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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.
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.
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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.
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.
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Automates the job for experimental biologists to identify enriched Gene Ontology (GO) terms in a list of microarray probe sets or gene identifiers (with or without expression information). agriGO consists of an analysis toolkit and database for agricultural community. This tools is able to perform custom analyses, including search, singular enrichment analysis (SEA), or parametric analysis of gene set enrichment (PAGE). It also contains a large number of species and datatypes available, which have been classified into several groups.
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.
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.
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.
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.
GSVA / Gene Set Variation Analysis
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A gene set enrichment (GSE) method that estimates variation of pathway activity over a sample population in an unsupervised manner. GSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data.
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.
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.
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.
X2K / Expression2Kinases
Infers upstream regulatory networks from signatures of differentially expressed genes. X2K is a computational pipeline that implements the original eXpression2Kinases algorithm. The software is constructed from three components: (i) the transcription factor enrichment analysis (TFEA), (ii) the protein–protein interactions (PPI) network construction (17); and (iii) the kinase enrichment analysis (KEA). It can be useful for generating hypotheses from their gene expression profiling studies.
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.
Performs gene set enrichment testing, an approach used to test for predefined biologically-relevant gene sets that contain more significant genes from an experimental dataset than expected by chance. Given a high-throughput dataset with continuous significance values (i.e. p-values), LRpath tests for gene sets (termed concepts) that have significantly higher significance values (e.g. for differential expression) than expected at random. LRpath can identify both concepts that have a few genes with very significant differential expression and concepts containing many genes with only moderate differential expression.
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.
A software package that centrally provides a large number of flexible toolsets useful for functional genomics, including microarray data storage, quality assessments, data visualization, gene annotation retrieval, statistical tests, genomic sequence retrieval and motif analysis. ArrayPlex uses a client-server architecture based on open source components, provides graphical, command-line, and programmatic access to all needed resources, and is extensible by virtue of a documented application programming interface.
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. piano package contains functions for combining the results of multiple runs of gene set analyses. It also provides functions for result visualization, including a network-based plot showing overlapping gene sets and their significance, and functions for the full analysis of microarray data. Some of its functionalities are also available through the browser-based GUI BioMet Toolbox.
A webservice for pathway annotation based on crosstalk derived through FunCoup, a framework for genome wide functional association networks. PathwAX runs the BinoX algorithm, which employs Monte-Carlo sampling of randomized networks and estimates a binomial distribution, for estimating the statistical significance of the crosstalk. A pathway is statistically enriched/depleted if the crosstalk, which is the number of links between the pathway and your gene set, is more/less than one would observe in a random network. This results in substantially higher accuracy than gene overlap methods.
Analyzes genome-wide expression patterns in one experiment at a time. T-profiler is a web application that uses the t-test to score the difference between the mean expression level of predefined groups of genes and that of all other genes on the microarray. The consensus motifs derive from three different sources: (i) motifs are extracted from the Promoter Database of Saccharomyces cerevisiae (SCPD) database, (ii) motifs are found by comparing the genome sequences of highly related yeast species, and (iii) motifs discovered from various microarray experiments using the REDUCE algorithm were added.
Implements topological gene set analysis using a two-step empirical approach. clipper exploits graph decomposition theory to create a junction tree and reconstruct the most relevant signal path. In the first step it selects significant pathways according to statistical tests on the means and the concentration matrices of the graphs derived from pathway topologies. Then, it "clips" the whole pathway identifying the signal paths having the greatest association with a specific phenotype.
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.
QuSAGE / Quantitative Set Analysis for Gene Expression
A computational framework for quantitative set analysis of gene expression. QuSAGE accounts for inter-gene correlations, improves the estimation of the variance inflation factor and, rather than evaluating the deviation from a null hypothesis with a P-value, it quantifies gene-set activity with a complete probability density function. From this probability density function, P-values and confidence intervals can be extracted and post hoc analysis can be carried out while maintaining statistical traceability. Compared with Gene Set Enrichment Analysis and CAMERA, QuSAGE exhibits better sensitivity and specificity on real data profiling the response to interferon therapy (in chronic Hepatitis C virus patients) and Influenza A virus infection.
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.
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.
MORPHIN / Model Organisms Projected on a Human Integrated Gene Network
A web-based discovery tool for human disease studies. MORPHIN investigates human diseases by an orthology-based projection of a set of model organism genes onto a genome-scale human gene network. MORPHIN then prioritizes human diseases by relevance to the projected model organism genes using two distinct methods: a conventional overlap-based gene set enrichment analysis and a network-based measure of closeness between the query and disease gene sets capable of detecting associations undetectable by the conventional overlap-based methods.
A web-based application for describing gene groups using logical rules based on Gene Ontology terms. RuleGO takes as an input two lists of genes: a list of genes to be described and a reference list of genes. As a result users obtain a list of the rules that allow to describe an input list of genes with the use of the conjunction of gene ontology terms. Obtained rules reflect co-appearance of GO-terms describing genes supported by the rules. The ontology level and the number of co-appearing GO-terms is adjusted in automatic manner. The rules have a statistical significance level determined by the user and are sorted according to the ranking obtained by a rule quality measure. Obtained rules also consider co-occurrence of the terms in a given gene group and the presented method guarantees that the co-occurrence will not be trivial (for example, resulting from hierarchy of the ontology graph). The RuleGO provides a tool that allows selecting the most interesting combinations of GO-terms from all possible significant combinations, which can save an expert time and improve the whole process of analysis.
GSCA / Gene Set Context Analysis
An open source software package to help researchers use massive amounts of publicly available gene expression data (PED) to make discoveries. Users can interactively visualize and explore gene and gene set activities in 25,000+ consistently normalized human and mouse gene expression samples representing diverse biological contexts (e.g. different cells, tissues and disease types, etc.). By providing one or multiple genes or gene sets as input and specifying a gene set activity pattern of interest, users can query the expression compendium to systematically identify biological contexts associated with the specified gene set activity pattern. In this way, researchers with new gene sets from their own experiments may discover previously unknown contexts of gene set functions and hence increase the value of their experiments. GSCA has a graphical user interface (GUI). The GUI makes the analysis convenient and customizable. Analysis results can be conveniently exported as publication quality figures and tables.
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.
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.
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