Techniques for multi-dimensional signal analysis are described herein. The techniques may be used in one or more sequencing applications. For example, according to some aspects, there is provided a method comprising: determining information about a sample that emits emission light in response to excitation light based on at least one of pulse duration and interpulse duration and at least two of wavelength, intensity, and lifetime of the emission light, wherein the sample comprises a reagent configured to be coupled to a luminescent label, and wherein a shielding element is disposed between the reagent and the luminescent label.

Systems and methods to automatically de novo assemble a set of unordered read sequences into one or more, larger nucleotide sequences are presented. The method involves first creating two identical sets of the reads, dividing each read in both sets into smaller sorted mer sequences and then comparing the mers for each read in set 1 to the mers from each read in set 2 to exhaustively identify overlapping segments. Overlap information is used to construct a modified assembly string graph, traversal of which produces a sorted string graph layout file consisting of all the reads ordered left to right including their approximate starting offset position. The sorted string graph layout file is then processed by a novel multiple sequence alignment system that uses mer matches between all the overlapping reads at a given position to place matching individual bases from each read into columns from which an overall consensus sequence is determined.

Systems and methods to automatically de novo assemble a set of unordered read sequences into one or more, larger nucleotide sequences are presented. The method involves first creating two identical sets of the reads, dividing each read in both sets into smaller sorted mer sequences and then comparing the mers for each read in set 1 to the mers from each read in set 2 to exhaustively identify overlapping segments. Overlap information is used to construct a modified assembly string graph, traversal of which produces a sorted string graph layout file consisting of all the reads ordered left to right including their approximate starting offset position. The sorted string graph layout file is then processed by a novel multiple sequence alignment system that uses mer matches between all the overlapping reads at a given position to place matching individual bases from each read into columns from which an overall consensus sequence is determined.

The method for protein selection can include: characterizing a protein set, training a prediction model, determining target characteristic values, and determining a candidate protein set based on the target characteristic values.

The method for protein selection can include: characterizing a protein set, training a prediction model, determining target characteristic values, and determining a candidate protein set based on the target characteristic values.

The invention provides a method for determining copy number variations (CNV) of a sequence of interest in a test sample that comprises a mixture of nucleic acids that are known or are suspected to differ in the amount of one or more sequence of interest. The method comprises a statistical approach that accounts for accrued variability stemming from process-related, interchromosomal and inter-sequencing variability. The method is applicable to determining CNV of any fetal aneuploidy, and CNVs known or suspected to be associated with a variety of medical conditions. CNV that can be determined according to the method include trisomies and monosomies of any one or more of chromosomes 1-22, X and Y, other chromosomal polysomies, and deletions and/or duplications of segments of any one or more of the chromosomes, which can be detected by sequencing only once the nucleic acids of a test sample.

The invention provides a method for determining copy number variations (CNV) of a sequence of interest in a test sample that comprises a mixture of nucleic acids that are known or are suspected to differ in the amount of one or more sequence of interest. The method comprises a statistical approach that accounts for accrued variability stemming from process-related, interchromosomal and inter-sequencing variability. The method is applicable to determining CNV of any fetal aneuploidy, and CNVs known or suspected to be associated with a variety of medical conditions. CNV that can be determined according to the method include trisomies and monosomies of any one or more of chromosomes 1-22, X and Y, other chromosomal polysomies, and deletions and/or duplications of segments of any one or more of the chromosomes, which can be detected by sequencing only once the nucleic acids of a test sample.

The invention provides systems and methods for determining patterns of modification to a genome of a subject by representing the genome using a graph, such as a directed acyclic graph (DAG) with divergent paths for regions that are potentially subject to modification, profiling segments of the genome for evidence of epigenetic modification, and aligning the profiled segments to the DAG to determine locations and patterns of the epigenetic modification within the genome.

The invention provides systems and methods for determining patterns of modification to a genome of a subject by representing the genome using a graph, such as a directed acyclic graph (DAG) with divergent paths for regions that are potentially subject to modification, profiling segments of the genome for evidence of epigenetic modification, and aligning the profiled segments to the DAG to determine locations and patterns of the epigenetic modification within the genome.

The technology disclosed generates a reference array of variant data for locations that are shared between read results which are to be compared, and generates hashes over a selected pattern length of positions in the reference array to independently produce non-unique window hashes for base patterns in the read results. It then selects for comparison window hashes that occur less than a ceiling number of times and compares the selected window hashes to identify common window hashes between the read results. It then determines a similarity measure for the read results based on the common window hashes.