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https://github.com/flo-compbio/xlmhg

Software Python package as a semiparametric test for enrichment in ranked lists. Used for determining gene set enrichment.

Proper citation: XL-mHG (RRID:SCR_016846) Copy   

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http://biotecnun.unav.es:8080/app/TranscriptAchilles

Software genome-wide tool to predict transcript biomarkers of gene essentiality in cancer. This tool can be used to predict new potential target genes with their corresponding biomarkers (either transcript or gene expression).

Proper citation: TranscriptAchilles (RRID:SCR_016849) Copy   

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http://www.cs.cmu.edu/~jernst/stem/

The Short Time-series Expression Miner (STEM) is a Java program for clustering, comparing, and visualizing short time series gene expression data from microarray experiments (~8 time points or fewer). STEM allows researchers to identify significant temporal expression profiles and the genes associated with these profiles and to compare the behavior of these genes across multiple conditions. STEM is fully integrated with the Gene Ontology (GO) database supporting GO category gene enrichment analyses for sets of genes having the same temporal expression pattern. STEM also supports the ability to easily determine and visualize the behavior of genes belonging to a given GO category or user defined gene set, identifying which temporal expression profiles were enriched for these genes. (Note: While STEM is designed primarily to analyze data from short time course experiments it can be used to analyze data from any small set of experiments which can naturally be ordered sequentially including dose response experiments.) Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: Short Time-series Expression Miner (STEM) (RRID:SCR_005016) Copy   

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http://mgc.nci.nih.gov/

NIH initiative project to provide full-length open reading frame (FL-ORF) clones for human, mouse, and rat genes, cow. MGC cDNA clones were obtained by screening of cDNA libraries, by transcript-specific RT-PCR cloning, and by DNA synthesis of cDNA inserts. All MGC sequences are deposited in GenBank and clones can be purchased from distributors of IMAGE consortium. With conclusion of MGC project in March 2009, GenBank records of MGC sequences will be frozen, without further updates. Since definition of what constitutes full-length coding region for some of genes and transcripts for which they have MGC clones will likely change in future, users planning to order MGC clones will need to monitor for these changes. Users can make use of genome browsers and gene-specific databases, such as the UCSC Genome browser, NCBI's Map Viewer, and Entrez Gene, to view relevant regions of genome (browsers) or gene-related information (Entrez Gene).

Proper citation: Mammalian Gene Collection (RRID:SCR_007024) Copy   

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http://www.bumc.bu.edu/cardiovascularproteomics/cpctools/strap/

Software program that automatically annotates a protein list with information that helps in the meaningful interpretation of data from mass spectrometry and other techniques. It takes protein lists as input, in the form of plain text files, protXML files (usually from the TPP), or Dat files from MASCOT search results. From this, it generates protein annotation tables, and a variety of GO charts to aid individual and differential analysis of proteomics data. It downloads information from mainly the Uniprot and EBI QuickGO databases. STRAP requires Windows XP or higher with at least version 3.5 of the Microsoft .NET Framework installed. Platform: Windows compatible

Proper citation: STRAP (RRID:SCR_005675) Copy   

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http://www.ihwg.org/

The IHWG Cell and DNA Bank was established as a shared resource to support the scientific projects of the 13th International Histocompatibility Workshop (IHWS). The Research Cell Bank (RCB), located in Fred Hutchinson Cancer Research Center in Seattle, WA, maintains the IHWG inventory. This comprehensive inventory includes B-Lymphoblastoid Cell Lines (B-LCL) from previous International Workshops, HLA heterozygous and homozygous donors, selected families, and individuals of diverse population groups. The RCB maintains stocks of purified DNA derived from these cell lines, as well as DNA reference panels that provide an extensive array of HLA and HLA-related sequence polymorphisms. The RCB also provides cloned HLA genes and B-LCL transfected with selected HLA genes, which are available on a limited basis.

Proper citation: International Histocompatibility Cell and DNA Bank (RRID:SCR_004871) Copy   

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https://cran.r-project.org/web/packages/LDheatmap/index.html

Software application that plots measures of pairwise linkage disequilibria for SNPs (entry from Genetic Analysis Software)

Proper citation: LDHEATMAP (RRID:SCR_006312) Copy   

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http://www.ebi.ac.uk/Tools/blast2/index.html

It is used to compare a novel sequence with those contained in nucleotide and protein databases by aligning the novel sequence with previously characterized genes.

Proper citation: Washington University Basic Local Alignment Search Tool (RRID:SCR_008285) Copy   

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http://www.gepas.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 12,2023. An integrated packages of tools for microarray data analysis. GEPAS provides a web-based interface that offers diverse analysis options from the early step of preprocessing (normalization of Affymetrix and two-color microarray experiments and other preprocessing options), to the final step of the functional profiling of the experiment (using Gene Ontology, pathways, PubMed abstracts etc.), which include different possibilities for clustering, gene selection, class prediction and array-comparative genomic hybridization management.

Proper citation: Gene Expression Profile Analysis Suite (RRID:SCR_008341) Copy   

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http://wpicr.wpic.pitt.edu/WPICCompGen/hclust/hclust.htm

Software application that is a simple clustering method that can be used to rapidly identify a set of tag SNP's based upon genotype data (entry from Genetic Analysis Software), THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: HCLUST (RRID:SCR_009154) Copy   

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http://www.cdc.gov/genomics/hugenet/default.htm

Human Genome Epidemiology Network, or HuGENet, is a global collaboration of individuals and organizations committed to the assessment of the impact of human genome variation on population health and how genetic information can be used to improve health and prevent disease. Its goals include: establishing an information exchange that promotes global collaboration in developing peer-reviewed information on the relationship between human genomic variation and health and on the quality of genetic tests for screening and prevention; providing training and technical assistance to researchers and practitioners interested in assessing the role of human genomic variation on population health and how such information can be used in practice; developing an updated and accessible knowledge base on the World Wide Web; and promoting the use of this knowledge base by health care providers, researchers, industry, government, and the public for making decisions involving the use of genetic information for disease prevention and health promotion. HuGENet collaborators come from multiple disciplines such as epidemiology, genetics, clinical medicine, policy, public health, education, and biomedical sciences. Currently, there are 4 HuGENet Coordinating Centers for the implementation of HuGENet activities: CDC''s Office of Public Health Genomics, Atlanta, Georgia; HuGENet UK Coordinating Center, Cambridge, UK; University of Ioannina, Greece; University of Ottawa , Ottawa, Canada. HuGENet includes: HuGE e-Journal Club: The HuGE e-Journal Club is an electronic discussion forum where new human genome epidemiologic (HuGE) findings, published in the scientific literature in the CDC''s Office of Public Health Genomics Weekly Update, will be abstracted, summarized, presented, and discussed via a newly created HuGENet listserv. HuGE Reviews: A HuGE Review identifies human genetic variations at one or more loci, and describes what is known about the frequency of these variants in different populations, identifies diseases that these variants are associated with and summarizes the magnitude of risks and associated risk factors, and evaluates associated genetic tests. Reviews point to gaps in existing epidemiologic and clinical knowledge, thus stimulating further research in these areas. HuGE Fact Sheets: HuGE Fact Sheets summarize information about a particular gene, its variants, and associated diseases. HuGE Case Studies: An on-line presentation designed to sharpen your epidemiological skills and enhance your knowledge on genomic variation and human diseases. Its purpose is to train health professionals in the practical application of human genome epidemiology (HuGE), which translates gene discoveries to disease prevention by integrating population-based data on gene-disease relationships and interventions. Students will acquire conceptual and practical tools for critically evaluating the growing scientific literature in specific disease areas. HUGENet Publications: Articles related to the HuGENet movement written by our HuGENet collaborators. HuGE Navigator: An integrated, searchable knowledge base of genetic associations and human genome epidemiology, including information on population prevalence of genetic variants, gene-disease associations, gene-gene and gene- environment interactions, and evaluation of genetic tests. HuGE Workshops: HuGENet has sponsored meetings and workshops with national and international partners since 2001. Available are detailed summaries, agendas or the ability to download speaker slides. HuGE Book: Human Genome Epidemiology: A Scientific Foundation for Using Genetic Information to Improve Health and Prevent Disease. (The findings and conclusions in this book are those of the author(s) and do not necessarily represent the views of the funding agency.) HuGENet Collaborators: HuGENet is interested in establishing collaborations with individuals and organizations working on population based research involving genetic information. HuGE Funding: Funding opportunities for specific population-based genetic epidemiology research projects are available. Research initiatives whose aims include assessing the prevalence of human genetic variation, the association between genetic variants and human diseases, the measurement of gene-gene or gene-environment interaction, and the evaluation of genetic tests for screening and prevention are compiled to create a posted listing. Additional information and application details can be found by clicking on the respective links.

Proper citation: Human Genome Epidemiology Network (RRID:SCR_013117) Copy   

•   Source: SciCrunch Registry

http://llama.mshri.on.ca/funcassociate/

A web-based tool that accepts as input a list of genes, and returns a list of GO attributes that are over- (or under-) represented among the genes in the input list. Only those over- (or under-) representations that are statistically significant, after correcting for multiple hypotheses testing, are reported. Currently 37 organisms are supported. In addition to the input list of genes, users may specify a) whether this list should be regarded as ordered or unordered; b) the universe of genes to be considered by FuncAssociate; c) whether to report over-, or under-represented attributes, or both; and d) the p-value cutoff. A new version of FuncAssociate supports a wider range of naming schemes for input genes, and uses more frequently updated GO associations. However, some features of the original version, such as sorting by LOD or the option to see the gene-attribute table, are not yet implemented. Platform: Online tool

Proper citation: FuncAssociate: The Gene Set Functionator (RRID:SCR_005768) Copy   

•   Source: SciCrunch Registry

http://metagp.ism.ac.jp/

THIS RESOURCE IS NO LONGER IN SERVCE, documented September 2, 2016. Meta Gene Profiler (MetaGP) is a web application tool for discovering differentially expressed gene sets (meta genes) from the gene set library registered in our database. Once user submits gene expression profiles which are categorized into subtypes of conditioned experiments, or a list of genes with the valid pvalues, MetaGP assigns the integrated p-value to each gene set by combining the statistical evidences of genes that are obtained from gene-level analysis of significance. The current version supports the nine Affymetrix GeneChip arrays for the three organisms (human, mouse and rat). The significances of GO terms are graphically mapped onto the directed acyclic graph (DAG). The navigation systems of GO hierarchy enable us to summarize the significance of interesting sub-graphs on the web browser. Platform: Online tool

Proper citation: MetaGeneProfiler (RRID:SCR_005794) Copy   

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http://functionalgenomics.de/ontogate/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 13, 2012. OntoGate provides access to GenomeMatrix (GM) entries from Ontology terms and external datasets which have been associated with ontology terms, to find genes from different species in the GM, which have been mapped to the ontology terms. OntoGate includes a BLAST search of amino acid sequences corresponding to annotated genes. Platform: Online tool

Proper citation: OntoGate (RRID:SCR_005795) Copy   

•   Source: SciCrunch Registry

http://wego.genomics.org.cn/cgi-bin/wego/index.pl

Web Gene Ontology Annotation Plot (WEGO) is a simple but useful tool for plotting Gene Ontology (GO) annotation results. Different from other commercial software for chart creating, WEGO is designed to deal with the directed acyclic graph (DAG) structure of GO to facilitate histogram creation of GO annotation results. WEGO has been widely used in many important biological research projects, such as the rice genome project and the silkworm genome project. It has become one of the useful tools for downstream gene annotation analysis, especially when performing comparative genomics tasks. Platform: Online tool

Proper citation: WEGO - Web Gene Ontology Annotation Plot (RRID:SCR_005827) Copy   

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http://app.aporc.org/NOA/

Network Ontology Analysis (NOA) (abbreviated to NOA) is a freely available collection of Gene Ontology tools aiming to analyze functions of gene network instead of gene list. Network rewiring facilitates the function changes between conditions even with the same gene list. Therefore, it is necessary to annotate the specific function of networks by considering the fundamental roles of interactions from the viewpoint of systems biology. NOA is such a novel functional enrichment analysis method capable to handle both dynamic and static networks. The application of NOA in biological networks shows that NOA can not only capture changing functions in rewiring networks but also find more relevant and specific functions in traditional static networks. Platform: Online tool

Proper citation: Network Ontology Analysis (RRID:SCR_005667) Copy   

•   Source: SciCrunch Registry

http://prorepeat.bioinformatics.nl/

ProRepeat is an integrated curated repository and analysis platform for in-depth research on the biological characteristics of amino acid tandem repeats. ProRepeat collects repeats from all proteins included in the UniProt knowledgebase, together with 85 completely sequenced eukaryotic proteomes contained within the RefSeq collection. It contains non-redundant perfect tandem repeats, approximate tandem repeats and simple, low-complexity sequences, covering the majority of the amino acid tandem repeat patterns found in proteins. The ProRepeat web interface allows querying the repeat database using repeat characteristics like repeat unit and length, number of repetitions of the repeat unit and position of the repeat in the protein. Users can also search for repeats by the characteristics of repeat containing proteins, such as entry ID, protein description, sequence length, gene name and taxon. ProRepeat offers powerful analysis tools for finding biological interesting properties of repeats, such as the strong position bias of leucine repeats in the N-terminus of eukaryotic protein sequences, the differences of repeat abundance among proteomes, the functional classification of repeat containing proteins and GC content constrains of repeats' corresponding codons.

Proper citation: ProRepeat (RRID:SCR_006113) Copy   

•   Source: SciCrunch Registry

http://www.yeastract.com

A curated repository of more than 206000 regulatory associations between transcription factors (TF) and target genes in Saccharomyces cerevisiae, based on more than 1300 bibliographic references. It also includes the description of 326 specific DNA binding sites shared among 113 characterized TFs. Further information about each Yeast gene has been extracted from the Saccharomyces Genome Database (SGD). For each gene the associated Gene Ontology (GO) terms and their hierarchy in GO was obtained from the GO consortium. Currently, YEASTRACT maintains a total of 7130 terms from GO. The nucleotide sequences of the promoter and coding regions for Yeast genes were obtained from Regulatory Sequence Analysis Tools (RSAT). All the information in YEASTRACT is updated regularly to match the latest data from SGD, GO consortium, RSA Tools and recent literature on yeast regulatory networks. YEASTRACT includes DISCOVERER, a set of tools that can be used to identify complex motifs found to be over-represented in the promoter regions of co-regulated genes. DISCOVERER is based on the MUSA algorithm. These algorithms take as input a list of genes and identify over-represented motifs, which can then be compared with transcription factor binding sites described in the YEASTRACT database.

Proper citation: Yeast Search for Transcriptional Regulators And Consensus Tracking (RRID:SCR_006076) Copy   

•   Source: SciCrunch Registry

http://operons.ibt.unam.mx/OperonPredictor/

The Prokaryotic Operon DataBase (ProOpDB) constitutes one of the most precise and complete repository of operon predictions in our days. Using our novel and highly accurate operon algorithm, we have predicted the operon structures of more than 1,200 prokaryotic genomes. ProOpDB offers diverse alternatives by which a set of operon predictions can be retrieved including: i) organism name, ii) metabolic pathways, as defined by the KEGG database, iii) gene orthology, as defined by the COG database, iv) conserved protein motifs, as defined by the Pfam database, v) reference gene, vi) reference operon, among others. In order to limit the operon output to non-redundant organisms, ProOpDB offers an efficient protocol to select the more representative organisms based on a precompiled phylogenetic distances matrix. In addition, the ProOpDB operon predictions are used directly as the input data of our Gene Context Tool (GeConT) to visualize their genomic context and retrieve the sequence of their corresponding 5�� regulatory regions, as well as the nucleotide or amino acid sequences of their genes. The prediction algorithm The algorithm is a multilayer perceptron neural network (MLP) classifier, that used as input the intergenic distances of contiguous genes and the functional relationship scores of the STRING database between the different groups of orthologous proteins, as defined in the COG database. Nevertheless, the operon prediction of our method is not restricted to only those genes with a COG assignation, since we successfully defined new groups of orthologous genes and obtained, by extrapolation, a set of equivalent STRING-like scores based on conserved gene pairs on different genomes. Since the STRING functional relationships scores are determined in an un-bias manner and efficiently integrates a large amount of information coming from different sources and kind of evidences, the prediction made by our MLP are considerably less influenced by the bias imposed in the training procedure using one specific organism.

Proper citation: ProOpDB (RRID:SCR_006111) Copy   

•   Source: SciCrunch Registry

http://bioinformatics.clemson.edu/G-SESAME/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 2,2025. G-SESAME contains a set of tools. They include: tools for measuring the semantic similarity of GO terms; tools for measuring the functional similarity of genes; and tools for clustering genes based on their GO term annotation information. Platform: Online tool, THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: G-SESAME - Gene Semantic Similarity Analysis and Measurement Tools (RRID:SCR_005816) Copy   

•   Source: SciCrunch Registry