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http://sing.ei.uvigo.es/GC/

Tool for extensively testing the discriminatory power of biologically relevant gene sets in microarray data classification. While the user can work with different gene set collections and several microarray data files to configure specific classification experiments, the tool is able to run several tests in parallel. It is able to render valuable information for diagnostic analyses and clinical management decisions based on systematically evaluating custom hypothesis over different data sets using complementary classifiers, a key aspect in clinical research.

Proper citation: GeneCommittee (RRID:SCR_004168) Copy   

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http://sourceforge.net/projects/hlaseq/

An open-source software tool for accurate genotyping the human HLA genes from Illumina GA high-throughput sequencing data.

Proper citation: HLASeq (RRID:SCR_004185) Copy   

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https://brainspan.org/

Atlas of developing human brain for studying transcriptional mechanisms involved in human brain development. One of the BrainSpan datasets, Exon microarray summarized to genes, is presented. It is a downloadable archive of files containing normalized RNA-Seq expression values for analysis.

Proper citation: BrainSpan (RRID:SCR_004219) Copy   

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http://bioinfo.au.tsinghua.edu.cn/software/TAGS/

Software tool for gene set enrichment analysis for expression time series, which can incorporate existing knowledge and analyze the dynamic property of a group of genes that have functional or structural associations. The installation file is for Windows., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: TAGS (RRID:SCR_004294) Copy   

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http://dkcoin.org/

THIS RESOURCE IS NO LONGER IN SERVICE, documented October 13, 2014. The resource has moved to the NIDDKInformation Network (dkNET) project. Contact them at info_at_dknet.org with any questions. Database of large pools of data relevant to the mission of NIDDKwith the goal of developing a community-based network for integration across disciplines to include the larger DKuniverse of diseases, investigators, and potential users. The focus is on greater use of this data with the objective of adding value by breaking down barriers between sites to facilitate linking of different datasets. To date (2013/06/10), a total of 1,195 resources have been associated with one or more genes. Of 11,580 total genes associated with resources, the ten most represented are associated with 359 distinct resources. The main method by which they currently interconnect resources between the providers is via EntrezGene identifiers. A total of 780 unique genes provide the connectivity between 3,159 resource pairs across consortia. To further increase interconnectivity, the groups have been further annotating their data with additional gene identifiers, publications, and ontology terms from selected Open Biological and Biomedical Ontologies (OBO).

Proper citation: dkCOIN (RRID:SCR_004438) Copy   

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http://nematode.lab.nig.ac.jp/

Expression pattern map of the 100Mb genome of the nematode Caenorhabditis elegans through EST analysis and systematic whole mount in situ hybridization. NEXTDB is the database to integrate all information from their expression pattern project and to make the data available to the scientific community. Information available in the current version is as follows: * Map: Visual expression of the relationships among the cosmids, predicted genes and the cDNA clones. * Image: In situ hybridization images that are arranged by their developmental stages. * Sequence: Tag sequences of the cDNA clones are available. * Homology: Results of BLASTX search are available. Users of the data presented on our web pages should not publish the information without our permission and appropriate acknowledgment. Methods are available for: * In situ hybridization on whole mount embryos of C.elegans * Protocols for large scale in situ hybridization on C.elegans larvae

Proper citation: NEXTDB (RRID:SCR_004480) Copy   

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http://stemcellcommons.org/

Open source environment for sharing, processing and analyzing stem cell data bringing together stem cell data sets with tools for curation, dissemination and analysis. Standardization of the analytical approaches will enable researchers to directly compare and integrate their results with experiments and disease models in the Commons. Key features of the Stem Cell Commons * Contains stem cell related experiments * Includes microarray and Next-Generation Sequencing (NGS) data from human, mouse, rat and zebrafish * Data from multiple cell types and disease models * Carefully curated experimental metadata using controlled vocabularies * Export in the Investigation-Study-Assay tabular format (ISA-Tab) that is used by over 30 organizations worldwide * A community oriented resource with public data sets and freely available code in public code repositories such as GitHub Currently in development * Development of Refinery, a novel analysis platform that links Commons data to the Galaxy analytical engine * ChIP-seq analysis pipeline (additional pipelines in development) * Integration of experimental metadata and data files with Galaxy to guide users to choose workflows, parameters, and data sources Stem Cell Commons is based on open source software and is available for download and development.

Proper citation: Stem Cell Commons (RRID:SCR_004415) Copy   

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http://en.wikibooks.org/wiki/Handbook_of_Genetic_Counseling

The Handbook of Genetic Counseling is a wikibook designed as an introduction to the discipline and practice of genetic counseling. The text provides an introduction to genetic counseling as a clinical practice and includes sample counseling outlines and letters for students of genetic counseling. Additional outline and letter examples are highly encouraged. Wikibooks contains books on many medical topics; however, no warranty whatsoever is made that any of the books are accurate.

Proper citation: Handbook of Genetic Counseling (RRID:SCR_004564) Copy   

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http://www.ncbi.nlm.nih.gov/biosystems/

Database that provides access to biological systems and their component genes, proteins, and small molecules, as well as literature describing those biosystems and other related data throughout Entrez. A biosystem, or biological system, is a group of molecules that interact directly or indirectly, where the grouping is relevant to the characterization of living matter. BioSystem records list and categorize components, such as the genes, proteins, and small molecules involved in a biological system. The companion FLink tool, in turn, allows you to input a list of proteins, genes, or small molecules and retrieve a ranked list of biosystems. A number of databases provide diagrams showing the components and products of biological pathways along with corresponding annotations and links to literature. This database was developed as a complementary project to (1) serve as a centralized repository of data; (2) connect the biosystem records with associated literature, molecular, and chemical data throughout the Entrez system; and (3) facilitate computation on biosystems data. The NCBI BioSystems Database currently contains records from several source databases: KEGG, BioCyc (including its Tier 1 EcoCyc and MetaCyc databases, and its Tier 2 databases), Reactome, the National Cancer Institute's Pathway Interaction Database, WikiPathways, and Gene Ontology (GO). It includes several types of records such as pathways, structural complexes, and functional sets, and is desiged to accomodate other record types, such as diseases, as data become available. Through these collaborations, the BioSystems database facilitates access to, and provides the ability to compute on, a wide range of biosystems data. If you are interested in depositing data into the BioSystems database, please contact them.

Proper citation: NCBI BioSystems Database (RRID:SCR_004690) Copy   

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http://www.ebi.ac.uk/QuickGO/

A web-based browser for Gene Ontology terms and annotations, which is provided by the UniProtKB-GOA group at the EBI. It is able to offer a range of facilities including bulk downloads of GO annotation data which can be extensively filtered by a range of different parameters and GO slim set generation. The software for QuickGO is freely available under the Apache 2 license. QuickGO can supply GO term information and GO annotation data via REST web services.

Proper citation: QuickGO (RRID:SCR_004608) Copy   

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http://openflydata.org/

A cross-database search service for Drosophila gene expression data, including microarray data from FlyAtlas and in situ images from BDGP and Fly-TED. The applications provide different ways to search for and compare gene expression data for the fruit fly Drosophila melanogaster. You may Search Gene Expression Data by Gene, Gene Batch, and by Tissue Expression Profile. A number of Web services (SPARQL endpoints) are provided from this site which may be queried programmatically for data.

Proper citation: OpenFlyData.org (RRID:SCR_004807) 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://inparanoid.sbc.su.se/cgi-bin/index.cgi

Collection of pairwise comparisons between 100 whole genomes generated by a fully automatic method for finding orthologs and in-paralogs between TWO species. Ortholog clusters in the InParanoid are seeded with a two-way best pairwise match, after which an algorithm for adding in-paralogs is applied. The method bypasses multiple alignments and phylogenetic trees, which can be slow and error-prone steps in classical ortholog detection. Still, it robustly detects complex orthologous relationships and assigns confidence values for in-paralogs. The original data sets can be downloaded.

Proper citation: InParanoid: Eukaryotic Ortholog Groups (RRID:SCR_006801) Copy   

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http://redfly.ccr.buffalo.edu

Curated collection of known Drosophila transcriptional cis-regulatory modules (CRMs) and transcription factor binding sites (TFBSs). Includes experimentally verified fly regulatory elements along with their DNA sequence, associated genes, and expression patterns they direct. Submission of experimentally verified cis-regulatory elements that are not included in REDfly database are welcome.

Proper citation: REDfly Regulatory Element Database for Drosophilia (RRID:SCR_006790) Copy   

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http://scicrunch.org

THIS RESOURCE IS NO LONGER IN SERVICE, documented on August 27, 2019.

Database for those interested in the consequences of Factor VIII genetic variation at the DNA and protein level, it provides access to data on the molecular pathology of haemophilia A. The database presents a review of the structure and function of factor VIII and the molecular genetics of haemophilia A, a real time update of the biostatistics of each parameter in the database, a molecular model of the A1, A2 and A3 domains of the factor VIII protein (based on the crystal structure of caeruloplasmin) and a bulletin board for discussion of issues in the molecular biology of factor VIII. The database is completely updated with easy submission of point mutations, deletions and insertions via e-mail of custom-designed forms. A methods section devoted to mutation detection is available, highlighting issues such as choice of technique and PCR primer sequences. The FVIII structure section now includes a download of a FVIII A domain homology model in Protein Data Bank format and a multiple alignment of the FVIII amino-acid sequences from four species (human, murine, porcine and canine) in addition to the virtual reality simulations, secondary structural data and FVIII animation already available. Finally, to aid navigation across this site, a clickable roadmap of the main features provides easy access to the page desired. Their intention is that continued development and updating of the site shall provide workers in the fields of molecular and structural biology with a one-stop resource site to facilitate FVIII research and education. To submit your mutants to the Haemophilia A Mutation Database email the details. (Refer to Submission Guidelines)

Proper citation: HAMSTeRS - The Haemophilia A Mutation Structure Test and Resource Site (RRID:SCR_006883) Copy   

•   Source: SciCrunch Registry

http://sourceforge.net/p/fastsemsim/home/Home/

A package that implements several semantic similarity measures. It is both a library and an end-user application, featuring an intuitive graphical user interface (GUI). It has been implemented with the aim of being fast, expandable, and easy to use. It allows the user to work with the most updated version of GO database and customizable annotation corpora. It provides a set of logically-organized classes that can be easily exploited to both integrate semantic similarity into different analysis pipelines and extend the library with new measures. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: FastSemSim (RRID:SCR_006919) Copy   

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http://gtrnadb.ucsc.edu

This genomic tRNA database contains tRNA gene predictions made by the program tRNAscan-SE (Lowe & Eddy, Nucl Acids Res 25: 955-964, 1997) on complete or nearly complete genomes. Unless otherwise noted, all annotation is automated, and has not been inspected for agreement with published literature. Transfer RNAs (tRNAs) represent the single largest, best-understood class of non-protein coding RNA genes found in all living organisms. By far, the major source of new tRNAs is computational identification of genes within newly sequenced genomes. To organize the rapidly growing collection and enable systematic analyses, we created the Genomic tRNA Database (GtRNAdb). The web resource provides overview statistics of tRNA genes within each analyzed genome, including information by isotype and genetic locus, easily downloadable primary sequences, graphical secondary structures and multiple sequence alignments. Direct links for each gene to UCSC eukaryotic and microbial genome browsers provide graphical display of tRNA genes in the context of all other local genetic information. The database can be searched by primary sequence similarity, tRNA characteristics or phylogenetic group. Inevitably with automated sequence analysis, we find exceptions to general identification rules, isoacceptor type predictions (esp. due to variable post-transcriptional anticodon modification), and questionable tRNA identifications (due to pseudogenes, SINES, or other tRNA-derived elements). We attempt to document all cases we come across, and welcome feedback on new or unrecognized discrepancies.

Proper citation: GtRNAdb - Genomic tRNA Database (RRID:SCR_006939) Copy   

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http://www.europhenome.org

Open source software system for capturing, storing and analyzing raw phenotyping data from SOPs contained in EMPReSS, it provides access to raw and annotated mouse phenotyping data generated from primary pipelines such as EMPReSSlim and secondary procedures from specialist centers. Mutants of interest can be identified by searching the gene or the predicted phenotype. You can also access phenotype data from the EMPReSSlim Pipeline for inbred mouse strains. Initially EuroPhenome was developed within the EUMORPHIA programme to capture and store pilot phenotyping data obtained on four background strains (C57BL/6J, C3H/HeBFeJ, BALB/cByJ and 129/SvPas). EUMORPHIA (European Union Mouse Research for Public Health and Industrial Applications) was a large project comprising of 18 research centers in 8 European countries, with the main focus of the project being the development of novel approaches in phenotyping, mutagenesis and informatics to improve the characterization of mouse models for understanding human molecular physiology and pathology. The current version of EuroPhenome is capturing data from the EUMODIC project as well as the WTSI MGP, HMGU GMC pipeline and the CMHD. EUMODIC is undertaking a primary phenotype assessment of up to 500 mouse mutant lines derived from ES cells developed in the EUCOMM project as well as other lines. Lines showing an interesting phenotype will be subject to a more in depth assessment. EUMODIC is building upon the comprehensive database of standardized phenotyping protocols, called EMPReSS, developed by the EUMORPHIA project. EUMODIC has developed a selection of these screens, called EMPReSSslim, to enable comprehensive, high throughput, primary phenotyping of large numbers of mice. Phenovariants are annotated using a automated pipeline, which assigns a MP term if the mutant data is statistically different to the baseline data. This data is shown in the Phenomap and the mine for a mutant tool. Please note that a statistically significant result and the subsequent MP annotation does not necessarily mean a true phenovariant. There are other factors that could cause this result that have not been accounted for in the analysis. It is the responsibility of the user to download the data and use their expert knowledge or further analysis to decide whether they agree or not. EuroPhenome is primarily based in the bioinformatics group at MRC Harwell. The development of EuroPhenome is in collaboration with the Helmholtz Zentrum Munchen, Germany, the Wellcome Trust Sanger Institute, UK and the Institut Clinique de la Souris, France.

Proper citation: Europhenome Mouse Phenotyping Resource (RRID:SCR_006935) Copy   

•   Source: SciCrunch Registry

http://autismkb.cbi.pku.edu.cn/

Genetic factors contribute significantly to ASD. AutismKB is an evidence-based knowledgebase of Autism spectrum disorder (ASD) genetics. The current version contains 2193 genes (99 syndromic autism related genes and 2135 non-syndromic autism related genes), 4617 Copy Number Variations (CNVs) and 158 linkage regions associated with ASD by one or more of the following six experimental methods: # Genome-Wide Association Studies (GWAS); # Genome-wide CNV studies; # Linkage analysis; # Low-scale genetic association studies; # Expression profiling; # Other low-scale gene studies. Based on a scoring and ranking system, 99 syndromic autism related genes and 383 non-syndromic autism related genes (434 genes in total) were designated as having high confidence. Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder with a prevalence of 1.0-2.6%. The three core symptoms of ASD are: # impairments in reciprocal social interaction; # communication impairments; # presence of restricted, repetitive and stereotyped patterns of behavior, interests and activities.

Proper citation: AutismKB (RRID:SCR_006937) Copy   

•   Source: SciCrunch Registry

http://www.dkfz.de/en/mga/Groups/LIFEdb-Database.html

Database that integrates large-scale functional genomics assays and manual cDNA annotation with bioinformatics gene expression and protein analysis. LifeDB integrates data regarding full length cDNA clones and data on expression of encoded protein and their subcellular localization on mammalian cell line. LifeDB enables the scientific community to systematically search and select genes, proteins as well as cDNA of interest by specific database identifiers as well as gene name. It enables to visualize cDNA clone and subcellular location of proteins. It also links the results to external biological databases in order to provide a broader functional information. LifeDB also provides an annotation pipeline which facilitates an improved mapping of clones to known human reference transcripts from the RefSeq database and the Ensembl database. An advanced web interface enables the researchers to view the data in a more user friendly manner. Users can search using any one of the following search options available both in Search gene and cDNA clones and Search Sub-cellular locations of human proteins: By Keyword, By gene/transcript identifier, By plate name, By clone name, By cellular location. * The Search genes and cDNA clones results include: Gene Name, Ensemble ID, Genomic Region, Clone name, Plate name, Plate position, Classification class, Synonymous SNP''s, Non- synonymous SNP''s, Number of ambiguous positions, and Alignment with reference genes. * The Search sub-cellular locations of human proteins results include: Subcellular location, Gene Name, Ensemble ID, Clone name, True localization, Images, Start tag and End tag. Every result page has an option to download result data (excluding the microscopy images). On click of ''Download results as CSV-file'' link in the result page the user will be given a choice to open or save result data in form of a CSV (Comma Separated Values) file. Later the CSV file can be easily opened using Excel or OpenOffice.

Proper citation: LifeDB (RRID:SCR_006899) Copy   

•   Source: SciCrunch Registry