Gene set enrichment analysis software tools | High-throughput sequencing
A common feature of many current functional genomics technologies, as well as many different types of bioinformatics analyses, is that they output very large lists of genes, typically in the order of hundreds or thousands. Evidently, interpreting these lists by assessing each gene individually is not practical. Therefore, Gene Set Enrichment Analysis (GSEA) has become the first step in interpreting these long lists of genes. The principle of GSEA is to search for sets of genes that are significantly over-represented in a given list of genes, compared to a background set of genes. These sets of genes consist typically, but not always, of genes that function together in a known biological pathway.
Enables visualization and statistical analysis of microarray gene expression, copy number, methylation and RNA-Seq data. BRB-ArrayTools provides scientists with software to (1) use valid and powerful methods appropriate for their experimental objectives without requiring them to learn a programming language, (2) encapsulate into software experience of professional statisticians who read and critically evaluate the extensive published literature of new analytic and computational methods, and (3) facilitate education of scientists in statistical methods for analysis of DNA microarray data.
Predicts the function of genes and gene sets. GeneMANIA is used for probing of gene function and revealing pairwise connections linking genes in yeast, fly, worm, human and other species. It allows users to construct networks from gene lists for custom organisms and network data. The prediction performed provides a method for leveraging functionally informative associations to explore bacterial gene function.
Evaluates microarray data at the level of gene sets. GSEA aims to determine whether members of a gene set S tend to occur toward the top (or bottom) of the list L, in which case the gene set is correlated with the phenotypic class distinction. This method eases the interpretation of a largescale experiment by identifying pathways and processes, and can boost the signal-to-noise ratio when the members of a gene set exhibit strong cross-correlation, allowing to detect modest changes in individual genes.
Allows differential expression analysis of digital gene expression data. edgeR implements a range of statistical methodology based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models and quasi likelihood tests. The package and methods are general, and can work on other sources of count data, such as barcoding experiments and peptide counts.
Allows users to obtain biological features/meaning associated with large gene or protein lists. DAVID can determine gene-gene similarity, based on the assumption that genes sharing global functional annotation profiles are functionally related to each other. It groups related genes or terms into functional groups employing the similarity distances measure. This tool takes into account the redundant and network nature of biological annotation contents.
Serves for the functional analysis of gene expression and genomic data. Babelomics offers the possibility to explore the effects of alteration in gene expression levels or changes in genes sequences within a functional context. It provides user-friendly access to a full range of methods that cover: (1) primary data analysis; (2) a variety of tests for different experimental designs; and (3) different enrichment and network analysis algorithms for the interpretation of the results of such tests in the proper functional context.
Provides various next-generation sequencing (NGS) data analysis applications which are developed or optimized by Illumina, or from a growing ecosystem of third-party app providers. BAseSpace is a cloud platform that can be integrated with the industry’s leading sequencing platforms, without cumbersome or time consuming data transfer steps.