A system and method of selecting genes for a gene panel, includes retrieving gene-disease associations of genes associated with diseases at a given level in the disease hierarchy from a disease association database. The disease association database stores disease information, gene information, phenotype information, associations between diseases in the disease hierarchy, gene-disease associations and strength parameters related to the gene-disease associations. For each gene associated with the diseases at the given level, the strength parameters are weighted and combined to determine a rank score for the each gene. The genes are ranked based on the rank scores to provide ranked gene information. The ranked gene information is linked with diseases at the higher levels of the disease hierarchy based on hierarchical relationships. The ranked gene information for gene-disease associations can be used to select genes for a gene panel design.

Methods and computer apparatuses are disclosed for processing genomic data in at least partially automated workflows of modules. A method comprises: specifying a source from which nucleic acid sequence(s) are to be obtained; selecting module(s) for processing data, including at least one module for processing the one or more nucleic acid sequences; presenting, in a graphical user interface, graphical components representing the source and the module(s) as nodes within a workspace; receiving, via the graphical user interface, inputs arranging the source and the module(s) as a workflow comprising a series of nodes, the series indicating, for each particular module, that output from one of the source or another particular module is to be input into the particular module; generating an output for the workflow based upon the nucleic acid sequence(s) by processing each module in an order indicated by the series.

Methods and computer apparatuses are disclosed for processing genomic data in at least partially automated workflows of modules. A method comprises: specifying a source from which nucleic acid sequence(s) are to be obtained; selecting module(s) for processing data, including at least one module for processing the one or more nucleic acid sequences; presenting, in a graphical user interface, graphical components representing the source and the module(s) as nodes within a workspace; receiving, via the graphical user interface, inputs arranging the source and the module(s) as a workflow comprising a series of nodes, the series indicating, for each particular module, that output from one of the source or another particular module is to be input into the particular module; generating an output for the workflow based upon the nucleic acid sequence(s) by processing each module in an order indicated by the series.

A method for at least one of characterizing, diagnosing, and treating a locomotor system condition 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 locomotor system 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 locomotor system condition; and at an output device associated with the subject, promoting a therapy to the subject based upon the characterization and the therapy model.

A repeatable methodology for generation of a specific biological function library (data pool) and techniques for structuring queries that cluster and parse gene and protein alterations in individual patients and patient cohorts. Method enables analytical distinction between detectable changes in biological function and non-detectable changes in biological function using current diagnostic techniques and technologies.

A method for assessing quality of genomic regions studied for inclusion in standardized clinical formats includes receiving data sets to be applied to a plurality of gene panels. The method also includes applying, by a processor executing instructions, standardized quality control metrics to the data sets to be applied to the plurality of gene panels; filtering, by the processor executing the instructions, each of the data sets to be applied to the plurality of gene panels to sets of variants and formatting the sets of variants in a clinical format, and outputting the sets of variants resulting from the filtering to a downstream application.

A method for assessing quality of genomic regions studied for inclusion in standardized clinical formats includes receiving data sets to be applied to a plurality of gene panels. The method also includes applying, by a processor executing instructions, standardized quality control metrics to the data sets to be applied to the plurality of gene panels; filtering, by the processor executing the instructions, each of the data sets to be applied to the plurality of gene panels to sets of variants and formatting the sets of variants in a clinical format, and outputting the sets of variants resulting from the filtering to a downstream application.

Computational methods for classifying and predicting protein side chain conformations utilizing a data driven scoring function are disclosed. According to some embodiments, the methods may include obtaining structure data representing a plurality of conformations of a compound. The methods may also include determining structural differences among the conformations. The methods may also include classifying, based on the structural differences, the conformations into one or more clusters. The methods may also include determining representative conformations of the dusters, wherein an average structural difference between a representative conformation of a duster and conformations in the duster is below a predetermined threshold. The method may further include determining the representative conformations as poses of the compound.

Databases and data processing systems for use with a network-based personal genetics services platform may include member information pertaining to a plurality of members of the network-based personal genetics services platform. The member information may include genetic information, family history information, environmental information, and phenotype information of the plurality of members. A data processing system may determine, based at least in part on the member information, a model for predicting a phenotype from genetic information, family history information, and environmental information, wherein determining the model includes training the model using the member information pertaining to a set of the plurality of members. The data processing system may also receive a request from a questing member to predict a phenotype of interest, and apply an individual's genetic information, family history information, and environmental information to the model to obtain a prediction associated with the phenotype of interest for the requesting member.

A system for enabling in-silico phenotypic screening of drugs, the system is communicably coupled to a phenotype ontological databank. The system include a processor communicably coupled to a memory. The processor is configured to receive a name of at least one drug as an input, fetch targets of at least one existing drug that is similar to the at least one drug to obtain a drug target list, determine, phenotypes of the at least one drug based on associations between the targets in the drug target list and the phenotypes, said associations being accessed from the phenotype ontological databank, generate a network comprising the at least one drug, the targets and the phenotypes, determine a plurality of groups of similar phenotypes that belong to similar biological processes or clinical pathologies, and determine expressions of the phenotypes in each of a plurality of tissues, based on gene, protein and tissue expression data accessed from at least one database, determine tissues where the phenotypes of a given group are relevant and diseases associated with the tissues, based on said expressions of the phenotypes, thereby enabling phenotypic screening of the at least one drug.