The present invention provides breeding methods and compositions to enhance the germplasm of a plant by the use of direct nucleic acid sequence information. The methods describe the identification and accumulation of preferred nucleic acid sequences in the germplasm of a breeding population of plants.

Disclosed are methods and tools for rapidly aligning reads to a reference sequence. These methods and tools employ Bloom filters or similar set membership testers to perform the alignment. The reads may be short sequences of nucleic acids or other biological molecules and the reference sequences may be sequences of genomes, chromosomes, etc. The Bloom filters include a collection of hash functions, a bit array, and associated logic for applying reads to the filter. Each filter, and there may be multiple of these used in a particular application, is used to determine whether an applied read is present in a reference sequence. Each Bloom filter is associated with a single reference sequence such as the sequence of a particular chromosome. In one example, chromosomal abundance is determined by aligning reads from a sequencer to multiple chromosomes, each having an associated Bloom filter or other set membership tester.

The present application is directed to methods and systems for identifying small molecule compounds in mixtures using a library comprising calculated structures and corresponding calculated mass spectral fragmentation patterns of known and/or hypothetical small molecule compounds that may be in the mixture and screening of a mass spectrum of the mixture using the library to identify matching fragmentation patterns. If a mass spectral fragmentation pattern present in the mass spectrum of the mixture matches a calculated fragmentation pattern of one of the known or hypothetical compounds this confirms the identity of a compound in the mixture as the known or hypothetical compound. The method represents a platform method that can be used for a multitude of purposes related to the screening and identification of compounds in mixtures. Therefore the methods and systems of the present application represent an approach that is uniquely capable of navigating chemical space and providing a understanding of desired families and pharmacophores.

The invention relates to methods for modulating the immune function through targeting of CLIP molecules. The result is wide range of new therapeutic regimens for treating, inhibiting the development of, or otherwise dealing with, a multitude of illnesses and conditions, including autoimmune disease, cancer, Alzheimer's disease, allergic disease, transplant and cell graft rejection, HIV infection and other viral, bacterial, and parasitic infection, and AIDS. Methods are also provided for preparing a peptide having the property of being able to displace CLIP by feeding one or more peptide sequences into software that predicts MHC Class II binding regions in an antigen sequence and related products.

A method of classifying a lung graft subjected to normothermic ex vivo lung perfusion (EVLP), during perfusion and/or after perfusion, the method comprising: a) collecting a test sample from the lung graft; b) measuring a polypeptide level of a negative transplant predictor gene product selected from CCG predictor gene products M-CSF, IL-8 SCGF-beta, GRO-alpha, G-CSF, MIP-1 alpha, and/or MIP-1beta, endothelin predictor gene products endothelin 1 (ET-1) and/or big ET-1, and/or apoptosis predictor gene products cytokeratin 18 (CK-18), caspase 3 and/or HMGB-1 in the sample and/or determining a metabolite profile of the sample for lung grafts that are from donors where the death was due to cardiac death (DCD); c) identifying the graft as a good candidate for transplant or a poor candidate for transplant wherein an increased polypeptide level of one or more negative transplant outcome predictor gene products compared to an outcome control or a reference metabolic profile is indicative the graft is a poor candidate for transplant.

Technology provided herein relates in part to methods, processes, machines and apparatuses for detecting genetic variations. In some embodiments, the technology is related to non-invasive assessment of aneuploidies.

The present invention relates to a method and system for associating gene expression data with a disease name. A first data set associated with a plurality of genetic probes for a plurality of biological samples may be received. The first data set may be sorted based on a normalized gene expression values for the plurality of genetic probes. A largest value gap of the normalized gene expression values may be identified. A set of expressed genes within the first data set may be identified. An indexable document may be generated for a biological sample of the plurality of biological samples comprising data associated with the set of expressed genes. A second data set associated with an expressed gene of the set of expressed genes may be searched. A disease name may be associated with an expressed gene based on a threshold correlation between the disease name and the expressed gene.

A method and computer system for identifying genes associated with a phenotype includes obtaining data representing mutations in a cohort of subjects exhibiting a phenotype. An evolutionary action (EA) score is calculated for each mutation using the data obtained. For each gene in the cohort, respective distributions of the calculated EA scores are determined for mutations found in the gene. The determined distributions of EA scores are quantitatively compared within the cohort and with random distributions to establish comparison data. Based on the comparison data, distributions of EA scores are identified that are non-random, and linkage of each gene in the cohort to the phenotype is assessed based on the identified non-random distributions to identify genes associated with the phenotype. The phenotype can be a disease, such as cancer, and linkage of each gene in the cohort to the disease can be assessed to identify disease causing genes.

A method for at least one of characterizing, diagnosing, and treating an autoimmune disorder in at least a subject, the method comprising: receiving an aggregate set of biological samples from a population of subjects; generating at least one of a microbiome composition dataset and a microbiome functional diversity dataset for the population of subjects; generating a characterization of the autoimmune condition based upon features extracted from at least one of the microbiome composition dataset and the microbiome functional diversity dataset; based upon the characterization, generating a therapy model configured to correct the autoimmune condition; and at an output device associated with the subject, promoting a therapy to the subject based upon the characterization and the therapy model.

Disclosed in some examples are methods including selecting a first plurality of single gene mutants from a pool of possible single gene mutants of an organism. The first plurality of single gene mutants is less than a number of possible single mutants. A computer processor is used to iteratively select a second plurality of single gene mutants by selecting single gene mutants from the pool of possible single gene mutants that increases a sum of products of similarities between the first plurality of single gene mutants and corresponding functional relationships. The second plurality of single gene mutants is larger in number than the first plurality of single gene mutants, and wherein the second plurality of single gene mutants is less than the number of possible single gene mutants of the organism. A set of genes is outputted comprising the first and second pluralities of single gene mutants.