneficial for low viral load samples by increasing genome coverage uniformity (FIG. 23G). Using a standardized cDNA input, Applicants found that the DNA Flex library preparation kit resulted in an increased depth of coverage for the SARS-CoV-2 genome across all CT values tested, compared to Nextera XT (FIG. 23H). To further mitigate the risk of contamination from such highly amplified libraries, Applicants took advantage of the self-normalizing feature of the DNA flex kit and found that limiting the tagmentation beads by scaling down (0.5×) all components of the DNA Flex library construction reagents restricted library over-amplification. Notably, this limitation did not impact final library size distributions or SDSI amplification, while having the desired effect of generating final sequencing libraries at half their original concentrations (Table 8). This approach also had the added benefit of nearly halving the library construction cost per sample (Methods). Applicants have summarized the results of the optimizations within the full SDSI+AmpSeq protocol (https://benchling.com/s/prt-R95g0tCxKOeCAqn8lAk3); additionally, Applicants have found that the SDSIs can be easily integrated with numerous protocol alterations.

Example 9—Implementation of SDSIs to SARS-CoV-2 Clinical Samples at Scale

[0123] The SDSI+AmpSeq method is compatible with a range of viral CTs, SARS-CoV-2 lineages, origin of the patient sample, and laboratory in which the pipeline is implemented demonstrating that this is a robust and flexible approach that can be readily implemented for surveillance. A half plate of SDSIs were used at two large-scale sequencing facilities, the Broad Institute and Jackson Laboratories (JAX), for SDSI+AmpSeq SARS-CoV-2 surveillance across a total of 6,741 clinical samples and controls (JAX: N=3,838; Broad: N=2,903). Individual batches typically consisted of 92 clinical samples with 4 designated water controls. Clinical samples were largely from Maine, Massachusetts, and Rhode Island from December 2020 to July 2021 and covered a wide range of viral CT values (CT 8.4-39.9) and pango lineages (77 total lineages) (FIG. 16A). The SDSI+AmpSeq method worked robustly despite minor implementation differences in protocols between the two laboratories including alterations in the cDNA synthesis enzymes (SSIV vs Lunascript), CT normalization implementation, and library construction approaches (0.5× Illumina DNA Flex vs Illumina COVID-Seq) (Methods).

[0124] The SDSI+AmpSeq is a tractable and easily-implemented method for genome quality control when applied to high-throughput processing of clinical samples. Across thousands of clinical samples, the SDSIs performed consistently and reliably (FIG. 16B,C). The mean percentage of SDSI reads that mapped to the expected SDSI was above 95% for all SDSIs in both laboratories (FIG. 16B). This demonstrated that across a large set of highly variable clinical samples, there were no systemic issues of misidentification for specific SDSIs. Additionally, across all samples from both institutions, the percentage of all SDSI reads in SARS-CoV-2 positive samples averaged 3.71% (90% of samples fell between 0.002-9.989%) (FIG. 16C). Each SDSI consumed roughly the same read percentage, with no SDSI consistently absent or regularly taking up more than 10% of the sample reads, supporting the prediction that the unique constructs amplified at similar rates. Importantly, this low, but consistent percentage of reads mapping to SDSIs allows for their implementation without needing to greatly increase sequencing depth. Across batches, SDSIs also take up roughly similar shares of the reads, indicating that the SDSI+AmpSeq method is consistent over time. Notably, the SDSIs performed well with and without prior normalization of cDNA based on CT, however normalizing did increase the percentage of SDSI reads (FIG. 21B, FIG. 16B left, Methods). Normalization of viral CT may provide an additional level of quality control that is especially important for labs with limited sequencing capacities.

Example 10—SDSI+AmpSeq Provides Highly Confident Genome Sequencing and Analysis

[0125] SDSIs enable detection of sample swaps and contamination events that occur in large scale batch processing which may otherwise go undetected. In a controlled experiment, Applicants demonstrated that the SDSI+AmpSeq approach provides a feasible method to accurately detect contamination. Applicants mixed two SDSIs at various ratios prior to the ARTIC PCR and found that those SDSI ratios were reflected in the sequencing output (FIG. 17A). With evidence of SDSI's robust detection, uniqueness, and ability to detect intentional contamination, Applicants proceeded to use them to identify sample swaps and contamination in large batch processing. Across thousands of SARS-CoV-2 samples processed, SDSIs detected in samples to which they were not intentionally added allowed for identification of multiple key modes of error (FIG. 17B). As plotted, a plate without contaminating events or sample swaps should display a simple diagonal pattern with 1-1 matching of expected and observed SDSIs. In some cases, off-diagonal events occur in clear patterns, enabling speculation on the nature of the contamination, clearly demonstrating the utility of SDSIs as an internal control and in-sample label. Applicants observed cases where a plate was likely inverted when SDSI+AmpSeq pool 1 was mixed with pool 2 (FIG. 15B). The SDSI+AmpSeq approach allows researchers to detect entire flawed batches that may not have been flagged with standard controls (as in the case with the plate inversion where water controls in plate corners would not have been affected). In another example, SDSIs were detected unexpectedly throughout a batch, indicating that SDSI (and possibly SARS-CoV-2 and other genetic material) contaminated a common reagent.

[0126] SDSI+AmpSeq also enables fine-resolution insight into sample processing errors with high specificity. In one example, SDSI counts indicated columns were unintentionally mixed together (FIG. 17B). Here, in-sample labeling in all wells allowed researchers to confidently move forward with analyses on unaffected samples. In other cases, samples are associated with both the expected SDSI and SDSIs that were expected in neighboring samples. This indicates a potential spillover event or pipetting errors. Again, genomes generated from samples with suspicious SDSI profiles can be investigated further, and potentially removed from analyses and/or reprocessed. Applicants recommend manual curation of genomes assembled from any samples with <95% of SDSI reads mapping to the expected SDSI. This level of impurity is likely attributable to sample processing contamination, given minimal baseline crosstalk from sources like indexing primer or oligo synthesis observed (Methods, FIG. 25). Moreover, these patterns of contamination events identified via use of SDSI+AmpSeq illuminated key sources of error in processing pipelines and provided an opportunity to improve processing fidelity in subsequent batches.

[0127] To demonstrate the application of the SDSIs for confident interpretation of sequencing data Applicants used SDSI+AmpSeq to investigate a putative SARS-CoV-2 cluster from Massachusetts General Hospital (MGH) for which the Infection Control Unit suspected nosocomial transmission, a context in which both sample swaps and contamination could easily undermine findings. Applicants sequenced 22 samples with SDSI+AmpSeq (14 samples suspected to be part of the cluster based on epidemiological contact-tracing and 8 unlinked samples as controls), within 24 hours and final genomes were assembled within 52 hours of biosample receipt. Of the 11 samples that Applicants assembled genomes from that were suspected to be part of the cluster, 10 were genetically highly similar (0-1 consensus nucleotide difference) (FIG. 17C) and distinct from other samples from Massachusetts around the same time (FIG. 26), strongly suggesting that this cluster did arise from nosocomial transmission. Analysis of the SDSIs confirmed that genome sequence similarity among cluster-associated samples was not the result of cross-contamination (FIG. 17C). Indeed, 23/24 libraries (22 patient samples and 2 water controls) contained >95% SDSI-mapped reads corresponding to the expected SDSI. One sample that was not part of the cluster (MA_MGH_02845) showed 18% of reads from a second SDSI, which was added to a different sample in the batch (MA_MGH_02839). Applicants re-sequenced both samples implicated in the contamination event. Applicants confirmed that the two genome sequences for MA_MGH_02845 were 100% concordant, and no genome was assembled for MA_MGH_02839 in either attempt, likely due to its very low viral load (CT=37). This example illustrates how SDSIs can be used to isolate and validate only those samples implicated in contamination events and altogether increase confidence in cluster investigations.

[0128] To further increase the confidence in AmpSeq methods for viral genomics, Applicants sought to capture contamination and sample swaps that might occur before the cDNA stage. Applicants explored the feasibility of modifying the SDSI approach to enable synthetic RNA spike-ins (SRSI) from the same constructs, which could be added to clinical sample RNA to provide end-to-end quality control. For a subset of SDSIs, Applicants included a T7 promoter site to enable in-vitro production of these constructs as RNAs. For two clinical samples representing low (20) and mid (26) CTs, Applicants detected reads from the RNA spike-ins added directly to extracted viral RNA as a proof of principle (FIG. 27). Notably, this approach did not require any additional protocol modifications, and Applicants therefore expect it to be a highly versatile and user-friendly method when deployed at scale for complete end-to-end sample tracking.

Example 11—Discussion on SDSI+AmpSeq

[0129] Amplicon-based sequencing methods crucially empower rapid, full genome recovery for emerging SARS-CoV-2 variant surveillance; however, robust tools are needed to ensure accuracy in genomic data. SDSI+AmpSeq is a reliable technique for detecting key modes of contamination, addressing this critical gap in standard controls and practices. SDSIs do not compromise genome quality, have been successfully deployed in thousands of clinical samples, and are in use across multiple laboratories with differing protocols. These SDSIs revealed numerous instances of sample swaps and contamination, many of which would go unnoticed with standard batch-level controls. SDSIs further provide critical confidence in the interpretation of clusters of identical genomes, a renewed challenge in the surveillance of more transmissible variants. The common primer design of the SDSI approach enables them to be readily applied to multiple short amplicon designs and sequencing strategies, adding only minor changes to existing protocols and minimal additional cost.

[0130] SDSIs overcome multiple modes of error in the production of amplicon-based genomic sequencing data and are a critical component of quality control measures. The approach is most effective when adopted fully within a laboratory setting and thus Applicants propose routine use of the SDSI+AmpSeq method to flag laboratory-wide contamination. Applicants have implemented SDSI's across diverse approaches and provide an extensively tested protocol with ARTIC v3 and Illumina-based tagmentation. It can also be applied to other sequencing pipelines, though this potentially requires further optimization. The pathogen-exclusion design criteria allows the 96 validated SDSIs to be immediately incorporated into other tiled amplicon panels, such as existing ones for Zika, Ebola, and other viruses of epidemic potential.sup.26,27.

[0131] The SDSI-labeling paradigm is broadly applicable to many amplicon-based needs: amenable to a variety of technical enhancements, flexible to remaining error modes, and expandable to additional targets. One could apply the same design parameters to expand the set of SDSIs, such as to 384 well formats. Additionally, uniquely permuted sets of any size could be created for specific sample batches. To design larger panels of SDSIs, Applicants could use artificial core sequences, rather than excerpting from archaea. Primer sites could also be easily adapted for integration with new advancements in amplicon sequencing, like tailed primer approaches or new primer schemes.sup.38-32. In its current implementation, the SDSIs detect contamination or workflow errors that occur during and after amplification, but not issues arising at the RNA or cDNA generation stage, and act qualitatively, rather than quantitatively. Further refinement of the RNA spike-in approach could address other modes of contamination, enabling end-to-end sample tracking at scale. Future work improving quantification and SDSI analysis pipelines may enable them to serve as within sample controls, since samples or batches with outlier SDSI read counts may reveal missing or defective PCR components, incomplete mixing, thermocycling issues, or other types of experimental error.

[0132] The integration of SDSIs can mitigate a critical vulnerability of amplicon-based sequencing while preserving the many advantages, increasing the robustness of its use across laboratory and clinical settings. Adoption of controls across the viral surveillance community would increase accuracy and integrity of genomic data worldwide. Looking forward, SDSIs could serve as a crucial component in improving data integrity in amplicon based genomic sequencing beyond infectious disease surveillance, such as food safety, species identification and environmental sampling.

Example 12—Methods

SDSI Design and in Silico Validation

[0133] Applicants designed synthetic DNA fragments that each contained a 140 bp unique sequence and constant priming regions. Core SDSI sequence homology to sequences from various organisms was predicted by a permissive BLAST search (blastn; 5000 max targets; E=10; word size=11; no mask for low complexity). Applicants considered homologies identified with this BLASTn search described above that were additionally >50 bps (>35% query cover) and >90% sequence identity to be significant homologies. For all 96 selected SDSIs, there were no such significant homologies when results were filtered to all Homo sapiens (taxid:9606) or viral (taxid:10239) sequences in the NCBI database. For significant homologies to bacterial or eukaryotic sequences in the NCBI database (excluding archaea: taxid:2157), Applicants report both the SDSI and the genus it mapped to in each case (FIG. 18A). Using the same BLASTn parameters, Applicants also mapped SDSIs against a custom database including SDSI core sequences and found no significant homologies between SDSIs. As there were no significant homologies between SDSIs and human, virus, or other SDSI sequences, Applicants noted the maximum alignment scores for any non-significant homology identified and reported these (FIG. 18B).

[0134] Applicants confirmed that SDSI primers and amplicons were predicted to amplify specifically and consistently with ARTIC v3 amplicons. Applicants used Primer-BLAST to predict 50-5000 bp amplicons produced on templates in the entire nr database; no amplicons were identified. Applicants calculated the length and GC content of SDSI primers and full SDSI amplicon sequences and ARTIC v3 primers and amplicons using Geneious Prime (2019.2.1) and compared their distributions (FIG. 19B-C). ARTIC and SDSI primer melting temperatures were matched and calculated using the New England Biolabs online calculator (tmcalculator.neb.com). SDSI experimental validation

[0135] Applicants sought to validate in silico predictions for the performance of the SDSI primers and amplicons. Applicants ordered primers (IDT) (oligo sequences in Supplementary Data File 1) and performed qPCR using the Q5 Hotstart 2× Mastermix, with 500 nM SDSI primers and 0.17×SYBR Gold (ThermoFisher #S11494), and without ARTIC primer pools. Applicants performed this assay in triplicate in 10 μL reactions on a QuantStudio 6 with the following cycling conditions: 95° C. for 30 seconds, followed by 35 cycles of 95° C. for 15 seconds and 65° C. for 5 minutes. Applicants tested 4 conditions: (1) 0.5 μL of an SDSI gene block (IDT) (1 pM), (2) 0.5 μL of an SDSI gene block+0.5 μL of cDNA from an NP swab, (3) 0.5 μL of cDNA from an NP swab, and (4) no template to detect any nonspecific amplification of the primers (FIG. 19A). Applicants performed PCR on each SDSI oligo, using the standard SDSI+AmpSeq PCR conditions (benchling.com/s/prt-R95g0tCxKOeCAqn8lAk3), then ran the PCR products on a 2.2% agarose gel to confirm that these primers amplified the SDSIs and that the product was clean and of the expected size (FIG. 19D).

[0136] Applicants ordered unique oligos as TruGrade ultramers (IDT), then resuspended and stored them at 10 μM in water (oligo sequences in Table 1). Further characterization for identification of 96 SDSIs was achieved by direct PCR amplification with primers containing the constant SDSI handle and an Illumina P5/P7 adapter followed by sequencing with a Mi Seq Nano 2×150 bp kit (Illumina #MS-102-2002). SDSI reads were quantified by mapping each SDSI against other SDSIs with the align_and_count_multiple_report wdl implemented in Terra, as described below, and purity and sequence fidelity of SDSIs was achieved by calculating the percentage of reads mapping to each SDSI out of total SDSI reads (FIG. 14B). Given these same data, Applicants explored the SDSI mapping stringency threshold. Applicants determined whether each SDSI was uniquely identified over a range of SDSI stringency thresholds (0.01%-50% of SDSI reads mapping, with a step size of 0.01%) (FIG. 25). Applicants tested 142 total unique SDSIs; all SDSIs amplified successfully with high sequence fidelity and purity (>95% of reads mapped to the expected SDSI in the experiment described above). The final set of 96 SDSIs were chosen after first pass validation in a combination of clinical sample amplification tests, GC cutoffs, and sequence homology cutoffs. SDSIs excluded because of poor amplification or impurity in clinical sample processing were not retested to determine whether error was technical or biological.

Sample Collection and Study Design

[0137] Research was conducted at the Broad Institute with an exempt determination from the Broad Office of Research Subjects Protections and with approval from the MIT Institutional Review Board under protocol #1612793224. Samples were obtained from Massachusetts General Hospital (MGH), Massachusetts Department of Public Health, the Rhode Island Department of Public Health and the Broad Institute Clinical Research Sequencing Platform. Samples from Massachusetts General Hospital (MGH) fall under Partners Institutional Review Board under protocol #2019P003305. Samples were secondary-use or residual clinical and diagnostic specimens (referred to collectively throughout as clinical samples), obtained by researchers under a waiver of consent. All samples were nasopharyngeal or anterior nares swabs in a stabilizing medium (e.g., MTM or VTM). These unique biological materials are not available to other researchers as they are human patient samples from clinical excess material and thus are of limited volume. Samples sequenced at Jackson Laboratories (JAX) were approved under protocol 2020-NHSR-019-BH.

Viral CT Determination

[0138] Viral cycle threshold (CT) for all samples sequenced at the Broad Institute were obtained using the CDC RT-qPCR assay with the N1 probe as previously described.sup.21. Viral CTs for samples sequenced at JAX were obtained from various providers and thus the RT-qPCR assays used are variable.

CT Normalization

[0139] CT normalization was performed by first setting a desired mock viral CT and calculating the difference between this desired mock viral CT and the measured viral CT of a given sample, rounding to the nearest whole number. Applicants next calculated the number of doublings required for the mock viral CT (assuming 100% PCR efficiency) and multiplied this by the volume of cDNA input to be used for the normalization. The final volume of water used to dilute the cDNA was the doubling factor minus the volume of cDNA input. An example calculation is illustrated below:

Example of CT Normalization:

[0140] N=Difference between actual and mock [0141] X=Volume (μL) of cDNA to use for normalization [0142] DF=Doubling factor is X(2.sup.N) [0143] Volume water for dilution (μL)=DF-X [0144] Actual viral CT=23 [0145] Desired mock viral CT=27 [0146] N=27−23=4 [0147] X=1 μL [0148] DF=1(2.sup.4) [0149] Volume water for dilution (μL)=16−1=15 μL [0150] Add 1 μL of cDNA to 15 μL nuclease free water

[0151] This CT normalization was done for certain method development samples which are described throughout the manuscript as being “mock diluted” or “normalized to CT X”. The nosocomial cluster was normalized to CT 27. The majority of batch data generated at the Broad Institute underwent CT normalization to CT 25. Batch data from JAX did not undergo CT normalization. CT normalization of the cDNA prior to the ARTIC PCR should reduce the potential for generating excessively large libraries from very high viral load samples, keep the percentage of SDSI reads in a detectable range (FIG. 21B), and further reduce the need for additional normalization steps later in the pipeline.

cDNA Generation and ARTIC Amplification Optimization

Reverse Transcriptase

[0152] Applicants tested reverse transcriptase enzymes using extracted RNA from four SARS-CoV-2 positive clinical samples (CTs=13.9, 23.9, 29.6, 33.6) (FIG. 23A,B). Applicants added 2 μL of purified DNase treated RNA as input into SuperScript III (Thermo #18080093), SuperScript IV (Thermo #18091050), or SuperScript IV VILO (Thermo #11756500). Superscript IV (SSIV) reactions incubated at room temperature for 10 minutes, followed by 50° C. for 60 minutes and an inactivation step at 80° C. for 10 min. Superscript IV VILO shared the same protocol, but with a temperature of 85° C. for the inactivation step. Applicants input 2.5 μL of cDNA for ARTIC pool #1 PCR under standard conditions for 40 cycles. Applicants then tested the resulting pool #1 using the scaled down Illumina DNA Flex library construction (as described in Methods below) and sequenced on Illumina Miseq (V2 reagent kit) with 2×150 bp paired end sequencing.

ARTIC PCR Enzyme

[0153] Applicants tested PCR enzyme efficiency using extracted RNA from SARS-CoV-2 positive clinical samples followed by cDNA generation using SuperScript IV and diluted the resulting cDNA to a mock CT value of 35 for standardization across all PCR enzyme tests. Applicants set up the standard ARTIC PCR pool #1 and pool #2 using an input of 2.5 μL, altering only the PCR enzyme and corresponding buffer. Applicants tested NEB Q5 Hot Start High-fidelity 2× Master Mix (Q5 2× MM) (NEB #M0494L), NEB Q5 Hot Start High-fidelity 2× Master Mix plus 0.01% SDS, NEB Q5 Ultra II Master Mix (NEB #M0544L), KAPA HiFi HotStart (Roche #KK2601), and KOD Hot Start DNA polymerase (Sigma-Aldrich #71842) (FIG. 23C). Applicants quantified the resulting ARTIC PCR amplicons using a High Sensitivity DNA Qubit kit, then input 25 ng from each pool (50 ng total) into scaled down Illumina DNA Flex library construction. The resulting libraries (except Q5 plus 0.01% SDS, which had no visible product using the Tapestation D1000 High Sensitivity Kit) were quantified and pooled on Illumina Miseq (V2 reagent kit) with 2×150 paired end sequencing.

Rehybridization PCR

[0154] Applicants optimized PCR cycling conditions on mock CT 35 cDNA (generated as described above) using standard ARTIC PCR primer conditions. Applicants performed a catch-up/rehybridization PCR under the following conditions: 98° C. for 30s, 95° C. for 15s then 65° C. for 5 min (10 cycles), 95° C. for 15s then 80° C. for 30s then 65° C. for 5 min (2 cycles), 95° C. for 15s then 65° C. for 5 min (8 cycles), 4° C. hold (FIG. 23E). Applicants quantified the resulting ARTIC PCR amplicons using a High Sensitivity DNA Qubit kit, then input 25 ng from each pool (50 ng total) into scaled down Illumina DNA Flex library construction. Applicants then quantified these libraries and pooled on Illumina Miseq (V2 reagent kit) with 2×150 paired end sequencing.

Cycle Test

[0155] Applicants further optimized ARTIC PCR by modifying PCR cycle numbers. Extracted RNA from six SARS-CoV-2 positive clinical samples ranging from CT 27-37 were converted to cDNA with Superscript IV and amplified under standard ARTIC PCR reaction components (with Q5 2× MM) modifying the final number of cycles of PCR from 35, 40 and 45 (FIG. 23G). Applicants quantified cDNA and used at a standard 50 ng of input for scaled down Illumina DNA Flex Library Construction, then quantified the resulting libraries and pooled on Illumina Miseq (V2 reagent kit) with 2×150 paired end sequencing.

Ramp Test

[0156] Applicants used mock CT 35 cDNA to test the effect of decreased ramp speed on genome recovery and coverage. ARTIC PCR conditions for this experiment were 98° C. for 30 seconds, followed by 40 cycles of 95° C. for 15 seconds and 65° C. for 5 minutes with a cooling and heating ramping speed of 3° C./s. Applicants tested a slow ramp PCR protocol with the ramp speed reduced to 1.5° C./s (FIG. 23F). Libraries were constructed with Illumina DNA Flex and were sequenced on Illumina Miseq (V2 reagent kit) with 2×150 paired end sequencing.

Primer Concentration Optimization

[0157] Under standard ARTIC protocol conditions, Applicants ordered lyophilized ARTIC v3 primers from IDT and resuspended in water at 100 μM each. Pool #1 primers consisted of all odd numbered amplicons whereas pool #2 primers consisted of all even numbered amplicons. To generate the 100 μM pool #1 primer stock, Applicants combined 5 μL of each 100 μM pool #1 primer, and repeated this protocol for the even numbered primers to give a 100 μM pool #2 primer stock. Applicants selected a total of 20 amplicons as regions of low coverage from previous sequencing data (Table 4). Low coverage amplicons were present in both pools, with 11 coming from pool #1 and 9 coming from pool #2. For the primer 2× pools, Applicants spiked in primers for the corresponding amplicons at 2× the concentration (20.8 nM final) of the other primers in the pool. For these low coverage primers, Applicants used 10 μL of the 100 μM stock rather than 5 μL. Applicants diluted both the original and 2× primer pools 1:10 in nuclease free water to generate a 10 μM working stock. Applicants then selected 8 samples with varying CT values to determine if selectively increasing primer concentrations reduced amplicon dropout (FIG. 23D). Applicants used the SDSI+AmpSeq protocol (without the SDSI or SDSI primers) and processed each sample with both the original primer pool, as well as the 2× primer pool, then sequenced these 16 samples on an Illumina Miseq (V2 reagent kit) with 2×150 paired end sequencing. Only 6 of the 8 samples generated complete genomes (>98%) in both conditions and were used for further analysis.

CT Normalization Experiment

[0158] The CT normalization experiment was performed by taking four individual clinical samples (CT=18-25) with four randomly selected SDSIs and either not normalizing the cDNA or normalizing to CT 25, 26, or 27 prior to the ARTIC PCR (FIG. 21B). Samples were processed with the standard SDSI+AmpSeq protocol described below and were sequenced on a NextSeq 500 Mid Output Kit v2.5 (300 Cycles)

Illumina DNA Flex

[0159] Applicants performed a head-to-head comparison of standard Illumina Nextera DNA Flex and Nextera XT (Illumina #FC-131-1096) library construction kits (FIG. 23H). The Nextera XT protocol was performed as previously described.sup.21,33. Both library construction methods were compared on post ARTIC v1 PCR amplicons from clinical samples. In short, applicants amplified samples with a range of SARS-CoV-2 viral CT values (CTs=22.9, 26.2, 30.3) with ARTIC v1 primers, producing 400 bp size fragments. Applicants then quantified amplicons from each ARTIC primer pool and pooled in equal molar concentrations. Standard Nextera DNA Flex input was 100 ng (50 ng from each pool) and 1 ng (0.5 ng from each pool) for Nextera XT. Applicants quantified and pooled the resulting libraries before sequencing on an Illumina Miseq (V2 reagent kit) with 2×150 paired end sequencing.

[0160] Applicants optimized Illumina DNA Flex library construction (Illumina #20018705) construction with the goal of reducing normalization steps, cost and increasing throughput. Applicants scaled down (0.5×) Illumina DNA Flex throughout the standard Illumina sequencing protocol, also scaling down sample input for a total of 50 ng (25 ng from each primer pool). Due to the CT normalization step, applicants removed the pre-DNA Flex DNA concentration and pooling step. Applicants used 1-2 μL of post ARTIC PCR amplicon as input into the scaled down DNA Flex library construction and performed post library construction quantification and pooling with more uniform library size and concentration, further reducing time and cost of pooling libraries for sequencing. This protocol was used for all method development experiments, the cluster investigation, and a portion of the batch data generated from both the Broad Institute and JAX.

SDSI+AmpSeq SDSI Titration in ARTIC SARS-CoV-2 Sequencing

[0161] To determine an optimal concentration for SDSIs in ARTIC SARS-CoV-2 sequencing, applicants diluted SDSI 49 to 0.6, 6, 60, and 600 copies/μL (1, 0.1, 0.01, and 0.001fM); 1 μL of SDSI 49 was added to 5 μL of cDNA, to be split to 2×3 μL for each ARTIC pool (FIG. 20, Table 1). SDSI primers were added to each ARTIC pool with a final concentration of 40 nM. The cDNA from one clinical sample (MA_MGH_00195; CT=16) was mock diluted to CT 20,25,30, and 35 for this experiment using the protocol described within the CT normalization section. Based on the results of this experiment, SDSIs were used at 6e2 copies/μL (1fM) for all method development data. Batch processing modifications to this approach from the Broad Institute and Jackson Laboratories are detailed below.

SDSI+AmpSeq Protocol

[0162] Full protocol details can be found here: benchling.com/s/prt-R95g0tCxKOeCAqn8lAk3 (FIG. 13). In short, cDNA synthesis is performed on 2.5 μL of DNAse-treated viral RNA with SSIV following the manufacturer's protocol with an extension of the 50° C. incubation from 10 minutes to 60 minutes. An additional cDNA normalization step can be performed (see above) or one can move directly into the ARTIC PCR by taking 5 μL of cDNA and mixing this with 1 uL of a 1fM SDSI (equal to 600 copies/μL). After mixing, split into 2×3 μL aliquots and add ARTIC primer pool 1 or pool 2, as well as 1 μM of the spike-in forward and reverse primers (40 nM final concentration in the ARTIC pool). The ARTIC PCR conditions were 98° C. for 30 seconds, followed by 40 cycles of 95° C. for 15 seconds and 65° C. for 5 minutes. Pool 1 and pool 2 PCR reactions were combined and taken through library construction with scaled down Illumina DNA Flex.

Broad Institute Sample Processing

[0163] The batch data from the Broad Institute was generated using SDSI+AmpSeq with minor modifications (FIG. 16). In short, SSIV was used for cDNA synthesis. Q5 2× MM was used for the ARTIC PCR which was run for 35 cycles. The SDSIs were spiked in at 6e3 copies/μL and the SDSI specific primers were added to each ARTIC pool at a final concentration of 40 nM. Library construction was performed either with the scaled down Illumina DNA Flex (previously described) or COVID-seq (Illumina #20043675). Samples were sequenced on a NovaSeq 6000 SP Reagent Kit v1 (300 cycles) or v1.5 kits (300 cycles), or NextSeq 500 v2 kit (300 cycles).

[0164] The GC percent for each SDSIs and percent SDSI reads over total reads correlation for SDSI (2-48) was performed with the samples sequenced at the Broad Institute (N=2,903) (FIG. 19E). A linear regression was used to evaluate significance (p-value=0.8160).

Jackson Laboratory Sample Processing

[0165] Data generated at Jackson Laboratory (JAX) used two different protocols publicly available here: github.com/tewhey-lab/SARS-CoV-2-Consensus (FIG. 16). All samples included 6e2 copies/μL of SDSIs and the SDSI specific primers were added to each ARTIC pool at a final concentration of 4 nM. Samples processed from December 2020 to April 2021 used Lunascript (NEB #E3010) for cDNA synthesis and Q5 2× MM for the ARTIC PCR which was run for 35 cycles. These samples used scaled down Illumina DNA Flex for library construction. Samples sequenced after April 2021 used the standard COVID-seq protocol. All samples were sequenced on a NextSeq500 using paired 75 bp reads by the Genome Technology group on Jackson Laboratory's Bar Harbor campus. The validation of all SDSIs in clinical samples (FIG. 15A) was performed with this protocol and is presented as the percent of SDSI reads over the total of all reads for each sample.

[0166] Of note, the SDSIs (used at the lowest recommended concentration of 6e2 copies/uL) were reliably detected in the samples sequenced at JAX. This reliable detection however is also dependent on the sequencing depth used by the institution.

SDSI Impact on Genome Recovery

[0167] For +/−SDSI experiments testing impact on recovery of viral genomes, fourteen clinical samples spanning a range of CTs (CT=17.6-30) were selected (FIG. 15B, FIG. 21A). Samples were CT normalized and split after cDNA synthesis into 2×5 μL aliquots. Samples below CT 20 were normalized to CT 25 and samples between CT 20-25 were normalized to CT 26. Fourteen randomly selected SDSIs were used with each sample receiving either an SDSI (600 copies/μL) and the SDSI specific primers (40 nM final concentration in the ARTIC pool) or just the ARTIC pool 1 and pool 2 mastermix with additional nuclease free water and no SDSI primers. Samples were processed according to the SDSI+AmpSeq protocol using scaled down Illumina DNA Flex for library construction, sequenced on a NextSeq 500 Mid Output Kit v2.5 (300 Cycles) and analyzed as described below.

[0168] Statistical analysis for the plus/minus SDSI experiment involved analysis of the mean coverage for all 98 amplicons for the full sample set with a two-tailed Mann Whitney t-test and multiple comparison two-stage step-up Benjamini, Krieger, and Yekutieli test with FDR set to 5%. All 98 amplicons were found to be not significantly different (p-value >0.05) between the plus and minus SDSI group. Samples were also separated into three CT bins (CT<27 (n=4), 27-29 (n=6), CT>30 (n=4)) and this test repeated for each CT bin. This analysis also revealed that there was no significant difference (p-value >0.05) in the mean coverage across any amplicon for any CT bin.

Intentional SDSI Contamination Experiment

[0169] The intentional contamination experiment used SDSI 87 and SDSI 94 (SDSI 87: SDSI 94). The SDSIs were mixed at five different proportions (100:0, 75:25, 50:50, 25:75, and 0:100) (FIG. 17A). Each condition was performed in duplicate. All validation experiment samples were processed according to the SDSI+AmpSeq protocol using scaled down Illumina DNA Flex for library construction. Samples were processed with the standard SDSI+AmpSeq protocol and sequenced on a NextSeq 500 Mid Output Kit v2.5 (300 Cycles).

Production and Application of Synthetic RNA Spike-Ins (SRSI)

[0170] Applicants ordered SDSI oligos with minor modifications to enable in-vitro transcription of RNAs (including a T7 promoter upstream of the SDSI amplicon, as well as 17 bps of constant sequence within the primer region) (Twist Bioscience) (sequences in attached Sup Data File 1). For two SDSIs (SDSI 1 and SDSI 4) applicants in-vitro transcribed RNA using a T7 transcription kit (NEB E2050), quantified by RNA screen tape (Agilent 5067-5579 and 5067-5580), then diluted in water to 10fM (6,000 copies/μL), 1fM (600 copies/μL), 100 aM (60 copies/μL), and 10 aM (6 copies/μL). Applicants added 1 μL of SRSI at each concentration directly to 5 μL of RNA from two patient samples with high and intermediate viral loads, respectively, and prepared sequencing libraries using the SDSI+AmpSeq protocol (without the SDSI addition step at the cDNA stage). For the sample with a high viral load, applicants performed a dilution at the cDNA stage (diluting 32-fold for a mock Ct of 25 rather than 20). Reads mapping to unique SDSI sequences and SARS-CoV-2 were quantified using the align_and_count_multiple_report and assemble_refbased wdls respectively, and % SDSI/total reads was reported (FIG. 27).

Computational Analysis Workflow

[0171] Applicants analyzed sequencing data on the Terra platform (app.terra.bio) using viral-ngs 2.1.28 with workflows that are publicly available on the Dockstore Tool Repository Service (dockstore.org/organizations/BroadInstitute/collections/pgs). Samples were demultiplexed using the demux_plus workflow with a spike in database file for the SDSIs. Applicants performed any separate analyses to quantify read counts, including those for SDSIs, with the align_and_count_multiple_report workflow with the relevant database. For most analyses involving direct comparisons between samples, applicants performed downsampling to the lowest number of reads passing filter with the downsample workflow. Applicants performed assembly using the assemble_refbased workflow to the following reference fasta: www.ncbi.nlm.nih.gov/nuccore/NC_045512.2?report=fasta. Applicants used iVar version 1.2.1 for primer trimming on all samples followed by assembly with minimap2 set to a minimum coverage of either 3, 10, or 20, skipping deduplication procedures. The computational pipeline for all samples sequenced at JAX is publicly available at the following: github.com/tewhey-lab/SARS-CoV-2-Consensus.

[0172] Samples from the batch data were subset in the following way for analysis. All samples with a present SDSI were used for the percent of SDSI reads out of the sum of all SDSI reads analysis (JAX: N=3,838, Broad: N=2,903). Samples with known experimental contamination errors or where the dominant (>50%) SDSI was not the correct SDSI were removed. For the percent of SDSI reads over the total of all sequenced reads analysis (JAX: N=3,093, Broad: N=2,670), non-template controls (waters) and clinical samples with no detectable viral load (CT>40 or not detected via qPCR as described above) were removed from analysis.

Metagenomic Sequencing and Comparison

[0173] Metagenomic sequencing data and genome assemblies used for the comparison of amplicon-based sequencing were prepared, sequenced, analyzed as described previously,.sup.21 and the data are publicly available at NCBI's GenBank and SRA databases under BioProject PRJNA622837. Applicants prepared amplicon sequencing libraries from the sample RNA extract following the SDSI+AmpSeq protocol (FIG. 13). In order to increase sample throughput and bypass an additional more laborious quantification step post the ARTIC PCR, applicants normalized cDNA samples that had a high viral load (CT<27) to a CT of 27. To prepare for the ARTIC PCR, applicants transferred 5 μL of the normalized cDNA to a new plate and added 1 μL of a SDSI (600 copies/μL). After mixing, applicants transferred 3 μL to a new plate, added ARTIC PCR pool #1 mastermix and pool #2 mastermix to the respective plates, and on a thermal cycler incubated at 98° C. for 30s, followed by 40 cycles of 95° C. for 15s and 65° C. for 5 min. Applicants then combined in equal molar amounts of amplified samples for a total of 50 ng and processed through 0.5× Illumina Flex library construction pipeline. Applicants sequenced the concordance data set on a NovaSeq 6000 SP Reagent Kit v1 (300 cycles) and analyzed as detailed in the methods below. For SNV analysis, the coverage depth over each divergent SNV was greater than 1000× for both platforms, and both SNV calls persisted at relaxed (n=3) and conservative (n=20) minimum coverage thresholds. Primer trimming using iVar version 1.2.1 was manually confirmed.

Suspected Nosocomial Cluster Investigation

[0174] Applicants received NP swab samples in UTM and extracted RNA from 200 μL of biosample as previously described.sup.21. Applicants prepared amplicon sequencing libraries as described above and analyzed them as detailed in the methods below. A pairwise distance was calculated between all partial genomes (>80% complete), excluding gaps, to determine whether samples were likely to be the result of nosocomial transmission (FIG. 17C). Applicants calculated the proportion of reads that mapped to a given SDSI out of all reads that mapped to any SDSI. Data has been made available in both the Short Read Archive and NCBI GenBank under Bioproject PRJNA622837. GenBank accessions for SARS-CoV-2 genomes from this set of samples are MW454553-MW454562.

[0175] For phylogenetic tree reconstruction applicants placed the suspected nosocomial cluster in a broader genomic context by performing a subsampling of the genome sequences available in GISAID (as of Jan. 26, 2021) (FIG. 26). Applicants used the sarscov2_nextstrain workflow to perform a Massachusetts-weighted subsampling of samples from 1 Jan. 2020-1 Nov. 2020. Applicants' sub sampled dataset included 3146 sequences; 1449 samples from Massachusetts, 1425 samples from elsewhere in the United States and 283 from other countries. Applicants constructed a maximum likelihood tree using iqtree with a GTR substitution model and edited and interpreted the tree in Figtree v1.4.4.

Data Presentation

[0176] Data analysis and graphing was performed using R Statistical Software (version 1.3.959; R Foundation for Statistical Computing, Vienna, Austria), GraphPad PRISM (version 9.0.2; GraphPad Software, La Jolla Calif. USA, www.graphpad.com) and Python (version 3.7). Applicants created original figures using BioRender (BioRender.com).

Code Availability

[0177] Viral genomes were processed using the Terra platform (app.terra.bio) using viral-ngs 2.1.1 with workflows that are publicly available on the Dockstore Tool Repository Service (dockstore.org/organizations/BroadInstitute/collections/pgs). Downstream analyses were performed using Geneious or standard R packages. Custom scripts used to generate figures are available upon request.

Methods

Data Availability

[0178] Sequences and genome assembly data are publicly available on NCBI's Genbank and SRA databases under BioProject PRJNA622837. GenBank accessions for SARS-CoV-2 genomes newly reported in this study are MW454553-MW454562.

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Performance of Targeted Library Preparation Solutions for SARS-CoV-2 Whole Genome Analysis. Diagnostics (Basel, Switzerland), 10(10). doi. org/10.3390/diagnostics10100769 [0220] 42. Mathieu-Daudé, F., Welsh, J., Vogt, T., & McClelland, M. (1996). DNA Rehybridization During PCR: The “C o t Effect” and Its Consequences. Nucleic Acids Research, 24(11), 2080-2086. [0221] 43. Metsky, H. C., Siddle, K. J., Gladden-Young, A., Qu, J., Yang, D. K., Brehio, P., Goldfarb, A., Piantadosi, A., Wohl, S., Carter, A., Lin, A. E., Barnes, K. G., Tully, D. C., Corleis, B., Hennigan, S., Barbosa-Lima, G., Vieira, Y. R., Paul, L. M., Tan, A. L., . . . Matranga, C. B. (2019). Capturing sequence diversity in metagenomes with comprehensive and scalable probe design. Nature Biotechnology, 37(2), 160-168. [0222] 44. No, J. S., Kim, W.-K., Cho, S., Lee, S.-H., Kim, J.-A., Lee, D., Song, D. H., Gu, S. H., Jeong, S. T., Wiley, M. R., Palacios, G., & Song, J.-W. (2019). Comparison of targeted next-generation sequencing for whole-genome sequencing of Hantaan orthohantavirus in Apodemus agrarius lung tissues. Scientific Reports, 9(1), 16631. [0223] 45. Popa, A., Genger, J.-W., Nicholson, M. D., Penz, T., Schmid, D., Aberle, S. W., Agerer, B., Lercher, A., Endler, L., Colaço, H., Smyth, M., Schuster, M., Grau, M. L., Martinez-Jiménez, F., Pich, O., Borena, W., Pawelka, E., Keszei, Z., Senekowitsch, M., . . . Bergthaler, A. (2020). Genomic epidemiology of superspreading events in Austria reveals mutational dynamics and transmission properties of SARS-CoV-2. Science Translational Medicine, 12(573). doi.org/10.1126/scitranslmed.abe2555 [0224] 46. Quick, J., Grubaugh, N. D., Pullan, S. T., Claro, I. M., Smith, A. D., Gangavarapu, K., Oliveira, G., Robles-Sikisaka, R., Rogers, T. F., Beutler, N. A., Burton, D. R., Lewis-Ximenez, L. L., de Jesus, J. G., Giovanetti, M., Hill, S. C., Black, A., Bedford, T., Carroll, M. W., Nunes, M., . . . Loman, N. J. (2017). Multiplex PCR method for MinION and Illumina sequencing of Zika and other virus genomes directly from clinical samples. Nature Protocols, 12(6), 1261-1276. [0225] 47. SARS-CoV-2 COVID-19 Coronavirus research and surveillance. (n.d.). Retrieved Feb. 12, 2021, from www.paragongenomics.com/product/cleanplex-sars-cov-2-panel/Sethuraman, N., Jeremiah, S. S., & Ryo, A. (2020). Interpreting Diagnostic Tests for SARS-CoV-2. JAMA: The Journal of the American Medical Association, 323(22), 2249-2251. [0226] 48. Volz, E., Mishra, S., Chand, M., Barrett, J. C., Johnson, R., Geidelberg, L., Hinsley, W. R., Laydon, D. J., Dabrera, G., O'Toole, Á., Amato, R., Ragonnet-Cronin, M., Harrison, I., Jackson, B., Ariani, C. V., Boyd, O., Loman, N. J., McCrone, J. T., Gonsalves, S., . . . The COVID-19 Genomics UK (COG-UK) consortium. (2021). Transmission of SARS-CoV-2 Lineage B.1.1.7 in England: Insights from linking epidemiological and genetic data. In bioRxiv. medRxiv. doi.org/10.1101/2020.12.30.20249034

TABLES

[0227]

TABLE-US-00003 TABLE 2 Non-limiting set of designed spike-ins. Nonarchaeal genuses with significant Oligo ID Sequence to order (5′ to 3′) homology* SDSI forward TCTCCTTCTTAGCTTCGTGAGAAC (SEQ ID NO: 391) n/a primer SDSI reverse CTTGGTCGTCTACTACATGATGTG (SEQ ID NO: 392) n/a primer SDSI 1 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGACCGGACGTTGTGATCACGGG none TACCTTGATCTGGTACTCAAAGGTTTGCCCCCGTGAAGTCTGGTACATGGCT AGACACGTCACTCCATTCGAGGGACATTCGAAGTTAGAGAAGGGCAGAGC GATACATCAGATATATCACATCATGTAGTAGACGACCAAGACAGT (SEQ ID NO: 393) SDSI 2 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCTTAATGGAAAGTATGCTTTAGA none TACCTTCTGGAACGCTATCTCACTTGGCGGGAATTCAGATATGGAGAGTAA ATTAAGGGATCTGGAAGTAAAGTTAATGTCGTTAATCTATTTAAATGAGTC ACCATTAAAATCACCCACATCATGTAGTAGACGACCAAGACAGT (SEQ ID NO: 394) SDSI 3 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCATAATATGTTAGAGGTAGAATT none TCTTTGTGATAGAATATTATTGATGAATGATGGAAGAGAATTAGCATTAGG AAAACCTAAGGAACTGGTAAAGGATACAGAATCTAAGAATCTTGAAGAGG TTTTCCTTAAACTTGTCACATCATGTAGTAGACGACCAAGACAGT (SEQ ID NO: 395) SDSI 4 ACAGTTCTCCTTCTTAGCTTCGTGAGAACAGTCTAGGTTTTAATTCTTCAAC none TGCTTCAAATACTAGCTTACTGTAGTTATCTGCCCTCATGTTAGGATATATA TCTGGAATATAAGGAGGTTGATGAGTTATAAGAAGTGGATGAAATTGTTGT CACACACTCCCCTACACATCATGTAGTAGACGACCAAGACAGT (SEQ ID NO: 396) SDSI 5 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCTCGTAAGCGTTTCCTACCCTCG none AGAGGGCCATCCTGGTGGTGAGGAAGTCGTCGAAGTGGGCTAAGTAAAAA GCGAAGATCTCGACCCACAATTACCTCCTCCTGTACACCAGGAATACCCCT ATCAGGATAGAGATACCACATCATGTAGTAGACGACCAAGACAGT (SEQ ID NO: 397) SDSI 6 ACAGTTCTCCTTCTTAGCTTCGTGAGAACTCACGGTCCGCGACGTGAATCG none GGCGTTCCAGTCGGCGTTCGGCTACGACGCCGACGACGTGGTCGGAAGCG ACCTCCTCGGGCGAATCGTGCCCCCGGTGCCGGACCCGGACCCGGTGCCGG AACCGGGGGACGACGAGCACATCATGTAGTAGACGACCAAGACAGT (SEQ ID NO: 398) SDSI 7 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGCGTCCGCGAGTTCATCCTGAAC none GTCGTCCCGCTGTCGCCCGGCGAGGAGCGCGGGGCGGGCTACGCCATCTAC ACCGACATCACGGAGCGGAAGACCCGCGAAAGCGAGCTAGAGCGACAGA ACGAGCGATTGGAGGAGCACATCATGTAGTAGACGACCAAGACAGT (SEQ ID NO: 399) SDSI 8 ACAGTTCTCCTTCTTAGCTTCGTGAGAACACGAACTCGTCGGTGAACATCTC none GTCTTCCGGGGAGCCCGCCGCTCATGGCCTGCCCCCGCCGTAAGCTGCTGC ATAAACCCGCTCCAAAATATACGGATCATTCACCCCTTGGAATCGCTCAAT CAGATCAATGTACACCACATCATGTAGTAGACGACCAAGACAGT (SEQ ID NO: 400) SDSI 9 ACAGTTCTCCTTCTTAGCTTCGTGAGAACTGCGTACATTCCCCCTAAGCGGC none TCCCAATATACAGACGCCGGTTAACGACAGCTGGCGACCCTGTGATCTCAG TACCGGTGTCGAATGACCACATCAGCTTGCCTGTCCGTGCATGGAGTTCGT ATACGTACCCGTCGTCACATCATGTAGTAGACGACCAAGACAGT (SEQ ID NO: 401) SDSI 10 ACAGTTCTCCTTCTTAGCTTCGTGAGAACATACACCACCCCATCAGCAACA none ACTGAATCATGATTAAGTATCGCACCAGCATCGTAGCGCCAGCGTTCACTG CCAGTGGTGCTATCGAATGCATAGAAGATATGCTCCTAATCGCCAATATCA GTACTTCACAAAGCCGCACATCATGTAGTAGACGACCAAGACAGT (SEQ ID NO: 402) SDSI 11 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGTGGAGTCTTTTGTCACACCGCA none GAGGCGTAGCGCTGCAGAGCAGGAGCCCAAGCCTACTGCCAACATAGAGA ACATAGTGGCTACAGTATCCCTCGACCAGACTCTAGACCTGAACCTCATAG AGAGGAGCATACTGACCACATCATGTAGTAGACGACCAAGACAGT (SEQ ID NO: 403) SDSI 12 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCGTCGCCTGGGTTAAGAGGATG none TTCGGCCTCTCCAAGGCGGGTCACGGAGGCACGCTGGACCCGAAGGTCAC CGGCGTCCTCCCCGTAGCCCTGGAGGAAGCAACCAAGGTCATAGGCCTGGT GGTGCACACGAGCAAGGCACATCATGTAGTAGACGACCAAGACAGT (SEQ ID NO: 404) SDSI 13 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCGTGGGCGAGATCTACCAGAGG none CCGCCGCTCCGCAGCAGTGTTAAGAGAAGCCTCCGCGTCAAGAGGATATA CGAGATAGAGCTGCTGGAGTACAACGGCAGGTACGCGCTCATGAGGGTGC TCTGCGAGGCCGGCACATCACATCATGTAGTAGACGACCAAGACAGT (SEQ ID NO: 405) SDSI 14 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCGCTGGAAGAACGAGGGCAAGG none AGGACCTGCTGCGGAGCTACATCAAGCCCGTCGAGTACGCCGTGAGCCAC CTGCCCAAGATAGTTATACGCGATACCGCGGTGGACGCCATAGCCCATGGC GCGAACCTCGCGGTGCCCACATCATGTAGTAGACGACCAAGACAGT (SEQ ID NO: 406) SDSI 15 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGGGAGACCCCAAGGTGACCGGC none GTCCTACCAGTGGGGCTCGCCAACAGCACCAAGGTCATTGGTAATGTTATA CATAGTGTTAAAGAATACGTGATGGTTATACAGCTCCACGGCGATGTAGCC GAGCAGGATTTAAGAACACATCATGTAGTAGACGACCAAGACAGT (SEQ ID NO: 407) SDSI 16 ACAGTTCTCCTTCTTAGCTTCGTGAGAACTAGAGGGAAAGACTGTAGCTTT none CATTCCTAGGCACGGAAAGAGACACAGAATACCTCCACATAAGATAAATT ATAGAGCTAATATATGGGCATTAAAAGAACTAGGAGTGAAATGGGTCATC TCAGTTTCTGCCGTAGGACACATCATGTAGTAGACGACCAAGACAGT (SEQ ID NO: 408) SDSI 17 ACAGTTCTCCTTCTTAGCTTCGTGAGAACTGAGGGAGCTCAGGAGGACTCG none CACGGGGCCCTACAGGGAGGATGAGACACTTGTAAGGCTCCAGGACGTCA GCGAGGCCCTGCTCCTGTGGAGGAGCAACGGGGATGAGAGGTATCTTAGA CGCATCGTGCTACCCGTTCACATCATGTAGTAGACGACCAAGACAGT (SEQ ID NO: 409) SDSI 18 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGAAACATCTATCGCCCACCTCCC none GAAGATAATGATCTTGGATACAGCTGTCGACGCCATAGCACATGGTGCCAA CCTGGCTGCCCCAGGCGTCGCCAGGTTAACCAGGAACATCGCGAAGGGTA GTACCGTAGCGATCCTCACATCATGTAGTAGACGACCAAGACAGT (SEQ ID NO: 410) SDSI 19 ACAGTTCTCCTTCTTAGCTTCGTGAGAACTCGCTATCCCCGTGTACAGCATG none GTGGGGGTGCCGATGCCCGGGTAGAACTTGGTGACGCTCTCCAGCTTCTCG AGGACGGTTTCCTTGGGGAGGCTCGCGGTGTCCACGAGGGTTATCGCGTCC TCGGCGCCGTCGCCGCACATCATGTAGTAGACGACCAAGACAGT (SEQ ID NO: 411) SDSI 20 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCGAGGACGCGAAGAGCGCGGTG none GATGTGGACGCGCCGCCGCACACGTAGCCGTCGAGGTAGCGCGGAACCAT CGGCGACATCAGCCCCACGACGCGACCCGAGGCGTTGCCGAGGATCACGT CGAGCGTCACGCGCGGCACACATCATGTAGTAGACGACCAAGACAGT (SEQ ID NO: 412) SDSI 21 ACAGTTCTCCTTCTTAGCTTCGTGAGAACTCTATGGTGTAGAACGGGTCGTT none GCGGAGCCAGCCTGGCGGCACGTACCGGTCGTCCGCTATCGCCAGCGATCT CTCGAAGAGGTCGAGGTAGGCGGACGCGTTGGCGAACGCCCCGTGTATCA CGACGTCTATCCCGCCCACATCATGTAGTAGACGACCAAGACAGT (SEQ ID NO: 413) SDSI 22 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCCTACGCCGGGTGCGTAGGAGG none GCTCGAGTACATCCATGTCTATACTGATGTATGTTTTACCCAGGTCGCCTAG TGCCAGGGGTCCCTTTAACGCTTCCAGGATAGAGTACACGGTGACGTCTCT AGTCTTCTTCAAGAACACATCATGTAGTAGACGACCAAGACAGT (SEQ ID NO: 414) SDSI 23 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCTACTAGCGTGTCAACGGAGCTC none TTCAACGCCTTTACTATTGGATAGGTTATAAGGTGCTCGCCTCCGAGGAAT CCCAGGAGCATGCCGGGATACTCGTCTACAACGCCTTTCACCACGTCACCT ATGATTCTTAAAGAGCACATCATGTAGTAGACGACCAAGACAGT (SEQ ID NO: 415) SDSI 24 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCATAGGTGACATGGGGTTTCCCA none TTGACTCTATAAAGCCGTATCCTTTAAGCGGAGTGCAATTGGTCTACGCTTT GCTTAACAACAGGTATTTCCTACCGGGTAGAGAGGGCTCGCTCATAGCTTT AGGTAGCGTGACGGCACATCATGTAGTAGACGACCAAGACAGT (SEQ ID NO: 416) SDSI 25 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGGTATCTCACCGCTTGTCACCAT none AGTATCCCTCAGGTACTCCAGTATTCTTGAGAGAAACGCACCTAAGCCGGA TCTCAGGTTTGAATCCATAAGAACTATGAGTGAAGCGGGATTGAAGCCCCT GCTGTTTCTAAGACCCACATCATGTAGTAGACGACCAAGACAGT (SEQ ID NO: 417) SDSI 26 ACAGTTCTCCTTCTTAGCTTCGTGAGAACTAAGGGAGATAGAGAAACGCAT none CAAAATACCCTTGGGGAAACTGCGTGCAGGGGTTCAATATGGAGTAGAGG TCTCAGACATAAAGGAGAAGATAGCTGCTTACGCTAGGAGGAAGGGGCTT AAATACTTCCCATCGGCACACATCATGTAGTAGACGACCAAGACAGT (SEQ ID NO: 418) SDSI 27 ACAGTTCTCCTTCTTAGCTTCGTGAGAACTGTGAACCTCGTGCCCGGCTCTA none AGTCGTGAGGGCTTGCAACATAGGTGGGGAGGAACCCGAGCAACGGGTAA GAAGACAGGATAAGCGGTATCGCTATGAAGAGGGCTGAGAAAAGGACATA TACTCCTGAGCCCGTCCCACATCATGTAGTAGACGACCAAGACAGT (SEQ ID NO: 419) SDSI 28 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCGAACATGCCTTCCCCGTCTATA none TAGACCCAGTAGAGTTTAAAAACTTAACCAGAGACGGCTTGTGAGCCGGAT CTCTCCCCCGCTAGGCCCTGGATTGGGCTCGCTCCTCCTGGGACCCCGGCCT CCACATGCTCGGGACACATCATGTAGTAGACGACCAAGACAGT (SEQ ID NO: 420) SDSI 29 ACAGTTCTCCTTCTTAGCTTCGTGAGAACTCTCGGTTCGGCAATAAGTAATA mesorhizobium; CCAACGAGGTATTACCATGCGCGTGACCAGCAAAGGCCAAGTGACGATCC neorhizobium CAAAGGAGATACGGGATCATTTGGGGATTGGGCCGGGCTCCGAGGTGGAG TTCGTGCCCACAGACGACACATCATGTAGTAGACGACCAAGACAGT (SEQ ID NO: 421) SDSI 30 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCTCGATCATATGGCCGGCACGTT mesorhizobium; GGACTTGGGAGGCATGACAACGGACGAGTATATGGAGTGGCTGAGGGGTC neorhizobium; CACGTGAAGATCTCGACATTGATTGACACAAATGTCCTGATCGATGTTTGG rhizobium; GGTCCTGCCGGACAGGCACATCATGTAGTAGACGACCAAGACAGT (SEQ ID neorhizobium; NO: 422) aminobacter; sinorhizobium; shinella; SDSI 31 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCAGGTGTATTTTACACACCTGGA ‘uncultured bacteria’ CAGCCAGCATATGATGCTAGCACTCGGTGTCCCCTTATCACGGTTTCCCGC ATTGTAAAGTTTTCGCGCCTGCTGCGCCCCGTAGGGCCTGGATTCATGTCTC AGAATCCATCTCCGCACATCATGTAGTAGACGACCAAGACAGT (SEQ ID NO: 423) SDSI 32 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCGTAGCCCGCACCTTCCTCTGGT ‘uncultured bacteria’ TTAGCACCAGCGGTCCCCACAGAGTACCCATCATCCCGAAGGATATGCTGG CAACAGTGGGCACGGGTCTCGCTCGTTGCCTGACTTAACAGGATGCTTCAC AGTACGAACTGACGACACATCATGTAGTAGACGACCAAGACAGT (SEQ ID NO: 424) SDSI 33 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGAAACTTACCTTATCAGTGTCAT none TAAGCATATTGCTTCCAAGACCCATTGAAGCACTTACATCGTTGATACACA GGTGCCAGGAATAGTATTCCTCAGTCTCACTATAATCCTCGTTGGTGTAGCC TTCAAGAGAGTCAACACATCATGTAGTAGACGACCAAGACAGT (SEQ ID NO: 425) SDSI 34 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGTTTAAGCAATTCTTCGGATGAA none AGATGGCGCTCTATAGGAATTTGTTCTGGTCTAGCCATAAGGCATTATTTGT ACTTAATTAGTAATAAATGTTTAGTTAATGACTATAAATCTGCAATTGGAG TCTCAAATTTTCAACACATCATGTAGTAGACGACCAAGACAGT (SEQ ID NO: 426) SDSI 35 ACAGTTCTCCTTCTTAGCTTCGTGAGAACAACATGAAGGATGTGTGTAAGA none GGAAACGTTATTAACAGACGTAATCAGGAGGATAGTTATGCCCTAAAAAC AGCAGAGTTAAGGTTTAAAAATAAGATAAGAACTCAGTTGAGGTTTATCCA TTAATCCCATTAATCCTCACATCATGTAGTAGACGACCAAGACAGT (SEQ ID NO: 427) SDSI 36 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGTATCCGCTGATATATCCTGGGG none ATATAGATCGCTCTGAAATGGTTACATCTATCGGTTTTAAGGACAGTTCCA ACACTATTGGACCTTGCAGCTATGACAGGAATAATCTGTTTATCGAGCACA GTTGAATTTGACCTACACATCATGTAGTAGACGACCAAGACAGT (SEQ ID NO: 428) SDSI 37 ACAGTTCTCCTTCTTAGCTTCGTGAGAACATATTCCGTATTTCTTATCAAAC none CGATCGTGAAGATTTGACAAAGGCTTAACTTTAGGGCTCCACTTCTCATTAT TAGCCTTAGAATATAAAGCGTAACCGTAAGCCTGAGGAACGTAAAGCTTA GGAGATTCAATCCCGCACATCATGTAGTAGACGACCAAGACAGT (SEQ ID NO: 429) SDSI 38 ACAGTTCTCCTTCTTAGCTTCGTGAGAACTAAAATTAGCCGAAGGCTTCCC none ATTACCGAAAAAGTCGTTTATTAGCTCTTCATCCTTCTTCTCCACGTCCGCC CATTCCTCTCCTTCCCTTGGAATTTTAAGCTCGTCCCAGCTGACTCTTATGG GCAATTCAATATCCCACATCATGTAGTAGACGACCAAGACAGT (SEQ ID NO: 430) SDSI 39 ACAGTTCTCCTTCTTAGCTTCGTGAGAACTCCGGAGGAATCTATCATATTAA none ACCTCCTCAAAATCGCCTCCTCTTGATTGCTTAAAGGCTGTGAATTACAAA GCTTATTTAATGCGTCCCAAAGCGTTAAGTAATAATTATTTATATTAAACAC TACTATTTCAGTAGCACATCATGTAGTAGACGACCAAGACAGT (SEQ ID NO: 431) SDSI 40 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGTTCCTCCTCAATTCAATTGGAC none TGAAGGAGGGTACGTTCTGGAAAACAGAGCGTAAAAGAGATATAGAACGT AGTATACACATAGCTGGAAAAAGAACAATCATTAAGACAATAAAGAACTT TATGGAAAAGAGTAGAACACATCATGTAGTAGACGACCAAGACAGT (SEQ ID NO: 432) SDSI 41 ACAGTTCTCCTTCTTAGCTTCGTGAGAACTCGTGTAAAGGTTGTATAATTCA none AGCCTCAGAACATTTCGAACTCCTTACAAAATCGTTTAAACTTTCTAAGGC ATAAATTTACTAGAAATTGTCATTTATGAGAATGTAACTATATAGATGGTA AAATTATTAATCCTCCACATCATGTAGTAGACGACCAAGACAGT (SEQ ID NO: 433) SDSI 42 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGGCTGAAAAATAGGTTCGATCC none GCCTCCTCACTTCTTCTCCTTCTTGCCCTCGGCCTCGGAGGAGGCCTCTATT CCCAGCTTCTTGGCCTCCTCCTCGGTCGTCATGAACAGGCTAGTCCTCTGCC TTCCGCCCATGCTCCACATCATGTAGTAGACGACCAAGACAGT (SEQ ID NO: 434) SDSI 43 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGTTCAGCATAAAAGACGGTTTC none ACGGGCCAAAGCCTAAGCGGCGTAACGGTGAAAGAAGGAGATACGGTTTT GGGCACGATTGACGACGGCGGGACGCTGGAGCTCACGAGGGGCACTCACA CCTTGACTTTCGAGAAGCCACATCATGTAGTAGACGACCAAGACAGT (SEQ ID NO: 435) SDSI 44 ACAGTTCTCCTTCTTAGCTTCGTGAGAACCTGATGTTATAGAAGTCCGCAA none GGACGGCTCTGTCATCTCGCCCGAGGGTGGGAAATACTATCTCGGCGACAT AAGCGGCCCGACACAAATTAGCATCAAGTTCAAGGCCGGCGCGGTGGGAA CCCACGGCTTCACTATCCACATCATGTAGTAGACGACCAAGACAGT (SEQ ID NO: 436) SDSI 45 ACAGTTCTCCTTCTTAGCTTCGTGAGAACTCTCCCTCAACCTTCGCGGGGAG none AACGGCGCGGAGTACTGGACGGGCTACGCGGACGCGCTGGAAGACCTGTT GAAGAAAATCCAGAGGCGGGAGGTGAGGGCATGAGAAGGTATTGTTACAT CACGTGGGGATGGATCACACATCATGTAGTAGACGACCAAGACAGT (SEQ ID NO: 437) SDSI 46 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGAGCGCCGGGAGGTGAGGGCAT none GAGTGAGGAATTGATGTTTGGTCGTGTCGTGGAGTATGTTCAGCATAGTTT CTACAAGAAACCGTTTCCTCTTGGCAGTGAGCTCAAGAATGCAGTAGAGAA GGTTATGGAAACAGGACACATCATGTAGTAGACGACCAAGACAGT (SEQ ID NO: 438) SDSI 47 ACAGTTCTCCTTCTTAGCTTCGTGAGAACAGGTCAGAGCCCACGTGGCAAC none TTTTGAGGTTCTGACAAAAGACTATGTTCGTGAGAAATACAAAGACATCAT AGAGTTCATGAGGGAGAAAGGGACAGTATCGAGAAAGGAACTGCGGAAG AAGTTCTTCTTGCTTGCTCACATCATGTAGTAGACGACCAAGACAGT (SEQ ID NO: 439) SDSI 48 ACAGTTCTCCTTCTTAGCTTCGTGAGAACGTACCTCAAAATACAGAATCAT none ATTTTACAATCGCTTGGAAATATTAATATCAACAATACGCAAGTCCAAATT AACGTCCCTGGCAAACAGGTGACAATTTATACCCACGAAATACTAGATAAC GCCAAAAAGGCACTCGCACATCATGTAGTAGACGACCAAGACAGT (SEQ ID NO: 440)

TABLE-US-00004 TABLE 3 SDSI and Viral Read Percentages Mock CT Viral reads % Spike-in reads % 20 99.56 0.18 25 99.19 0.38 30 98.47 1.11 35 99.65 3.17

TABLE-US-00005 TABLE 4 Exemplary ARTIC v3 Primers and Primers Spiked in at 2X Spiked Name Pool Sequence Length % GC in at 2X nCoV-2019_1_LEFT nCoV-2019_1 ACCAACCAACTTTCGATCTCTTGT (SEQ 24 41.7 ID NO: 441) nCoV-2019_1_RIGHT nCoV-2019_1 CATCTTTAAGATGTTGACGTGCCTC (SEQ 25 44.0 ID NO: 442) nCoV-2019_2_LEFT nCoV-2019_2 CTGTTTTACAGGTTCGCGACGT (SEQ ID 22 50.0 NO: 443) nCoV-2019_2_RIGHT nCoV-2019_2 TAAGGATCAGTGCCAAGCTCGT (SEQ ID 22 50.0 NO: 444) nCoV-20193_LEFT nCoV-2019_1 CGGTAATAAAGGAGCTGGTGGC (SEQ ID 22 54.6 NO: 445) nCoV-2019_3_RIGHT nCoV-2019_1 AAGGTGTCTGCAATTCATAGCTCT (SEQ 24 41.7 ID NO: 446) nCoV-2019_4_LEFT nCoV-2019_2 GGTGTATACTGCTGCCGTGAAC (SEQ ID 22 54.6 NO: 447) nCoV-2019_4_RIGHT nCoV-2019_2 CACAAGTAGTGGCACCTTCTTTAGT (SEQ 25 44.0 ID NO: 448) nCoV-2019_5_LEFT nCoV-2019_1 TGGTGAAACTTCATGGCAGACG (SEQ ID 22 50.0 NO: 449) nCoV-2019_5_RIGHT nCoV-2019_1 ATTGATGTTGACTTTCTCTTTTTGGAGT 28 32.1 (SEQ ID NO: 450) nCoV-2019_6_LEFT nCoV-2019_2 GGTGTTGTTGGAGAAGGTTCCG (SEQ ID 22 54.6 NO: 451) nCoV-2019_6_RIGHT nCoV-2019_2 TAGCGGCCTTCTGTAAAACACG (SEQ ID 22 50.0 NO: 452) nCoV-2019_7_LEFT_alt0 nCoV-2019_1 CATTTGCATCAGAGGCTGCTCG (SEQ ID 22 54.6 X NO: 453) nCoV-2019_7_RIGHT_alt5 nCoV-2019_1 AGGTGACAATTTGTCCACCGAC (SEQ ID 22 50.0 X NO: 454) nCoV-2019_8_LEFT nCoV-2019_2 AGAGTTTCTTAGAGACGGTTGGGA (SEQ 24 45.8 ID NO: 455) nCoV-2019_8_RIGHT nCoV-2019_2 GCTTCAACAGCTTCACTAGTAGGT (SEQ 24 45.8 ID NO: 456) nCoV-2019_9_LEFT_alt4 nCoV-2019_1 TTCCCACAGAAGTGTTAACAGAGG (SEQ 24 45.8 X ID NO: 457) nCoV-2019_9_RIGHT_alt2 nCoV-2019_1 GACAGCATCTGCCACAACACAG (SEQ ID 22 54.6 X NO: 458) nCoV-2019_10_LEFT nCoV-2019_2 TGAGAAGTGCTCTGCCTATACAGT (SEQ 24 45.8 ID NO: 459) nCoV-2019_10_RIGHT nCoV-2019_2 TCATCTAACCAATCTTCTTCTTGCTCT 27 37.0 (SEQ ID NO: 460 nCoV-2019_11_LEFT nCoV-2019_1 GGAATTTGGTGCCACTTCTGCT (SEQ ID 22 50.0 NO: 461) nCoV-2019_11_RIGHT nCoV-2019_1 TCATCAGATTCAACTTGCATGGCA (SEQ 24 41.7 ID NO: 462) nCoV-2019_12_LEFT nCoV-2019_2 AAACATGGAGGAGGTGTTGCAG (SEQ ID 22 50.0 X NO: 463) nCoV-2019_12_RIGHT nCoV-2019_2 TTCACTCTTCATTTCCAAAAAGCTTGA 27 33.3 X (SEQ ID NO: 464) nCoV-2019_13_LEFT nCoV-2019_1 TCGCACAAATGTCTACTTAGCTGT (SEQ 24 41.7 ID NO: 465) nCoV-2019_13_RIGHT nCoV-2019_1 ACCACAGCAGTTAAAACACCCT (SEQ ID 22 45.5 NO: 466) nCoV-2019_14_LEFT_alt4 nCoV-2019_2 TGGCAATCTTCATCCAGATTCTGC (SEQ 24 45.8 X ID NO: 467) nCoV-2019_14_RIGHT_alt2 nCoV-2019_2 TGCGTGTTTCTTCTGCATGTGC (SEQ ID 22 50.0 X NO: 468) nCoV-2019_15_LEFT_alt1 nCoV-2019_1 AGTGCTTAAAAAGTGTAAAAGTGCCT 26 34.6 X (SEQ ID NO: 469) nCoV-2019_15_RIGHT_alt3 nCoV-2019_1 ACTGTAGCTGGCACTTTGAGAGA (SEQ 23 47.8 X ID NO: 470) nCoV-2019_16_LEFT nCoV-2019_2 AATTTGGAAGAAGCTGCTCGGT (SEQ ID 22 45.5 NO: 471) nCoV-2019_16_RIGHT nCoV-2019_2 CACAACTTGCGTGTGGAGGTTA (SEQ ID 22 50.0 NO: 472) nCoV-2019_17_LEFT nCoV-2019_1 CTTCTTTCTTTGAGAGAAGTGAGGACT 27 40.7 X (SEQ ID NO: 473) nCoV-2019_17_RIGHT nCoV-2019_1 TTTGTTGGAGTGTTAACAATGCAGT (SEQ 25 36.0 X ID NO: 474) nCoV-2019_18_LEFT_alt2 nCoV-2019_2 ACTTCTATTAAATGGGCAGATAACAACT 30 33.3 X GT (SEQ ID NO: 475) nCoV-2019_18_RIGHT_alt1 nCoV-2019_2 GCTTGTTTACCACACGTACAAGG (SEQ ID 23 47.8 X NO: 476) nCoV-2019_19_LEFT nCoV-2019_1 GCTGTTATGTACATGGGCACACT (SEQ ID 23 47.8 NO: 477) nCoV-2019_19_RIGHT nCoV-2019_1 TGTCCAACTTAGGGTCAATTTCTGT (SEQ 25 40.0 ID NO: 478) nCoV-2019_20_LEFT nCoV-2019_2 ACAAAGAAAACAGTTACACAACAACCA 27 33.3 (SEQ ID NO: 479) nCoV-2019_20_RIGHT nCoV-2019_2 ACGTGGCTTTATTAGTTGCATTGTT (SEQ ID NO: 480) 25 36.0 nCoV-2019_21_LEFT_alt2 nCoV-2019_1 GGCTATTGATTATAAACACTACACACCC 29 37.9 X T (SEQ ID NO: 481 nCoV-2019_21_RIGHT_alt0 nCoV-2019_1 GATCTGTGTGGCCAACCTCTTC (SEQ ID 22 54.6 X NO: 482) nCoV-2019_22_LEFT nCoV-2019_2 ACTACCGAAGTTGTAGGAGACATTATAC 29 37.9 T (SEQ ID NO: 483) nCoV-2019_22_RIGHT nCoV-2019_2 ACAGTATTCTTTGCTATAGTAGTCGGC 27 40.7 (SEQ ID NO: 484) nCoV-201923_LEFT nCoV-2019_1 ACAACTACTAACATAGTTACACGGTGT 27 37.0 (SEQ ID NO: 485) nCoV-201923_RIGHT nCoV-2019_1 ACCAGTACAGTAGGTTGCAATAGTG 25 44.0 (SEQ ID NO: 486) nCoV-2019_24_LEFT nCoV-2019_2 AGGCATGCCTTCTTACTGTACTG (SEQ ID 23 47.8 X NO: 487) nCoV-2019_24_RIGHT nCoV-2019_2 ACATTCTAACCATAGCTGAAATCGGG 26 42.3 X (SEQ ID NO: 488) nCoV-2019_25_LEFT nCoV-2019_1 GCAATTGTTTTTCAGCTATTTTGCAGT 27 33.3 (SEQ ID NO: 489) nCoV-2019_25_RIGHT nCoV-2019_1 ACTGTAGTGACAAGTCTCTCGCA (SEQ ID 23 47.8 NO: 490) nCoV-2019_26_LEFT nCoV-2019_2 TTGTGATACATTCTGTGCTGGTAGT (SEQ 25 40.0 ID NO: 491) nCoV-2019_26_RIGHT nCoV-2019_2 TCCGCACTATCACCAACATCAG (SEQ ID 22 50.0 NO: 492) nCoV-2019_27_LEFT nCoV-2019_1 ACTACAGTCAGCTTATGTGTCAACC (SEQ 25 44.0 ID NO: 493) nCoV-2019_27_RIGHT nCoV-2019_1 AATACAAGCACCAAGGTCACGG (SEQ ID 22 50.0 NO: 494) nCoV-2019_28_LEFT nCoV-2019_2 ACATAGAAGTTACTGGCGATAGTTGT 26 38.5 (SEQ ID NO: 495) nCoV-2019_28_RIGHT nCoV-2019_2 TGTTTAGACATGACATGAACAGGTGT 26 38.5 (SEQ ID NO: 496) nCoV-2019_29_LEFT nCoV-2019_1 ACTTGTGTTCCTTTTTGTTGCTGC (SEQ ID 24 41.7 NO: 497) nCoV-2019_29_RIGHT nCoV-2019_1 AGTGTACTCTATAAGTTTTGATGGTGTGT 29 34.5 (SEQ ID NO: 498) nCoV-2019_30_LEFT nCoV-2019_2 GCACAACTAATGGTGACTTTTTGCA (SEQ 25 40.0 ID NO: 499) nCoV-2019_30_RIGHT nCoV-2019_2 ACCACTAGTAGATACACAAACACCAG 26 42.3 (SEQ ID NO: 500) nCoV-201931_LEFT nCoV-2019_1 TTCTGAGTACTGTAGGCACGGC (SEQ ID 22 54.6 NO: 501) nCoV-2019_31_RIGHT nCoV-2019_1 ACAGAATAAACACCAGGTAAGAATGAG 28 35.7 T (SEQ ID NO: 502) nCoV-2019_32_LEFT nCoV-2019_2 TGGTGAATACAGTCATGTAGTTGCC (SEQ 25 44.0 ID NO: 503) nCoV-2019_32_RIGHT nCoV-2019_2 AGCACATCACTACGCAACTTTAGA (SEQ 24 41.7 ID NO: 504) nCoV-2019_33_LEFT nCoV-2019_1 ACTTTTGAAGAAGCTGCGCTGT (SEQ ID 22 45.5 X NO: 505) nCoV-2019_33_RIGHT nCoV-2019_1 TGGACAGTAAACTACGTCATCAAGC 25 44.0 X (SEQ ID NO: 506) nCoV-2019_34_LEFT nCoV-2019_2 TCCCATCTGGTAAAGTTGAGGGT (SEQ ID 23 47.8 NO: 507) nCoV-2019_34_RIGHT nCoV-2019_2 AGTGAAATTGGGCCTCATAGCA (SEQ ID 22 45.5 NO: 508) nCoV-2019_35_LEFT nCoV-2019_1 TGTTCGCATTCAACCAGGACAG (SEQ ID 22 50.0 NO: 509) nCoV-2019_35_RIGHT nCoV-2019_1 ACTTCATAGCCACAAGGTTAAAGTCA 26 38.5 (SEQ ID NO: 510) nCoV-2019_36_LEFT nCoV-2019_2 TTAGCTTGGTTGTACGCTGCTG (SEQ ID 22 50.0 NO: 511) nCoV-2019_36_RIGHT nCoV-2019_2 GAACAAAGACCATTGAGTACTCTGGA 26 42.3 (SEQ ID NO: 512) nCoV-2019_37_LEFT nCoV-2019_1 ACACACCACTGGTTGTTACTCAC (SEQ ID 23 47.8 NO: 513) nCoV-2019_37_RIGHT nCoV-2019_1 GTCCACACTCTCCTAGCACCAT (SEQ ID 22 54.6 NO: 514) nCoV-2019_38_LEFT nCoV-2019_2 ACTGTGTTATGTATGCATCAGCTGT (SEQ 25 40.0 ID NO: 515) nCoV-2019_38_RIGHT nCoV-2019_2 CACCAAGAGTCAGTCTAAAGTAGCG 25 48.0 (SEQ ID NO: 516) nCoV-2019_39_LEFT nCoV-2019_1 AGTATTGCCCTATTTTCTTCATAACTGGT 29 34.5 (SEQ ID NO: 517) nCoV-2019_39_RIGHT nCoV-2019_1 TGTAACTGGACACATTGAGCCC (SEQ ID 22 50.0 NO: 518) nCoV-2019_40_LEFT nCoV-2019_2 TGCACATCAGTAGTCTTACTCTCAGT 26 42.3 (SEQ ID NO: 519) nCoV-2019_40_RIGHT nCoV-2019_2 CATGGCTGCATCACGGTCAAAT (SEQ ID 22 50.0 NO: 520) nCoV-2019_41_LEFT nCoV-2019_1 GTTCCCTTCCATCATATGCAGCT (SEQ ID 23 47.8 NO: 521) nCoV-2019_41_RIGHT nCoV-2019_1 TGGTATGACAACCATTAGTTTGGCT (SEQ 25 40.0 ID NO: 522) nCoV-2019_42_LEFT nCoV-2019_2 TGCAAGAGATGGTTGTGTTCCC (SEQ ID 22 50.0 NO: 523) nCoV-2019_42_RIGHT nCoV-2019_2 CCTACCTCCCTTTGTTGTGTTGT (SEQ ID 23 47.8 NO: 524) nCoV-2019_43_LEFT nCoV-2019_1 TACGACAGATGTCTTGTGCTGC (SEQ ID 22 50.0 NO: 525) nCoV-2019_43_RIGHT nCoV-2019_1 AGCAGCATCTACAGCAAAAGCA (SEQ ID 22 45.5 NO: 526) nCoV-2019_44_LEFT_alt3 nCoV-2019_2 CCACAGTACGTCTACAAGCTGG (SEQ ID 22 54.6 NO: 527) nCoV-2019_44_RIGHT_alt0 nCoV-2019_2 CGCAGACGGTACAGACTGTGTT (SEQ ID 22 54.6 NO: 528) nCoV-2019_45_LEFT_alt2 nCoV-2019_1 AGTATGTACAAATACCTACAACTTGTGC 29 34.5 X T (SEQ ID NO: 529) nCoV-2019_45_RIGHT_alt7 nCoV-2019_1 TTCATGTTGGTAGTTAGAGAAAGTGTGT 29 37.9 X C (SEQ ID NO: 530) nCoV-2019_46_LEFT_alt1 nCoV-2019_2 CGCTTCCAAGAAAAGGACGAAGA (SEQ 23 47.8 ID NO: 531) nCoV-2019_46_RIGHT_alt2 nCoV-2019_2 CACGTTCACCTAAGTTGGCGTAT (SEQ ID 23 47.8 NO: 532) nCoV-2019_47_LEFT nCoV-2019_1 AGGACTGGTATGATTTTGTAGAAAACCC 28 39.3 (SEQ ID NO: 533) nCoV-2019_47_RIGHT nCoV-2019_1 AATAACGGTCAAAGAGTTTTAACCTCTC 28 35.7 (SEQ ID NO: 534) nCoV-2019_48_LEFT nCoV-2019_2 TGTTGACACTGACTTAACAAAGCCT (SEQ 25 40.0 ID NO: 535) nCoV-2019_48_RIGHT nCoV-2019_2 TAGATTACCAGAAGCAGCGTGC (SEQ ID 22 50.0 NO: 536) nCoV-2019_49_LEFT nCoV-2019_1 AGGAATTACTTGTGTATGCTGCTGA (SEQ 25 40.0 ID NO: 537) nCoV-2019_49_RIGHT nCoV-2019_1 TGACGATGACTTGGTTAGCATTAATACA 28 35.7 (SEQ ID NO: 538) nCoV-2019_50_LEFT nCoV-2019_2 GTTGATAAGTACTTTGATTGTTACGATG 30 33.3 GT (SEQ ID NO: 539) nCoV-2019_50_RIGHT nCoV-2019_2 TAACATGTTGTGCCAACCACCA (SEQ ID 22 45.5 NO: 540) nCoV-2019_51_LEFT nCoV-2019_1 TCAATAGCCGCCACTAGAGGAG (SEQ ID 22 54.6 NO: 541) nCoV-2019_51_RIGHT nCoV-2019_1 AGTGCATTAACATTGGCCGTGA (SEQ ID 22 45.5 NO: 542) nCoV-2019_52_LEFT nCoV-2019_2 CATCAGGAGATGCCACAACTGC (SEQ ID 22 54.6 NO: 543) nCoV-2019_52_RIGHT nCoV-2019_2 GTTGAGAGCAAAATTCATGAGGTCC 25 44.0 (SEQ ID NO: 544) nCoV-2019_53_LEFT nCoV-2019_1 AGCAAAATGTTGGACTGAGACTGA (SEQ 24 41.7 ID NO: 545) nCoV-2019_53_RIGHT nCoV-2019_1 AGCCTCATAAAACTCAGGTTCCC (SEQ ID 23 47.8 NO: 546) nCoV-2019_54_LEFT nCoV-2019_2 TGAGTTAACAGGACACATGTTAGACA 26 38.5 (SEQ ID NO: 547) nCoV-2019_54_RIGHT nCoV-2019_2 AACCAAAAACTTGTCCATTAGCACA 25 36.0 (SEQ ID NO: 548) nCoV-2019_55_LEFT nCoV-2019_1 ACTCAACTTTACTTAGGAGGTATGAGCT (SEQ ID NO: 549) 28 39.3 nCoV-2019_55_RIGHT nCoV-2019_1 GGTGTACTCTCCTATTTGTACTTTACTGT 29 37.9 (SEQ ID NO: 550) nCoV-2019_56_LEFT nCoV-2019_2 ACCTAGACCACCACTTAACCGA (SEQ ID 22 50.0 NO: 551) nCoV-2019_56_RIGHT nCoV-2019_2 ACACTATGCGAGCAGAAGGGTA (SEQ ID 22 50.0 NO: 552) nCoV-2019_57_LEFT nCoV-2019_1 ATTCTACACTCCAGGGACCACC (SEQ ID 22 54.6 NO: 553) nCoV-2019_57_RIGHT nCoV-2019_1 GTAATTGAGCAGGGTCGCCAAT (SEQ ID 22 50.0 NO: 554) nCoV-2019_58_LEFT nCoV-2019_2 TGATTTGAGTGTTGTCAATGCCAGA (SEQ 25 40.0 ID NO: 555) nCoV-2019_58_RIGHT nCoV-2019_2 CTTTTCTCCAAGCAGGGTTACGT (SEQ ID 23 47.8 NO: 556) nCoV-2019_59_LEFT nCoV-2019_1 TCACGCATGATGTTTCATCTGCA (SEQ ID 23 43.5 NO: 557) nCoV-2019_59_RIGHT nCoV-2019_1 AAGAGTCCTGTTACATTTTCAGCTTG 26 38.5 (SEQ ID NO: 558) nCoV-2019_60_LEFT nCoV-2019_2 TGATAGAGACCTTTATGACAAGTTGCA 27 37.0 (SEQ ID NO: 559) nCoV-2019_60_RIGHT nCoV-2019_2 GGTACCAACAGCTTCTCTAGTAGC (SEQ 24 50.0 ID NO: 560) nCoV-2019_61_LEFT nCoV-2019_1 TGTTTATCACCCGCGAAGAAGC (SEQ ID 22 50.0 NO: 561) nCoV-2019_61_RIGHT nCoV-2019_1 ATCACATAGACAACAGGTGCGC (SEQ ID 22 50.0 NO: 562) nCoV-2019_62_LEFT nCoV-2019_2 GGCACATGGCTTTGAGTTGACA (SEQ ID 22 50.0 NO: 563) nCoV-2019_62_RIGHT nCoV-2019_2 GTTGAACCTTTCTACAAGCCGC (SEQ ID 22 50.0 NO: 564) nCoV-2019_63_LEFT nCoV-2019_1 TGTTAAGCGTGTTGACTGGACT (SEQ ID 22 45.5 NO: 565) nCoV-2019_63_RIGHT nCoV-2019_1 ACAAACTGCCACCATCACAACC (SEQ ID 22 50.0 NO: 566) nCoV-2019_64_LEFT nCoV-2019_2 TCGATAGATATCCTGCTAATTCCATTGT 28 35.7 X (SEQ ID NO: 567) nCoV-2019_64_RIGHT nCoV-2019_2 AGTCTTGTAAAAGTGTTCCAGAGGT 25 40.0 X (SEQ ID NO: 568) nCoV-2019_65_LEFT nCoV-2019_1 GCTGGCTTTAGCTTGTGGGTTT (SEQ ID 22 50.0 NO: 569) nCoV-2019_65_RIGHT nCoV-2019_1 TGTCAGTCATAGAACAAACACCAATAGT 28 35.7 (SEQ ID NO: 570) nCoV-2019_66_LEFT nCoV-2019_2 GGGTGTGGACATTGCTGCTAAT (SEQ ID 22 50.0 X NO: 571) nCoV-2019_66_RIGHT nCoV-2019_2 TCAATTTCCATTTGACTCCTGGGT (SEQ 24 41.7 X ID NO: 572) nCoV-2019_67_LEFT nCoV-2019_1 GTTGTCCAACAATTACCTGAAACTTACT 28 35.7 X (SEQ ID NO: 573) nCoV-2019_67_RIGHT nCoV-2019_1 CAACCTTAGAAACTACAGATAAATCTTG 30 36.7 X GG (SEQ ID NO: 574) nCoV-2019_68_LEFT nCoV-2019_2 ACAGGTTCATCTAAGTGTGTGTGT (SEQ 24 41.7 ID NO: 575) nCoV-2019_68_RIGHT nCoV-2019_2 CTCCTTTATCAGAACCAGCACCA (SEQ ID 23 47.8 NO: 576) nCoV-2019_69_LEFT nCoV-2019_1 TGTCGCAAAATATACTCAACTGTGTCA 27 37.0 (SEQ ID NO: 577) nCoV-2019_69_RIGHT nCoV-2019_1 TCTTTATAGCCACGGAACCTCCA (SEQ ID 23 47.8 NO: 578) nCoV-2019_70_LEFT nCoV-2019_2 ACAAAAGAAAATGACTCTAAAGAGGGT 29 31.0 X TT (SEQ ID NO: 579) nCoV-2019_70_RIGHT nCoV-2019_2 TGACCTTCTTTTAAAGACATAACAGCAG 28 35.7 X (SEQ ID NO: 580) nCoV-2019_71_LEFT nCoV-2019_1 ACAAATCCAATTCAGTTGTCTTCCTATTC 29 34.5 X (SEQ ID NO: 581) nCoV-2019_71_RIGHT nCoV-2019_1 TGGAAAAGAAAGGTAAGAACAAGTCCT 27 37.0 X (SEQ ID NO: 582) nCoV-2019_72_LEFT nCoV-2019_2 ACACGTGGTGTTTATTACCCTGAC (SEQ 24 45.8 ID NO: 583) nCoV-2019_72_RIGHT nCoV-2019_2 ACTCTGAACTCACTTTCCATCCAAC (SEQ 25 44.0 ID NO: 584) nCoV-2019_73_LEFT nCoV-2019_1 CAATTTTGTAATGATCCATTTTTGGGTGT 29 31.0 (SEQ ID NO: 585) nCoV-2019_73_RIGHT nCoV-2019_1 CACCAGCTGTCCAACCTGAAGA (SEQ ID 22 54.6 NO: 586) nCoV-2019_74_LEFT nCoV-2019_2 ACATCACTAGGTTTCAAACTTTACTTGC 28 35.7 (SEQ ID NO: 587) nCoV-2019_74_RIGHT nCoV-2019_2 GCAACACAGTTGCTGATTCTCTTC (SEQ 24 45.8 ID NO: 588) nCoV-2019_75_LEFT nCoV-2019_1 AGAGTCCAACCAACAGAATCTATTGT 26 38.5 (SEQ ID NO: 589) nCoV-2019_75_RIGHT nCoV-2019_1 ACCACCAACCTTAGAATCAAGATTGT 26 38.5 (SEQ ID NO: 590) nCoV-2019_76_LEFT_alt3 nCoV-2019_2 GGGCAAACTGGAAAGATTGCTGA (SEQ 23 47.8 X ID NO: 591) nCoV-2019_76_RIGHT_alt0 nCoV-2019_2 ACCTGTGCCTGTTAAACCATTGA (SEQ ID 23 43.5 X NO: 592) nCoV-2019_77_LEFT nCoV-2019_1 CCAGCAACTGTTTGTGGACCTA (SEQ ID 22 50.0 NO: 593) nCoV-2019_77_RIGHT nCoV-2019_1 CAGCCCCTATTAAACAGCCTGC (SEQ ID 22 54.6 NO: 594) nCoV-2019_78_LEFT nCoV-2019_2 CAACTTACTCCTACTTGGCGTGT (SEQ ID 23 47.8 NO: 595) nCoV-2019_78_RIGHT nCoV-2019_2 TGTGTACAAAAACTGCCATATTGCA 25 36.0 (SEQ ID NO: 596) nCoV-2019_79_LEFT nCoV-2019_1 GTGGTGATTCAACTGAATGCAGC (SEQ 23 47.8 X ID NO: 597) nCoV-2019_79_RIGHT nCoV-2019_1 CATTTCATCTGTGAGCAAAGGTGG (SEQ 24 45.8 X ID NO: 598) nCoV-2019_80_LEFT nCoV-2019_2 TTGCCTTGGTGATATTGCTGCT (SEQ ID 22 45.5 X NO: 599) nCoV-2019_80_RIGHT nCoV-2019_2 TGGAGCTAAGTTGTTTAACAAGCG (SEQ 24 41.7 X ID NO: 600) nCoV-2019_81_LEFT nCoV-2019_1 GCACTTGGAAAACTTCAAGATGTGG 25 44.0 (SEQ ID NO: 601) nCoV-2019_81_RIGHT nCoV-2019_1 GTGAAGTTCTTTTCTTGTGCAGGG (SEQ 24 45.8 ID NO: 602) nCoV-2019_82_LEFT nCoV-2019_2 GGGCTATCATCTTATGTCCTTCCCT (SEQ 25 48.0 ID NO: 603) nCoV-2019_82_RIGHT nCoV-2019_2 TGCCAGAGATGTCACCTAAATCAA (SEQ 24 41.7 ID NO: 604) nCoV-2019_83_LEFT nCoV-2019_1 TCCTTTGCAACCTGAATTAGACTCA (SEQ 25 40.0 ID NO: 605) nCoV-2019_83_RIGHT nCoV-2019_1 TTTGACTCCTTTGAGCACTGGC (SEQ ID 22 50.0 NO: 606) nCoV-2019_84_LEFT nCoV-2019_2 TGCTGTAGTTGTCTCAAGGGCT (SEQ ID 22 50.0 NO: 607) nCoV-2019_84_RIGHT nCoV-2019_2 AGGTGTGAGTAAACTGTTACAAACAAC 27 37.0 (SEQ ID NO: 608) nCoV-2019_85_LEFT nCoV-2019_1 ACTAGCACTCTCCAAGGGTGTT (SEQ ID 22 50.0 NO: 609) nCoV-2019_85_RIGHT nCoV-2019_1 ACACAGTCTTTTACTCCAGATTCCC (SEQ 25 44.0 ID NO: 610) nCoV-2019_86_LEFT nCoV-2019_2 TCAGGTGATGGCACAACAAGTC (SEQ ID 22 50.0 NO: 611) nCoV-2019_86_RIGHT nCoV-2019_2 ACGAAAGCAAGAAAAAGAAGTACGC 25 40.0 (SEQ ID NO: 612) nCoV-2019_87_LEFT nCoV-2019_1 CGACTACTAGCGTGCCTTTGTA (SEQ ID 22 50.0 NO: 613) nCoV-2019_87_RIGHT nCoV-2019_1 ACTAGGTTCCATTGTTCAAGGAGC (SEQ 24 45.8 ID NO: 614) nCoV-2019_88_LEFT nCoV-2019_2 CCATGGCAGATTCCAACGGTAC (SEQ ID 22 54.6 NO: 615) nCoV-2019_88_RIGHT nCoV-2019_2 TGGTCAGAATAGTGCCATGGAGT (SEQ 23 47.8 ID NO: 616) nCoV-2019_89_LEFT_alt2 nCoV-2019_1 CGCGTTCCATGTGGTCATTCAA (SEQ ID 22 50.0 NO: 617) nCoV-2019_89_RIGHT_alt4 nCoV-2019_1 ACGAGATGAAACATCTGTTGTCACT 25 40.0 (SEQ ID NO: 618) nCoV-2019_90_LEFT nCoV-2019_2 ACACAGACCATTCCAGTAGCAGT (SEQ 23 47.8 ID NO: 619) nCoV-2019_90_RIGHT nCoV-2019_2 TGAAATGGTGAATTGCCCTCGT (SEQ ID 22 45.5 NO: 620) nCoV-2019_91_LEFT nCoV-2019_1 TCACTACCAAGAGTGTGTTAGAGGT 25 44.0 X (SEQ ID NO: 621) nCoV-2019_91_RIGHT nCoV-2019_1 TTCAAGTGAGAACCAAAAGATAATAAGC 29 31.0 X A (SEQ ID NO: 622) nCoV-2019_92_LEFT nCoV-2019_2 TTTGTGCTTTTTAGCCTTTCTGCT (SEQ ID 24 37.5 NO: 623) nCoV-2019_92_RIGHT nCoV-2019_2 AGGTTCCTGGCAATTAATTGTAAAAGG 27 37.0 (SEQ ID NO: 624) nCoV-2019_93_LEFT nCoV-2019_1 TGAGGCTGGTTCTAAATCACCCA (SEQ ID 23 47.8 NO: 625) nCoV-2019_93_RIGHT nCoV-2019_1 AGGTCTTCCTTGCCATGTTGAG (SEQ ID 22 50.0 NO: 626) nCoV-2019_94_LEFT nCoV-2019_2 GGCCCCAAGGTTTACCCAATAA (SEQ ID 22 50.0 NO: 627) nCoV-2019_94_RIGHT nCoV-2019_2 TTTGGCAATGTTGTTCCTTGAGG (SEQ ID 23 43.5 NO: 628) nCoV-2019_95_LEFT nCoV-2019_1 TGAGGGAGCCTTGAATACACCA (SEQ ID 22 50.0 NO: 629) nCoV-2019_95_RIGHT nCoV-2019_1 CAGTACGTTTTTGCCGAGGCTT (SEQ ID 22 50.0 NO: 630) nCoV-2019_96_LEFT nCoV-2019_2 GCCAACAACAACAAGGCCAAAC (SEQ ID 22 50.0 NO: 631) nCoV-2019_96_RIGHT nCoV-2019_2 TAGGCTCTGTTGGTGGGAATGT (SEQ ID 22 50.0 NO: 632) nCoV-2019_97_LEFT nCoV-2019_1 TGGATGACAAAGATCCAAATTTCAAAGA 28 32.1 (SEQ ID NO: 633) nCoV-2019_97_RIGHT nCoV-2019_1 ACACACTGATTAAAGATTGCTATGTGAG 28 35.7 (SEQ ID NO: 634) nCoV-2019_98_LEFT nCoV-2019_2 AACAATTGCAACAATCCATGAGCA (SEQ 24 37.5 ID NO: 635) nCoV-2019_98_RIGHT nCoV-2019_2 TTCTCCTAAGAAGCTATTAAAATCACAT 30 33.3 GG (SEQ ID NO: 636)

TABLE-US-00006 TABLE 5 Time and Cost Comparison of FLEX vs XT Library Prep Kit Cost Per Sample ($) Time (hrs) Illumina DNA Flex 45.96 10 Illumina Nextera XT 64.43 13.5

TABLE-US-00007 TABLE 6 Cost of SDSI + AmpSeq Processing Item Cost Number of Cost per Step Reagent Vendor Number (dollars) Reactions Reaction Biosample MagMAX ™ Thermo Fisher A27828 495 96 5.16 Extraction mirVana ™ Total RNA Scientific Isolation Kit SSIV RT master mix Thermo Fisher 18090050 383 50 7.66 Scientific cDNA Random hexamers Thermo Fisher N808127 91 100 0.91 Synthesis (50 ng/ul) Scientific dNTPs (10 nM) Thermo Fisher 18427-013 99 100 0.99 Scientific 5x RT buffer Thermo Fisher 18090050 x x x Scientific DTT (100 mM) Thermo Fisher 18090050 x x x Scientific Superase rnase Thermo Fisher 10777-019 188 125 1.50 inhibitor Scientific ARTIC PCR Q5 Hot Start New England M0494L 845 500 1.69 High-Fidelity 2X BioLabs Master Mix Artic Primers Pool#1 IDT 30 500 0.06 and Pool#2 Spike-ins Spike in Primers IDT 500 1000000 0.00 (Forward/Reverse) Spike-in targets n = 96 IDT 5821 1000000 0.01 Post Artic Qubit ™ dsDNA HS Thermo Fisher Q32854 308 500 0.62 Pooling Assay Kit Scientific Quantification Library Nextera DNA flex Illumina 20018705 4153 190 21.86 Construction Library Prep (n = 96) Nextera index UD Set Illumina 20027213 672 384 1.75 A (n = 96) Library High Sensitivity D1000 Agilent 5067-5584 362 112 3.23 Quantification ScreenTape High Sensitivity D1000 Agilent 5067-5603 59.14 112 0.53 Sample Buffer TOTAL: 45.96

TABLE-US-00008 TABLE 8 Library Size DNA Flex Standard DNA Flex Standard DNA Flex .5X DNA Flex Library Concentration .5X DNA Flex Library Size (bp) Library CT Library Size (bp) Concentration Sample Dilution (nM) (nM) MA_MGH_00109 15.39 340 332 92 54.3 MA_MGH_00110 26.39 293 271 13.4 6.84 MA_MGH_00113 31.93 211 207 3.05 1.84

[0228] Various modifications and variations of the described methods, pharmaceutical compositions, and kits of the invention will be apparent to those skilled in the art without departing from the scope and spirit of the invention. Although the invention has been described in connection with specific embodiments, it will be understood that it is capable of further modifications and that the invention as claimed should not be unduly limited to such specific embodiments. Indeed, various modifications of the described modes for carrying out the invention that are obvious to those skilled in the art are intended to be within the scope of the invention. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure come within known customary practice within the art to which the invention pertains and may be applied to the essential features herein before set forth.