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GSEA / Gene Set Enrichment Analysis

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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.


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Performs differential gene expression analysis. DEseq is a method that integrates methodological advances with features to facilitate quantitative analysis of comparative RNA-seq data using shrinkage estimators for dispersion and fold change. The software is suitable for small studies with few replicates as well as for large observational studies. Its heuristics for outlier detection assist in recognizing genes for which the modeling assumptions are unsuitable and so avoids type-I errors caused by these.

limma / Linear Models for Microarray Data

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Provides an integrated solution for analysing data from gene expression experiments. limma contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. It also contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions: (i) it can perform both differential expression and differential splicing analyses of RNA-seq data; (ii) the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences.

edgeR / empirical analysis of DGE in R

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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.

DAVID / Database for Annotation, Visualization and Integrated Discovery

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.


Combines cross-species data and gene entity integration, scalable hierarchical analysis of user data with a community-built and curated data archive of gene sets and gene networks, and tools for data driven comparison of user-defined biological, behavioral and disease concepts. GeneWeaver allows users to integrate gene sets across species, tissue and experimental platform. It differs from conventional gene set over-representation analysis tools in that it allows users to evaluate intersections among all combinations of a collection of gene sets, including, but not limited to annotations to controlled vocabularies. There are numerous applications of this approach. Sets can be stored, shared and compared privately, among user defined groups of investigators, and across all users.


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. The GeneMANIA app extends the capabilities of the GeneMANIA prediction server by allowing users to quickly construct networks from gene lists for custom organisms and network data. The prediction performed by GeneMANIA provides a method for leveraging functionally informative associations to explore bacterial gene function.


A web service allowing the integrated analysis of transcriptomic, miRNomic, genomic, and proteomic datasets. GeneTrail offers multiple statistical tests, a large number of predefined reference sets, as well as a comprehensive collection of biological categories and enables direct comparisons between the computed results. GeneTrail was designed to offer users a maximal amount of flexibility while keeping the common workflow accessible to non-expert users. This is achieved by offering a user friendly, well-documented web interface. In turn, scripting capabilities allow expert users to conduct fully automated large-scale analyses and the integration into third-party applications.

IMP / Integrative Multi-species Prediction

An interactive web server that enables molecular biologists to interpret experimental results and to generate hypotheses in the context of a large cross-organism compendium of functional predictions and networks. The system provides biologists with a framework to analyze their candidate gene sets in the context of functional networks, expanding or refining their sets using functional relationships predicted from integrated high-throughput data.


Allows researchers to use prior information on groupings of genes and to specifically investigate groups of genes that interest them from a biological point of view. When researchers have many candidate pathways, available e.g. from gene ontology databases or programs, the global test can be used to find promising pathways. Special attention is given to visualizations of the test result, focusing on the associations between samples and showing the impact of individual genes on the test result. Glotest Special include graphs to search for outlying samples and diagnostic plots to judge how much each individual gene contributes to a significant test result for the group.

piano / Platform for Integrated Analysis of Omics data

Performs gene set analysis (GSA) using various statistical methods, from different gene level statistics and a wide range of gene-set collections. The piano package contains functions for combining the results of multiple runs of gene set analyses. It also includes several functions for result visualization, including a network-based plot showing overlapping gene sets and their significance. Finally, piano also contains functions for the full analysis of microarray data, if the user wants a fully integrated GSA starting from raw expression data. piano is available as an R/Bioconductor package. Some of its functionalities are also available through the browser-based GUI BioMet Toolbox.


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 pathway based data integration and visualization. Pathview maps and renders a wide variety of biological data on relevant pathway graphs. All users need is to supply their data and specify the target pathway. Pathview automatically downloads the pathway graph data, parses the data file, maps user data to the pathway, and render pathway graph with the mapped data. In addition, it also seamlessly integrates with pathway and gene set (enrichment) analysis tools for large-scale and fully automated analysis.


Allows to visualize complex gene expression analysis results coming from biclustering algorithms. BicOverlapper visualizes the most relevant aspects of the analysis, including expression data, profiling analysis results and functional annotation. It integrates several state-of-the-art numerical methods, such as differential expression analysis, gene set enrichment or biclustering. The tool permits to have an overall view of several expression aspects, from raw data to analysis results and functional annotations.

SAFE / Significance Analysis of Function and Expression

Allows to test functional categories in gene expression experiments. SAFE can be used as a workhorse to investigate possible functional relationships. It can study different standard gene-list methods. It also calculates permutation-based p-values using a separate null permutation distribution for each category. In addition, SAFE can detect gene categories with a high proportion of marginally significant genes that fail to appear on the significant gene list.

GeneSCF / Gene Set Clustering based on Functional annotation

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Predicts the functionally relevant biological information for a set of genes in a real-time updated manner. GeneSCF is designed to handle information from more than 4000 organisms from freely available prominent functional databases like KEGG, Reactome and Gene Ontology. GeneSCF is more reliable compared to other enrichment tools because of its ability to use reference functional databases in real-time to perform enrichment analysis. It is an easy-to-integrate tool with other pipelines available for downstream analysis of high-throughput data. More importantly, GeneSCF can run multiple gene lists simultaneously on different organisms thereby saving time for the users. Since the tool is designed to be ready-to-use, there is no need for any complex compilation and installation procedures.

X2K / Expression2Kinases

Identifies upstream regulators likely responsible for observed patterns in genome-wide gene expression. The X2K workflow provides several features: two methods for identifying transcription factors for lists of differentially expressed genes (ChEA and PWM), a large-scale protein-protein interaction (PPI) network with the ability to build subnetworks that connect seed lists of genes/proteins, kinase enrichment analysis tool and a database of kinase-substrate interactions, gene-list enrichment analysis tool and a tool for identifying drugs that induce or reverse the expression of lists of differentially expressed genes.


Enables comparative analyses of multiple gene lists. ToppCluster allows the identification of biological themes in data sets involving numerous gene sets. The software can co-analyze multiple gene lists and depict the results in a form that facilitates comparative and contrastive analysis. It is able to identify biological processes and putative regulatory mechanisms associated with human tissue-specific gene expression gene sets that exhibit rich disease and phenotypes impacts.

PlantGSEA / Plant GeneSet Enrichment Analysis toolkit

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Allows interpretation of biological meaning. PlantGSEA calculates the overlaps between the requested gene lists and various previously defined gene sets. The request can be derived from high-throughput experiments like microarray or next-generation sequencing. It contributes to uncovering facts from the data by calculating the enrichment gene ontology. This database integrates Gene Set Enrichment Analysis (GSEA) studies for plant species.

iGA / Iterative Group Analysis

Provides an automatic functional annotation of microarray results together with a statistical confidence level for each annotation feature. iGA is based on a comprehensive hypergeometric statistics calculation detecting concerted changes in functional classes of genes. This method offers a fast and efficient way to compare an experiment with a large number of published microarray experiments, without requiring a common experimental platform or analytical technique.

TRAPLINE / Transparent Reproducible and Automated PipeLINE

Serves for RNAseq data processing, evaluation and prediction. TRAPLINE guides researchers through the NGS data analysis process in a transparent and automated state-of-the-art pipeline. It can detect protein-protein interactions (PPIs), miRNA targets and alternatively splicing variants or promoter enriched sites. This tool includes different modules for several functions: (1) it scans the list of differentially expressed genes; (2) it includes modules for miRNA target prediction; and (3) a module is implemented to identify verified interactions between proteins of significantly upregulated and downregulated mRNAs.

EDDY / Evaluation of Dependency Differentiality

A statistical test for the differential dependency relationship of a set of genes between two given conditions. For each condition, possible dependency network structures are enumerated and their likelihoods are computed to represent a probability distribution of dependency networks. The difference between the probability distributions of dependency networks is computed between conditions, and its statistical significance is evaluated with random permutations of condition labels on the samples.


An R package that tests for expression enrichment in specific brain regions at different developmental stages using expression information gathered from multiple regions of the adult and developing human brain, together with ontologically-organized structural information about the brain, both provided by the Allen Brain Atlas. These expression patterns might provide insights into the processes in which these genes act, and this information can then be used to guide further functional experiments in cell lines, organoids or model organisms.


An approach for assessing uncertainty in the definition of gene sets. This is an essential step when new gene sets are constructed from domain knowledge or given gene sets are suspected to be affected by uncertainty. Quantification of uncertainty is implemented in the R-package GiANT. We also included widely used GSA methods, embedded in a generic framework that can readily be extended by custom methods. The package provides an easy to use front end and allows for fast parallelisation.

iNOTE / Integrative Network Omnibus Total Effect Test

Provides efficient procedures for gene set screening which use self-contained hypothesis tests. iNOTE is a robust method to model misspecification. This resource performed with close to optimal power across all simulations settings, particularly those in which the gene set is comprised of mixtures of different disease risk models – a highly likely biological scenario. It can also easily incorporate the adjustments for potential confounding covariates.

GSAR / Gene Set Analysis in R

Implements self-contained multivariate non-parametric statistical methods testing a complex null hypothesis against specific alternatives, such as differences in mean (shift), variance (scale), or net correlation structure. GSAR package also provides a graphical visualization tool, based on the union of two minimum spanning trees, for correlation networks to examine the change in the correlation structures of a gene set between two conditions and highlight influential genes (hubs). The methods in GSAR package are applicable to any type of omics data that can be represented in a matrix format.

HEAT / H-InvDB Enrichment Analysis Tool

Identifies automatically features specific to a given human gene set. HEAT is a data mining tool that searches H-InvDB annotations that are significantly enriched in a user-defined gene set, as compared with the entire H-InvDB representative transcripts. This technique is called gene set enrichment analysis (GSEA), and is popularly used in analyzing results of microarray experiments. HEAT requires three steps. (i) Gene-Set Submission, (ii) Execution, and (iii) Results.

MiSTIC / Minimum Spanning Trees Inferred Clustering

Visualizes and compares collections of gene expression profiles, instantly highlighting differences and similarities in gene clustering between cancer types or subtypes. MiSTIC should facilitate identification of new prognostic markers and accelerate improvements in the molecular classification of cancers. Its integrative concept greatly improves the accessibility of complex datasets by end-users and enables the generation of hypotheses on mechanisms driving correlated gene expression. It is adaptable to the analysis of any collection of quantitative profiles.


A web-based database and a knowledge extraction engine. Lynx supports annotation and analysis of high-throughput experimental data and generation of weighted hypotheses regarding genes and molecular mechanisms contributing to human phenotypes or conditions of interest. Since the last release, the Lynx knowledge base (LynxKB) has been periodically updated with the latest versions of the existing databases and supplemented with additional information from public databases. These additions have enriched the data annotations provided by Lynx and improved the performance of Lynx analytical tools. Moreover, the Lynx analytical workbench has been supplemented with new tools for reconstruction of co-expression networks and feature-and-network-based prioritization of genetic factors and molecular mechanisms. These developments facilitate the extraction of meaningful knowledge from experimental data and LynxKB. The Service Oriented Architecture provides public access to LynxKB and its analytical tools via user-friendly web services and interfaces.


Accelerates querying of large-scale transcription profiles all-pairs comparison and global clustering analysis on transcriptomic datasets. paraGSEA is based on a scalable parallel algorithm. It is designed to accelerate large-scale transcription profile querying and clustering analysis to make up for the lack of tools in parallel computing. The tool can be applied to large-scale data with enough clusters support by an efficient second level of parallelization and strict data partition and communication strategies.


Performs network enrichment analysis against functional gene sets, benchmarks networks and renders raw gene profile matrices of dimensionality 'Ngenes x Nsamples' into the space of gene set (typically pathway) enrichment scores of dimensionality 'Npathways x Nsamples'. NEArender is a package which can transform raw 'omics' features of experimental or clinical samples into matrices describing the same samples with many fewer network enrichment analysis (NEA)-based pathway scores.