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http://www.broadinstitute.org/mmgp/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 6, 2023. Database providing access and limited analysis to the MMGP portal data sets. These include the MMRC funded reference array comparative genomic hybridization (aCGH) and gene expression data and additional public multiple myeloma datasets. The MMGP will be updated with new features such as additional data and analysis tools as they become available.

Proper citation: Multiple Myeloma Genomics Portal (RRID:SCR_003722) Copy   

•   Source: SciCrunch Registry

http://www.netpath.org/

A manually curated resource of signal transduction pathways in humans. All pathways are freely available for download in BioPAX level 3.0, PSI-MI version 2.5 and SBML version 2.1 formats. The slim pathway models representing only core reactions in each pathway are available at NetSlim. All the NetPath pathway models are also submitted to WikiPathways., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: NetPath (RRID:SCR_003567) Copy   

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http://www.hgsc.bcm.tmc.edu/content/hapmap-3-and-encode-3

Draft release 3 for genome-wide SNP genotyping and targeted sequencing in DNA samples from a variety of human populations (sometimes referred to as the HapMap 3 samples). This release contains the following data: * SNP genotype data generated from 1184 samples, collected using two platforms: the Illumina Human1M (by the Wellcome Trust Sanger Institute) and the Affymetrix SNP 6.0 (by the Broad Institute). Data from the two platforms have been merged for this release. * PCR-based resequencing data (by Baylor College of Medicine Human Genome Sequencing Center) across ten 100-kb regions (collectively referred to as ENCODE 3) in 712 samples. Since this is a draft release, please check this site regularly for updates and new releases. The HapMap 3 sample collection comprises 1,301 samples (including the original 270 samples used in Phase I and II of the International HapMap Project) from 11 populations, listed below alphabetically by their 3-letter labels. Five of the ten ENCODE 3 regions overlap with the HapMap-ENCODE regions; the other five are regions selected at random from the ENCODE target regions (excluding the 10 HapMap-ENCODE regions). All ENCODE 3 regions are 100-kb in size, and are centered within each respective ENCODE region. The HapMap 3 and ENCORE 3 data are downloadable from the ftp site.

Proper citation: HapMap 3 and ENCODE 3 (RRID:SCR_004563) Copy   

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http://mltreemap.org/

Data analysis service that analyzes DNA sequences and determines their most likely phylogenetic origin. Its main use is in metagenomics projects, where DNA is isolated directly from natural environments and sequenced (the organisms from which the DNA originates are often entirely undescribed). It will search such sequences for suitable marker genes, and will use maximum likelihood analysis to place them in the ''''Tree of Life''''. This placement is more reliable than simply assessing the closest relative of a sequence using BLAST. More importantly, MLTreeMap decides not only who is the closest relative of your query sequence, but also how deep in the tree of life it probably branched off. Additionally, MLTreeMap searches the sequences for genes, which are coding for key enzymes of important functional pathways, such as RuBisCo, methane monooxygenase or nitrogenase. In case of a positive hit, MLTreeMap uses maximum likelihood analysis to place them in the respective ''''gene-family tree''''.

Proper citation: MLTreeMap (RRID:SCR_004792) Copy   

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http://integromedb.org/

THIS RESOURCE IS NO LONGER IN SERVICE, documented May 26, 2016. Search engine that integrates over 100 curated and publicly contributed data sources and provides integrated views on the genomic, proteomic, transcriptomic, genetic and functional information currently available. Information featured in the database includes gene function, orthologies, gene expression, pathways and protein-protein interactions, mutations and SNPs, disease relationships, related drugs and compounds.

Proper citation: IntegromeDB (RRID:SCR_004620) Copy   

•   Source: SciCrunch Registry

http://exac.broadinstitute.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 9, 2023. An aggregated data platform for genome sequencing data created by a coalition of investigators seeking to aggregate and harmonize exome sequencing data from a variety of large-scale sequencing projects, and to make summary data available for the wider scientific community. The data set provided on this website spans 61,486 unrelated individuals sequenced as part of various disease-specific and population genetic studies. They have removed individuals affected by severe pediatric disease, so this data set should serve as a useful reference set of allele frequencies for severe disease studies. All of the raw data from these projects have been reprocessed through the same pipeline, and jointly variant-called to increase consistency across projects. They ask that you not publish global (genome-wide) analyses of these data until after the ExAC flagship paper has been published, estimated to be in early 2015. If you''re uncertain which category your analyses fall into, please email them. The aggregation and release of summary data from the exomes collected by the Exome Aggregation Consortium has been approved by the Partners IRB (protocol 2013P001477, Genomic approaches to gene discovery in rare neuromuscular diseases).

Proper citation: ExAc (RRID:SCR_004068) Copy   

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http://www.yeastgenome.org/

A curated database that provides comprehensive integrated biological information for Saccharomyces cerevisiae along with search and analysis tools to explore these data. SGD allows researchers to discover functional relationships between sequence and gene products in fungi and higher organisms. The SGD also maintains the S. cerevisiae Gene Name Registry, a complete list of all gene names used in S. cerevisiae which includes a set of general guidelines to gene naming. Protein Page provides basic protein information calculated from the predicted sequence and contains links to a variety of secondary structure and tertiary structure resources. Yeast Biochemical Pathways allows users to view and search for biochemical reactions and pathways that occur in S. cerevisiae as well as map expression data onto the biochemical pathways. Literature citations are provided where available.

Proper citation: SGD (RRID:SCR_004694) Copy   

•   Source: SciCrunch Registry

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://www.flytf.org/

A database of genomic and protein data for Drosophila site-specific transcription factors.

Proper citation: FlyTF.org (RRID:SCR_004123) Copy   

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https://reich.hms.harvard.edu/software

Software application that finds skews in ancestry that are potentially associated with disease genes in recently mixed populations like African Americans. It can be downloaded for either UNIX or Linux.

Proper citation: Ancestrymap (RRID:SCR_004353) Copy   

•   Source: SciCrunch Registry

https://scicrunch.org/scicrunch/data/source/nlx_154697-4/search?q=*

Virtual database indexing brain region gene expression data from mice from: Gene Expression Nervous System Atlas (GENSAT), Allen Mouse Brain Atlas, and Mouse Genome Institute (MGI).

Proper citation: Integrated Brain Gene Expression (RRID:SCR_004197) Copy   

•   Source: SciCrunch Registry

http://selectome.unil.ch/

Database of positive selection based on a rigorous branch-site specific likelihood test. Positive selection is detected using CODEML on all branches of animal gene trees.

Proper citation: Selectome: a Database of Positive Selection (RRID:SCR_004542) Copy   

•   Source: SciCrunch Registry

http://exon.cshl.org/cgi-bin/atprobe/atprobe.pl

Arabidopsis thaliana promoter binding element database that focuses on specific binding elements on known genes, found with experimental methods.

Proper citation: AtProbe (RRID:SCR_005412) Copy   

•   Source: SciCrunch Registry

http://www.phenologs.org/

Database for identifying orthologous phenotypes (phenologs). Mapping between genotype and phenotype is often non-obvious, complicating prediction of genes underlying specific phenotypes. This problem can be addressed through comparative analyses of phenotypes. We define phenologs based upon overlapping sets of orthologous genes associated with each phenotype. Comparisons of >189,000 human, mouse, yeast, and worm gene-phenotype associations reveal many significant phenologs, including novel non-obvious human disease models. For example, phenologs suggest a yeast model for mammalian angiogenesis defects and an invertebrate model for vertebrate neural tube birth defects. Phenologs thus create a rich framework for comparing mutational phenotypes, identify adaptive reuse of gene systems, and suggest new disease genes. To search for phenologs, go to the basic search page and enter a list of genes in the box provided, using Entrez gene identifiers for mouse/human genes, locus ids for yeast (e.g., YHR200W), or sequence names for worm (e.g., B0205.3). It is expected that this list of genes will all be associated with a particular system, trait, mutational phenotype, or disease. The search will return all identified model organism/human mutational phenotypes that show any overlap with the input set of the genes, ranked according to their hypergeometric probability scores. Clicking on a particular phenolog will result in a list of genes associated with the phenotype, from which potential new candidate genes can identified. Currently known phenotypes in the database are available from the link labeled ''Find phenotypes'', where the associated gene can be submitted as queries, or alternately, can be searched directly from the link provided.

Proper citation: Phenologs (RRID:SCR_005529) Copy   

•   Source: SciCrunch Registry

http://www.knockoutmouse.org/

Database of the international consortium working together to mutate all protein-coding genes in the mouse using a combination of gene trapping and gene targeting in C57BL/6 mouse embryonic stem (ES) cells. Detailed information on targeted genes is available. The IKMC includes the following programs: * Knockout Mouse Project (KOMP) (USA) ** CSD, a collaborative team at the Children''''s Hospital Oakland Research Institute (CHORI), the Wellcome Trust Sanger Institute and the University of California at Davis School of Veterinary Medicine , led by Pieter deJong, Ph.D., CHORI, along with K. C. Kent Lloyd, D.V.M., Ph.D., UC Davis; and Allan Bradley, Ph.D. FRS, and William Skarnes, Ph.D., at the Wellcome Trust Sanger Institute. ** Regeneron, a team at the VelociGene division of Regeneron Pharmaceuticals, Inc., led by David Valenzuela, Ph.D. and George D. Yancopoulos, M.D., Ph.D. * European Conditional Mouse Mutagenesis Program (EUCOMM) (Europe) * North American Conditional Mouse Mutagenesis Project (NorCOMM) (Canada) * Texas A&M Institute for Genomic Medicine (TIGM) (USA) Products (vectors, mice, ES cell lines) may be ordered from the above programs.

Proper citation: International Knockout Mouse Consortium (RRID:SCR_005574) Copy   

•   Source: SciCrunch Registry

http://www2.hu-berlin.de/wikizbnutztier/software/CandiSNPer/

A webtool which helps in characterizing Single Nucleotide Polymorphisms (SNPs) that are located in the vicinity of an SNP of interest (start SNP). Along with the computation of the maximal Linkage Disequilibrium (LD) region around the start SNP. CandiSNPer provides additional information with respect to the molecular consequences of the SNPs and the genes located in the LD region.

Proper citation: CandiSNPer (RRID:SCR_005173) Copy   

•   Source: SciCrunch Registry

http://wishart.biology.ualberta.ca/polysearch/index.htm

A web-based tool that supports more than 50 different classes of queries against nearly a dozen different types of text, scientific abstract or bioinformatic databases. The typical query supported by PolySearch is Given X, find all Y''s where X or Y can be diseases, tissues, cell compartments, gene/protein names, SNPs, mutations, drugs and metabolites. PolySearch also exploits a variety of techniques in text mining and information retrieval to identify, highlight and rank informative abstracts, paragraphs or sentences.

Proper citation: PolySearch (RRID:SCR_005291) Copy   

•   Source: SciCrunch Registry

http://www.plexdb.org/plex.php?database=Barley/funcexpression.php

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 11, 2012. FuncExpression is a web-based resource for functional interpretation of large scale genomics data. FuncExpression can be used for the functional comparison of plant, animal, and fungal gene name lists generated from genomics and proteomics experiments. Multiple gene lists can be classified, compared and visualized. FuncExpression supports two way-integration of plant gene functional information and the gene expression data, which allows for further cross-validation with plant microarray data from related experiments at BarleyBase. Platform: Online tool

Proper citation: FuncExpression (RRID:SCR_005773) Copy   

•   Source: SciCrunch Registry

http://www.dbs.ifi.lmu.de/~bundschu/LHGDN.html

A text mining derived database with focus on extracting and classifying gene-disease associations with respect to several biomolecular conditions. It uses a machine learning based algorithm to extract semantic gene-disease relations from a textual source of interest. The semantic gene-disease relations were extracted with F-measures of 78. More specifically, the textual source utilized here originates from Entrez Gene''''s GeneRIF (Gene Reference Into Function) database (Mitchell, et al., 2003). LHGDN was created based on a GeneRIF version from March 31st, 2009, consisting of 414241 phrases. These phrases were further restricted to the organism Homo sapiens, which resulted in a total of 178004 phrases. We benchmark our approach on two different tasks. The first task is the identification of semantic relations between diseases and treatments. The available data set consists of manually annotated PubMed abstracts. The second task is the identification of relations between genes and diseases from a set of concise phrases, so-called GeneRIF (Gene Reference Into Function) phrases. In our experimental setting, we do not assume that the entities are given, as is often the case in previous relation extraction work. Rather the extraction of the entities is solved as a subproblem. Compared with other state-of-the-art approaches, we achieve very competitive results on both data sets. To demonstrate the scalability of our solution, we apply our approach to the complete human GeneRIF database. The resulting gene-disease network contains 34758 semantic associations between 4939 genes and 1745 diseases. The gene-disease network is publicly available as a machine-readable RDF graph. We extend the framework of Conditional Random Fields towards the annotation of semantic relations from text and apply it to the biomedical domain. Our approach is based on a rich set of textual features and achieves a performance that is competitive to leading approaches. The model is quite general and can be extended to handle arbitrary biological entities and relation types. The resulting gene-disease network shows that the GeneRIF database provides a rich knowledge source for text mining.

Proper citation: Literature-derived human gene-disease network (RRID:SCR_005653) Copy   

•   Source: SciCrunch Registry

http://bigg.ucsd.edu/

A knowledgebase of Biochemically, Genetically and Genomically structured genome-scale metabolic network reconstructions. BiGG integrates several published genome-scale metabolic networks into one resource with standard nomenclature which allows components to be compared across different organisms. BiGG can be used to browse model content, visualize metabolic pathway maps, and export SBML files of the models for further analysis by external software packages. Users may follow links from BiGG to several external databases to obtain additional information on genes, proteins, reactions, metabolites and citations of interest.

Proper citation: BiGG Database (RRID:SCR_005809) Copy   

•   Source: SciCrunch Registry