1 - 50 of 57 results

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.

Ontologizer

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A Java application that can be used to perform statistical analysis for overrepresentation of Gene Ontology (GO) terms in sets of genes or proteins derived from an experiment. The Ontologizer implements the standard approach to statistical analysis based on the one-sided Fisher's exact test, the novel parent-child method, as well as topology-based algorithms. A number of multiple-testing correction procedures are provided. The Ontologizer allows users to visualize data as a graph including all significantly overrepresented GO terms and to explore the data by linking GO terms to all genes/proteins annotated to the term and by linking individual terms to child terms.

GeneCodis

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A web-based tool for the ontological analysis of large lists of genes. It can be used to determine biological annotations or combinations of annotations that are significantly associated to a list of genes under study with respect to a reference list. As well as single annotations, this tool allows users to simultaneously evaluate annotations from different sources, for example Biological Process and Cellular Component categories of Gene Ontology.

BrainCell

Provides functions to test query gene sets in the context of brain and neurodevelopment. BrainCell contains two databases: BrainH represents gene sets expression pattern across brain regions during development and post-natal life, and CellTax shows gene sets expression pattern across cortical cell types. The package also contains two tests: CellFET tests gene sets for enrichment in brain cell type marker genes, and DNMFET tests gene sets for enrichment in deleterious de novo mutations ascertained from patients with neurodevelopmental disorder.

NET-GE / NETwork-based Gene Enrichment

A web server for associating biological processes and pathways to sets of human proteins involved in the same phenotype. NET-GE is based on protein–protein interaction networks, following the notion that for a set of proteins, the context of their specific interactions can better define their function and the processes they can be related to in the biological complexity of the cell. Our method is suited to extract statistically validated enriched terms from Gene Ontology, KEGG and REACTOME annotation databases. Furthermore, NET-GE is effective even when the number of input proteins is small.

EnrichmentBrowser

Enables the execution and combination of several set- and network-based enrichment methods. EnrichmentBrowser is an R package that performs three steps: (1) chosen set- and network-based enrichment methods are executed individually, (2) enriched gene sets are combined by a selection of ranking criteria, and (3) resulting gene set rankings are displayed for detailed inspection. The software can facilitate the visualization and exploration of gene sets and biological pathways.

GANPA / Gene Association Network-based Pathway Analysis

Implements a network-based gene weighting algorithm for pathways, as well as a gene-weighted gene set analysis approach for microarray data pathway analysis. As a network-based gene-weighting method, GANPA offers several notable advantages over other algorithms that rely on curated gene-gene linkages in KEGG. First, it is more general-purpose and widely applicable, as both pathways with graph representations (KEGG, WikiPathways, etc.) and pathways with gene compositions alone (Reactome, MSigDB, GO BP, PANTHER, etc.) can be used for weighted GSA. Second, the potentially misclassified genes by curation errors in pathway databases are likely to be automatically identified and down-weighted by integrating gene functional associations. Third, genes with many non-specific associations to other genes across various pathways are statistically readjusted in these pathways, by considering the whole association network in a hypergeometric model.

CHRONOS

Detects significantly enriched miRNA mediated subpathways per time point from paired miRNA/mRNA time series expression profiles. CHRONOS outperforms current subpathway-based methods into unraveling the inherent dynamic properties of pathways. The method and the supporting R package are flexible by allowing the user to intervene and adapt all discrete phases to the needs of the study under investigation. CHRONOS can assist significantly in complex disease analysis by enabling the experimentalists to acquire a more realistic time-varying dynamical view of the involved perturbed mechanisms.

GRAPE / Gene-Ranking Analysis of Pathway Expression

Allows users to infer whether a pathway is differentially regulated based on the rankings of the genes. GRAPE uses pairwise gene expression ordering within individual samples of a particular collection of genes to create a template representing the consensus ordering for components of the pathway within the collection. The tool can discriminate among different tissue types with accuracies similar to state-of-the-art machine learning techniques within a single dataset.

Subpathway-GM

A powerful analytical method for the identification of biologically meaningful metabolic subpathways. Subpathway-GM integrates ‘interesting genes’ and ‘interesting metabolites’ related to the study condition (e.g. disease) into the corresponding enzyme and metabolite nodes (referred to as signature nodes) within the metabolic pathway. We then analyzed lenient distance similarities of signature nodes within the pathway structure to locate key metabolic cascade subpathway regions. Finally, a hypergeometric test was used to evaluate the enrichment significance of these subpathway regions.

PathwayExplorer

Provides comprehensive and facile mapping of gene or protein expression profiles. The profiles are simultaneously mapped onto the major regulatory, metabolic and cellular pathways available from the KEGG, BioCarta and GenMAPP pathway resources. PathwayExplorer accepts expression data files in a tab-delimited text format and generates high-resolution vector graphic images of mapped pathways. It enables further very compact representations of expression profiles within all available pathways. PathwayExplorer not only unifies the access to different pathway resources, but also combines gene identifiers arbitrarily selectable by the user.

SubpathwayMiner

Obsolete
An R-based software package for flexible pathway identification. SubpathwayMiner facilitates sub-pathway identification of metabolic pathways by using pathway structure information. Additionally, SubpathwayMiner also provides more flexibility in annotating gene sets and identifying the involved pathways (entire pathways and sub-pathways): (i) SubpathwayMiner is able to provide the most up-to-date pathway analysis results for users; (ii) SubpathwayMiner supports multiple species (approximately 100 eukaryotes, 714 bacteria and 52 Archaea) and different gene identifiers (Entrez Gene IDs, NCBI-gi IDs, UniProt IDs, PDB IDs, etc.) in the KEGG GENE database; (iii) the system is quite efficient in cooperating with other R-based tools in biology.

PI / Pathway Inspector

New
Finds patterns of expression in complex RNAseq experiments. PI combines two standard approaches for RNAseq analysis: the identification of differentially expressed genes and a topology-based analysis of enriched pathways. This software is designed to perform several comparisons in one run, such as samples coming from different tissues in two conditions (e.g. roots, leaves, and/or plant body in optimal conditions versus water stress). It also assists the discovery of modulated pathways for every sequenced organism.

ToPASeq

Provides a set of easy-to-use and general tools for topology-based pathway analysis within the R workspace. ToPASeq offers seven distinct topology-based pathway analysis methods that cover wide range of approaches and can be easily applied on both microarray and RNA-Seq data. It also offers a visualization tool that is able to capture all the relevant information about the expression of genes within one pathway. Finally, the functions for pathway conversion extend the application of topology-based pathway analysis to experiments on species other than human.

CePa

An R package aiming to find significant pathways through network topology information. The package has several advantages compared with current pathway enrichment tools. First, pathway node instead of single gene is taken as the basic unit when analysing networks to meet the fact that genes must be constructed into complexes to hold normal functions. Second, multiple network centralities are applied simultaneously to measure importance of nodes from different aspects to make a full view on the biological system. CePa extends standard pathway enrichment methods, which include both over-representation analysis procedure and gene-set analysis procedure.

GOToolBox

Provides a series of programs allowing the functional investigation of groups of genes, based on the Gene Ontology resource. GOToolBox allows 1) the identification of statistically relevant over- or under-represented terms in a gene dataset, 2) the clustering of functionally related genes within a set and 3) the retrieval of genes sharing annotations with a query gene. The user can also constrain the GO annotations to a slim hierarchy or to a given level of the ontology, in order to facilitate the interpretation of the results.

AMBIENT / Active Modules for Bipartite Networks

Analyzes high-throughput data in the context of metabolic models. AMBIENT allows users to find metabolic subnetworks that are significantly affected by a given genetic or environmental change. It permits coordinated metabolic pathway changes to be discovered from transcriptomic or metabolomic data. This tool enables the analysis of disparate data and improves the biological understanding accessible using high-throughput experimental techniques.

TEAK / Topology Enrichment Analysis frameworK

An innovative approach to detect activated subpathways. First, TEAK uses an in-house graph traversal algorithm to extract all root to leaf linear subpathways of a given pathway. Its major contributions include fully accounting for the topological information of subpathways and its ability to provide an interactive view of the data in the KEGG pathways. Furthermore, TEAK’s GUI allows easy accessibility for a diverse set of users, and it implements an efficient algorithm to extract root-to-leaf linear subpathways where BFS and DFS algorithms may fail. Compared with previous approaches, TEAK also does not use differential gene expression analysis to determine modules of interest and is thus not sensitive to threshold values. Finally, by integrating the computational TEAK with experimental approaches, we have discovered and experimentally validated previously uncharacterized subpathways involved in the yeast stress response to nitrogen starvation.

GOSim

Allows to calculate the functional similarity of genes based on various information theoretic similarity concepts for GO terms. GOSim extends existing tools by providing additional lately developed functional similarity measures for genes. These can e.g. be used to cluster genes according to their biological function. Vice versa, they can also be used to evaluate the homogeneity of a given grouping of genes with respect to their GO annotation. GOSim hence provides the researcher with a flexible and powerful tool to combine knowledge stored in GO with experimental data. It can be seen as complementary to other tools that, for instance, search for significantly overrepresented GO terms within a given group of genes.

timeClip

Allows long time course data without replicates. timeClip is based on principal component analysis, regression models and graph decomposition method. It can investigate temporal variations across and within pathways. This tool allows users to effectively display the dynamics of the pathways. It is able to produce good results in term of power, specificity and sensitivity. timeClip can decompose the pathway into a junction tree and highlight the portion mostly dependent on time.

BLMA

Provides a bi-level meta-analysis (BLMA) framework that can be applied in a wide range of applications: functional analysis, pathway analysis, differential expression analysis, and general hypothesis testing. BLMA is able to integrate multiple studies to gain more statistical power, and can be used in conjunction with any statistical hypothesis testing method. It exploits not only the vast number of studies performed in independent laboratories, but also makes better use of the available number of samples within individual studies.

MORPH / MOdule guided Ranking of candidate PatHway genes

An algorithm for revealing missing genes in biological pathways, and demonstrate its capabilities. MORPH supports the analysis in tomato, Arabidopsis and the two new species: rice and the newly sequenced potato genome. The MORPH algorithm is based on two main learning tasks. First, out of a large variety of possible data sources (e.g., gene expression matrices, protein protein interactions, and clustering solutions) it learns which datasets are more informative for the pathway of interest. Second, using the selected data it ranks genes by their association with the pathway of interest.

DEGraph

Implements recent hypothesis testing methods which directly assess whether a particular gene network is differentially expressed between two conditions. This is to be contrasted with the more classical two-step approaches which first test individual genes, then test gene sets for enrichment in differentially expressed genes. These recent methods take into account the topology of the network to yield more powerful detection procedures. DEGraph provides methods to easily test all KEGG pathways for differential expression on any gene expression data set and tools to visualize the results.

DRWPClass

A pathway-based disease classification method. DRWPClass incorporates directed pathway topological information to infer reproducible pathway activities by directed random walk (DRW) and uses them for accurate and robust cancer classification. Experiments on 18 cancer datasets showed that the proposed method yielded a more accurate and robust overall performance compared with several existing gene-based and pathway-based classification methods. The resulting risk-active pathways are more reliable in guiding therapeutic selection and the development of pathway-specific therapeutic strategies.

THINK Back

A set of tools that provide a robust gene set enrichment testing analysis of microarray data, using pathways as a source of biological knowledge. The goal of these tools is to derive high-quality hypotheses regarding microarray data. To do so, each of these tools performs a complex and specific analysis over the biological pathway database. The THINK-Back web services have been implemented with the purpose of enabling scientists to access our tools from anywhere in the world, and have been designed to be executed as long-running tasks. This means that the service can be invoked and will return the results asynchronously.

GrAPPA / Graph Algorithms Pipeline for Pathway Analysis

Provides a visualization tool for graph theoretical analysis. GrAPPA is a web-based application that contains combinatorial methods integrated into a complete microarray analysis toolchain, from uploading raw high-throughput data to visualization of results. This resource offers (i) a larger repository of pre-processing options, including Gaussian graphical models and an expanded set of correlation metrics, (ii) post-processing capabilities, for example, software tools for Bayesian analysis, and (iii) links to related web-centric resources.

PathNet / Pathways based on Network information

A method for identifying enrichment and association between canonical pathways in the context of gene expression data. PathNet utilizes the connectivity information in canonical pathway descriptions to help identify study-relevant pathways and characterize non-obvious dependencies and connections among pathways using gene expression data. PathNet considers both the differential expression of genes and their pathway neighbors to strengthen the evidence that a pathway is implicated in the biological conditions characterizing the experiment. As an adjunct to this analysis, PathNet uses the connectivity of the differentially expressed genes among all pathways to score pathway contextual associations and statistically identify biological relations among pathways.