Phenotype enrichment software tools | Transcription data analysis
Genome-scale phenotypic data are available for many model organisms, yet existing tools to functionally interpret gene sets from these phenotypic data are largely based on mutagenesis-derived phenotypes observed in mouse or human.
Consists of a one-stop online assembly of computational software tools. ToppGene Suite enables users to (i) perform gene list enrichment analysis, (ii) perform candidate gene prioritization based on functional annotations, (iii) perform candidate gene prioritization based on protein interactions network analysis and (iv) identify and rank candidate genes in the interactome based on both functional annotations and e protein-protein interaction network (PPIN) analysis. The suite can identify true candidate genes.
Incorporates information on gene regulation from a set of markers, increases the power to detect associations relative to traditional SNP-based GWAS and known gene-based tests under a broad range of genetic architectures and provides mechanistic insights and more easily interpreted direction of effect into the observed associations. PrediXcan can detect known and novel genes associated with disease traits and provide insights into the mechanism of these associations.
A web application dedicated to understanding functional properties of mammalian gene sets based on mouse-mutant phenotypes. MamPhEA allows users to conduct enrichment analysis on predefined or user-defined phenotypes, gives users the option to specify phenotypes derived from null mutations, produces easily comprehensible results and supports analyses on genes of all mammalian species with a fully sequenced genome.
An algorithm for the identification of pathways and networks associated with different phenotypes. PhenoNet uses two types of input data: gene expression data (RMA, RPKM, FPKM, etc.) and phenotypic information, and integrates these data with curated pathways and protein-protein interaction information. Comprehensive iterations across all possible pathways and subnetworks result in the identification of key pathways or subnetworks that distinguish between the two phenotypes.
A public web server for automated phenomic enrichment analyses of the genes of A. thaliana. POEAS uses a simple list of genes/proteins as input and perform enrichment analysis and provide results and tools for visualizing enrichment results. Users can also plot various statistics by grouping results by genes and PO terms.
An empirically optimal method for phenotype prediction. Our most valuable predictors (MVP) filtering algorithm, increases prediction accuracy and suggests that filtering may allow researchers to focus validation on a relatively small number of transcripts. Our analysis shows that isoforms are complementary to genes, providing non-redundant information and enhanced predictive power.
Includes all the necessary functions to build and test predictors from expression data. Phenopredict is an R package that use expression data and phenotype information from any study to build a phenotype predictor. This package allows for (i) regions to be selected for the phenotype of interest, (ii) a predictor to be built for either continuous or categorical variables, (iii) the predictor in to be tested on the training data to assess resubstitution error, (iv) data to be extracted from a new data set for the same regions upon with the predictor was built, and (v) phenotype prediction in new data set.