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.

Methods for preparing sequencing libraries from a DNA-containing test sample, as well as methods for correcting sequencing-derived errors, are provided.

Methods for preparing sequencing libraries from a DNA-containing test sample, as well as methods for correcting sequencing-derived errors, are provided.

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.

This disclosure provides systems and methods for sample processing and data analysis. Sample processing may include nucleic acid sample processing and subsequent sequencing. Some or all of a nucleic acid sample may be sequenced to provide sequence information, which may be stored or otherwise maintained in an electronic storage location. The sequence information may be analyzed with the aid of a computer processor, and the analyzed sequence information may be stored in an electronic storage location that may include a pool or collection of sequence information and analyzed sequence information generated from the nucleic acid sample. Methods and systems of the present disclosure can be used, for example, for the analysis of a nucleic acid sample, for producing one or more libraries, and for producing biomedical reports. Methods and systems of the disclosure can aid in the diagnosis, monitoring, treatment, and prevention of one or more diseases and conditions.

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.

The invention provides compositions and methods for determining whether a subject is predisposed to the disease or condition, or for diagnosing a disease or condition, or for detecting the state of a disease or condition, by detecting the methylation state of the subject's nucleic acids. In addition, the invention provides methods for determining the methylation age of a subject or tissue from a subject or for differentiation between nucleic acids originating from different subjects or tissues. The invention further provides methods for selecting nucleic acid molecules for use in the methods of the invention.

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.