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http://go.princeton.edu/cgi-bin/GOTermFinder

The Generic GO Term Finder finds the significant GO terms shared among a list of genes from an organism, displaying the results in a table and as a graph (showing the terms and their ancestry). The user may optionally provide background information or a custom gene association file or filter evidence codes. This tool is capable of batch processing multiple queries at once. GO::TermFinder comprises a set of object-oriented Perl modules GO::TermFinder can be used on any system on which Perl can be run, either as a command line application, in single or batch mode, or as a web-based CGI script. This implementation, developed at the Lewis-Sigler Institute at Princeton, depends on the GO-TermFinder software written by Gavin Sherlock and Shuai Weng at Stanford University and the GO:View module written by Shuai Weng. It is made publicly available through the GMOD project. The full source code and documentation for GO:TermFinder are freely available from http://search.cpan.org/dist/GO-TermFinder/. Platform: Online tool, Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: Generic GO Term Finder (RRID:SCR_008870) Copy   

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http://apid.dep.usal.es

APID Interactomes (Agile Protein Interactomes DataServer) provides information on the protein interactomes of numerous organisms, based on the integration of known experimentally validated protein-protein physical interactions (PPIs). The interactome data includes a report on quality levels and coverage over the proteomes for each organism included. APID integrates PPIs from primary databases of molecular interactions (BIND, BioGRID, DIP, HPRD, IntAct, MINT) and also from experimentally resolved 3D structures (PDB) where more than two distinct proteins have been identified. This collection references protein interactors, through a UniProt identifier.

Proper citation: Agile Protein Interactomes DataServer (RRID:SCR_008871) Copy   

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http://plantgrn.noble.org/LegumeIP/

LegumeIP is an integrative database and bioinformatics platform for comparative genomics and transcriptomics to facilitate the study of gene function and genome evolution in legumes, and ultimately to generate molecular based breeding tools to improve quality of crop legumes. LegumeIP currently hosts large-scale genomics and transcriptomics data, including: * Genomic sequences of three model legumes, i.e. Medicago truncatula, Glycine max (soybean) and Lotus japonicus, including two reference plant species, Arabidopsis thaliana and Poplar trichocarpa, with the annotation based on UniProt TrEMBL, InterProScan, Gene Ontology and KEGG databases. LegumeIP covers a total 222,217 protein-coding gene sequences. * Large-scale gene expression data compiled from 104 array hybridizations from L. japonicas, 156 array hybridizations from M. truncatula gene atlas database, and 14 RNA-Seq-based gene expression profiles from G. max on different tissues including four common tissues: Nodule, Flower, Root and Leaf. * Systematic synteny analysis among M. truncatula, G. max, L. japonicus and A. thaliana. * Reconstruction of gene family and gene family-wide phylogenetic analysis across the five hosted species. LegumeIP features comprehensive search and visualization tools to enable the flexible query on gene annotation, gene family, synteny, relative abundance of gene expression.

Proper citation: LegumeIP (RRID:SCR_008906) Copy   

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http://www.hapsample.org/

Web application for simulating SNP genotypes for case-control and affected-child trio studies by resampling from Phase I/II HapMap SNP data. The user provides a list of SNPs to be genotyped, along with a disease model file that describes causal SNPs and their effect sizes. The simulation tool is appropriate for candidate regions or whole-genome scans. (entry from Genetic Analysis Software)

Proper citation: HAP-SAMPLE (RRID:SCR_009234) Copy   

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http://pages.stat.wisc.edu/~yandell/qtl/software/qtlbim/

Software library for QTL Bayesian Interval Mapping that provides a Bayesian model selection approach to map multiple interacting QTL. It works on experimentally inbred lines and performs a genome-wide search to locate multiple potential QTL. The package can handle continuous, binary and ordinal traits. (entry from Genetic Analysis Software)

Proper citation: R/QTLBIM (RRID:SCR_009375) Copy   

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http://meme.nbcr.net/meme/cgi-bin/gomo.cgi

Gene Ontology for Motifs (GOMO) is an alignment- and threshold-free comparative genomics approach for assigning functional roles to DNA regulatory motifs from DNA sequence. The algorithm detects associations between a user-specified DNA regulatory motif (expressed as a position weight matrix; PWM) and Gene Ontology terms. The original method for predicting the roles of transcription factors (TFs starts with a PWM motif describing the DNA-binding affinity of the TF. GOMO uses the PWM to score the promoter region of each gene in the genome for its likelihood to be bound by the TF. The resulting ''''affinity'''' scores are then used to test each term in the Gene Ontology for association with high-scoring genes. The algorithm was subsequently extended to leverage conserved signals using multiple, related species in a comparative approach, which greatly improves the resulting annotations. Platform: Online tool, Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: GOMO - Gene Ontology for Motifs (RRID:SCR_008864) Copy   

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http://rgd.mcw.edu/rgdCuration/?module=portal&func=show&name=renal

An integrated resource for information on genes, QTLs and strains associated with a variety of kidney and renal system conditions such as Renal Hypertension, Polycystic Kidney Disease and Renal Insufficiency, as well as Kidney Neoplasms.

Proper citation: Renal Disease Portal (RRID:SCR_009030) Copy   

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http://vortex.cs.wayne.edu/projects.htm#OE2GO

Onto-Express is a web-based tool in the Onto-Tools suite that performs automated function profiling for a list of differentially expressed genes. However, Onto-Express does not support functional profiling for the organisms that do not have annotations in public domain, or use of custom (i.e. user-defined) ontologies. This limitation is also true for most of the other existing tools for functional profiling, which means that researchers working with uncommon organisms and/or new annotations or ontologies may be forced to construct such profiles manually. Onto-Express To Go (OE2GO) is a new tool added to the Onto-Tools ensemble to address these issues. OE2GO is built on top of OE to leverage its existing functionality. In OE2GO, the users now have an option to use either the Onto-Tools database as a source of functional annotations or provide their own annotations in a separate file. Currently, OE2GO supports annotation file in the Gene Ontology format. Platform: Online tool, Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: Onto-Express To Go (OE2GO) (RRID:SCR_008854) Copy   

•   Source: SciCrunch Registry

http://ageing-map.org/

Database of age-related changes covering different biological levels, including molecular, physiological, psychological and pathological age-related data, to create an interactive portal that serves as a centralized collection of human aging changes and pathologies. To facilitate integrative, system-level studies of aging, the DAA provides a centralized source for aging-related data as well as basic tools to query and visualize the data, including anatomical models. Data in the DAA is manually curated from the literature and retrieved from public databases. For more detailed analyses users are able to download the entire database. More information on how to use the DAA is available on the help page. The DAA primarily focuses on human aging, but also includes supplementary mouse data, in particular gene expression data, to enhance and expand the information on human aging. If you would like to contribute to the database yourself, for instance if you have new data on aging, please use the contribute page to submit your data.

Proper citation: Digital Ageing Atlas (RRID:SCR_009020) Copy   

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http://www.sph.umich.edu/csg/abecasis/MACH/download/

QTL analysis based on imputed dosages/posterior_probabilities.

Proper citation: MACH (RRID:SCR_009621) Copy   

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http://www.eplantsenescence.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 26, 2019. Database of leaf senescence to collect SAGs, mutants, phenotypes and literature references. Leaf senescence has been recognized as the last phase of plant development, a highly ordered process regulated by genes called SAGs. By integrating the data from mutant studies and transgenic analysis, they collected many SAGs related to regulation of the leaf senescence in various species. Additionally, they have categorized SAGs according to their functions in regulation of leaf senescence and used standard criteria to describe senescence associated phenotypes for mutants. Users are welcome to submit the new SAGs.

Proper citation: Leaf Senescence Database (RRID:SCR_010227) Copy   

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http://bioinf.wehi.edu.au/limma/

Software package for the analysis of gene expression microarray data, especially the use of linear models for analyzing designed experiments and the assessment of differential expression.

Proper citation: LIMMA (RRID:SCR_010943) Copy   

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http://django.nubic.northwestern.edu/fundo/

Tool that takes a list of genes and finds relevant diseases based on statistical analysis of the Disease Ontology annotation database. It accepts Entrez gene ids or gene symbols, separated by tabs, newlines, or commas. This list of genes can be obtained by microarray, proteomics, sequencing or other high-throughput screening methods.

Proper citation: FunDO (RRID:SCR_001725) Copy   

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http://www.homozygositymapper.org/

A web-based approach of homozygosity mapping that can handle tens of thousands markers. User can upload their own SNP genotype files to the database. Intuitive graphic interface is provided to view the homozygous stretches, with the ability of zooming into single chromosomes or user-defined chromosome regions. The underlying genotypes in all samples are displayed. The software is also integrated with our candidate gene search engine, GeneDistiller, so that users can interactively determine the most promising gene. (entry from Genetic Analysis Software)

Proper citation: HOMOZYGOSITYMAPPER (RRID:SCR_001714) Copy   

•   Source: SciCrunch Registry

http://weatherby.genetics.utah.edu/cgi-bin/Phevor/PhevorWeb.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on October 28,2025. Tool that integrates phenotype, gene function, and disease information with personal genomic data for improved power to identify disease-causing alleles. It works by combining knowledge resident in multiple biomedical ontologies with the outputs of variant prioritization tools. It does so using an algorithm that propagates information across and between ontologies. This process enables Phevor to accurately reprioritize potentially damaging alleles identified by variant prioritization tools in light of gene function, disease, and phenotype knowledge. Phevor is especially useful for single exome and family trio-based diagnostic analyses, the most commonly occurring clinical scenarios, and ones for which existing personal-genomes diagnostic tools are most inaccurate and underpowered. Phevor not only improves diagnostic accuracy for individuals presenting with established disease phenotypes, but also for those with previously undescribed and atypical disease presentations. Importantly, Phevor is not limited to known diseases, or known disease-causing alleles.

Proper citation: Phevor (RRID:SCR_002273) Copy   

•   Source: SciCrunch Registry

http://igenbio.com/

A web-based genome analysis platform that integrates proprietary functional genomic data, metabolic reconstructions, expression profiling, and biochemical and microbiological data with publicly available information. Focused on microbial genomics, it provides better and faster identification of gene function across all organisms. Building upon a comprehensive genomic database integrated with a collection of microbial metabolic and non-metabolic pathways and using proprietary algorithms, it assigns functions to genes, integrates genes into pathways, and identifies previously unknown or mischaracterized genes, cryptic pathways and gene products. . * Automated and manual annotation of genes and genomes * Analysis of metabolic and non-metabolic pathways to understand organism physiology * Comparison of multiple genomes to identify shared and unique features and SNPs * Functional analysis of gene expression microarray data * Data-mining for target gene discovery * In silico metabolic engineering and strain improvement

Proper citation: ERGO (RRID:SCR_001243) Copy   

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http://www.gensat.org/retina.jsp

Collection of images from cell type-specific protein expression in retina using BAC transgenic mice. Images from cell type-specific protein expression in retina using BAC transgenic mice from GENSAT project.

Proper citation: Retina Project (RRID:SCR_002884) Copy   

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http://hgc.rockefeller.edu/

An interactive web server that enables researchers to prioritize any list of genes by their biological proximity to defined core genes (i.e. genes that are known to be associated with the phenotype), and to predict novel gene pathways.

Proper citation: Human Gene Connectome Server (RRID:SCR_002627) Copy   

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http://www.inmex.ca./INMEX/

A web-based tool to support meta-analysis of multiple gene-expression data sets, as well as to enable integration of data sets from gene expression and metabolomics experiments. INMEX contains three functional modules. The data preparation module supports flexible data processing, annotation and visualization of individual data sets. The statistical analysis module allows researchers to combine multiple data sets based on P-values, effect sizes, rank orders and other features. The significant genes can be examined in functional analysis module for enriched Gene Ontology terms or Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, or expression profile visualization. INMEX has built-in support for common gene/metabolite identifiers (IDs), as well as 45 popular microarray platforms for human, mouse and rat. Complex operations are performed through a user-friendly web interface in a step-by-step manner.

Proper citation: INMEX (RRID:SCR_004173) Copy   

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http://kt.ijs.si/software/SEGS/

A web tool for descriptive analysis of microarray data. The analysis is performed by looking for descriptions of gene sets that are statistically significantly over- or under-expressed between different scenarios within the context of a genome-scale experiments (DNA microarray). Descriptions are defined by using the terms from the Gene Ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and gene-gene interactions found in the ENTREZ database. Gene annotations by GO and KEGG terms can also be found in the ENTREZ database. The tool provides three procedures for testing the enrichment of the gene sets (over- or under-expressed): Fisher's exact test, GSEA and PAGE, and option for combining the results of the tests. Because of the multiple-hypothesis testing nature of the problem, all the p-values are computed using the permutation testing method.

Proper citation: SEGS (RRID:SCR_003554) Copy   

•   Source: SciCrunch Registry