Tools   Select Another Resource Report Type

https://phenogen.org

Website for analyzing microarray data. Software toolbox for storing, analyzing and integrating microarray data and related genotype and phenotype data. The site is particularly suited for combining QTL and microarray data to search for candidate genes contributing to complex traits. In addition, the site allows, if desired by the investigators, sharing of the data. Investigators can conduct in-silico microarray experiments using their own and/or shared data. There are five major sections of the site: Genome/Transcriptome Data Browser, Microarray Analysis Tools, Gene List Analysis Tools, QTL Tools, and Downloads. The genome/transcriptome data browser combines a genome browser with all the microarray, RNA-Seq, and Genomic Sequencing data. This provides an effective platform to view all of this data side by side. Source code is available on GitHub.

Proper citation: PhenoGen Informatics (RRID:SCR_001613) Copy   

•   Source: SciCrunch Registry

http://www.norcomm.org/index.htm

Large-scale research initiative focused on developing and distributing a library of mouse embryonic stem (ES) cell lines carrying single gene trapped or targeted mutations across the mouse genome. NorCOMM's large and growing archive of ES cells is publicly available on a cost-recovery basis from the Canadian Mouse Mutant Repository. As an international public resource, access to clones is unrestricted and nonexclusive. Through NorCOMM's affiliation with the Canadian Mouse Consortium (CMC), NorCOMM also provides clients with a single point of access to regional mouse derivation, phenotyping, genetic and archiving services across Canada. These value-added services can help your company harness NorCOMM's resources for drug discovery, target discovery and preclinical validation.

Proper citation: North American Conditional Mouse Mutagenesis Project (RRID:SCR_001614) Copy   

•   Source: SciCrunch Registry

http://globin.cse.psu.edu/

Data and tools for studying the function of DNA sequences, with an emphasis on those involved in the production of hemoglobin. It includes information about naturally-occurring human hemoglobin mutations and their effects, experimental data related to the regulation of the beta-like globin gene cluster, and software tools for comparing sequences with one another to discover regions that are likely to play significant roles.

Proper citation: Globin Gene Server (RRID:SCR_001480) Copy   

•   Source: SciCrunch Registry

http://www.animalgenome.org/cgi-bin/QTLdb/index

Database of trait mapping data, i.e. QTL (phenotype / expression, eQTL), candidate gene and association data (GWAS) and copy number variations (CNV) mapped to livestock animal genomes, to facilitate locating and comparing discoveries within and between species. New data and database tools are continually developed to align various trait mapping data to map-based genome features, such as annotated genes. QTLdb is open to house QTL/association date from other animal species where feasible. Most scientific journals require that any original QTL/association data be deposited into public databases before paper may be accepted for publication. User curator accounts are provided for direct data deposit. Users can download QTLdb data from each species or individual chromosome.

Proper citation: Animal QTLdb (RRID:SCR_001748) Copy   

•   Source: SciCrunch Registry

http://www.sanbi.ac.za/resources/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23, 2022. The South African National Bioinformatics Institute delivers biomedical discovery appropriate to both international and African context. Researchers at SANBI perform the highest level of research and provide excellence in education. Research at SANBI has set well recognized milestones in the field of computational biology. The tools and techniques used have not only been developed but also implemented across heterogeneous domains of advanced research. Local and international efforts have driven our discoveries. Until recently, the core of SANBIs research has focused upon gene expression biology. Methods developed and applied at SANBI revolve around a greater understanding of the underlying causes of diseases. SANBI approaches the problem by comparison of genes, genomes and transcriptomes. It uses computational gene expression biology to create novel biological insights and to provide biomarkers for experimental validation. It also performs analysis of human genome variation, transcriptional diversity on both the expression and splicing level and the unravelling of transcriptional regulatory networks. Resources - Hinv, STACKdb, Malaria resources and Trypanosome databases are available for on-line seaching. - SANBI offers WCD, STACKdb, stackPACK and eVOC and the eVOKE viewer as tools that can be downloaded. Sponsors: SANBI receives funding and support from a range of organisations in South Africa and Internationally. Organisations currently supporting SANBI include: South Africa * South African Medical Research Council * South African AIDS Vaccine Initiative * National Bioinformatics Network * National Research Foundation * Claude Leon Foundation * International Business Machines Inc. Europe * European Unions 6th Framework Programme * World Health Organization USA * US National Institutes of Health * Fogarty International Centre * Ludwig Institute for Cancer Research

Proper citation: South African National Bioinformatics Institute: Resources (RRID:SCR_001867) Copy   

•   Source: SciCrunch Registry

http://www.doe-mbi.ucla.edu/

The UCLA-DOE Institute for Genomics and Proteomics carries out research in bioenergy, structural biology, genomics and proteomics, consistent with the research mission of the United States Department of Energy. Major interests of the 12 Principal Investigators and 9 Associate Members include systems approaches to organisms, structural biology, bioinformatics, and bioenergetic systems. The Institute sponsors 5 Core Technology Centers, for X-ray and NMR structural determination, bioinformatics and computation, protein expression and purification, and biochemical instrumentation. Services offered by this Institute: - Databases: * DIP (The Database of Interacting Proteins): The DIPTM database catalogs experimentally determined interactions between proteins. It combines information from a variety of sources to create a single, consistent set of protein-protein interactions. * ProLinks Database of Functional Linkages: The Prolinks database is a collection of inference methods used to predict functional linkages between proteins. These methods include the Phylogenetic Profile method which uses the presence and absence of proteins across multiple genomes to detect functional linkages; the Gene Cluster method, which uses genome proximity to predict functional linkage; Rosetta Stone, which uses a gene fusion event in a second organism to infer functional relatedness; and the Gene Neighbor method, which uses both gene proximity and phylogenetic distribution to infer linkage. - Data-to-Structure Servers: * SAVEs Structure Verification Server * Merohedral Twinning Test Server * SER Surface Entropy Reduction Server * VERIFY3D Structure Verification Server * ERRAT Structure Verification Server - Structure-to-Function Servers: * ProKnow Protein Functionator * Hot Patch Functional Site Locator

Proper citation: University of California at Los Angeles - Department of Energy Institute for Genomics and Proteomics (RRID:SCR_001921) Copy   

•   Source: SciCrunch Registry

http://meme-suite.org/

Suite of motif-based sequence analysis tools to discover motifs using MEME, DREME (DNA only) or GLAM2 on groups of related DNA or protein sequences; search sequence databases with motifs using MAST, FIMO, MCAST or GLAM2SCAN; compare a motif to all motifs in a database of motifs; associate motifs with Gene Ontology terms via their putative target genes, and analyze motif enrichment using SpaMo or CentriMo. Source code, binaries and a web server are freely available for noncommercial use.

Proper citation: MEME Suite - Motif-based sequence analysis tools (RRID:SCR_001783) Copy   

•   Source: SciCrunch Registry

http://sammeth.net/confluence/display/ASTA/2+-+Download

Tool that extracts and displays alternative splicing (AS) events from a given genomic annotation of exon-intron gene coordinates. By comparing all given transcripts, it detects the variations in their splicing structure and identifies all AS events (like exon skipping, alternate donor, etc) by assigning to each of them an AS code. It provides a visual summary of the AS landscape in the analyzed dataset, the possibility to browse the results on the UCSC website or to download them in GTF or ASTA format. You can use AStalavista for any genome by providing your own annotation set, the identifier of your gene(s) of interest, or analyze the AS landscape of reference annotation datasets like Gencode, RefSeq, Ensembl, FlyBase, etc.

Proper citation: AStalavista (RRID:SCR_001815) Copy   

•   Source: SciCrunch Registry

http://compbio.dfci.harvard.edu/tgi/

THIS RESOURCE IS NO LONGER IN SERVICE, documented May 10, 2017. A pilot effort that has developed a centralized, web-based biospecimen locator that presents biospecimens collected and stored at participating Arizona hospitals and biospecimen banks, which are available for acquisition and use by researchers. Researchers may use this site to browse, search and request biospecimens to use in qualified studies. The development of the ABL was guided by the Arizona Biospecimen Consortium (ABC), a consortium of hospitals and medical centers in the Phoenix area, and is now being piloted by this Consortium under the direction of ABRC. You may browse by type (cells, fluid, molecular, tissue) or disease. Common data elements decided by the ABC Standards Committee, based on data elements on the National Cancer Institute''s (NCI''s) Common Biorepository Model (CBM), are displayed. These describe the minimum set of data elements that the NCI determined were most important for a researcher to see about a biospecimen. The ABL currently does not display information on whether or not clinical data is available to accompany the biospecimens. However, a requester has the ability to solicit clinical data in the request. Once a request is approved, the biospecimen provider will contact the requester to discuss the request (and the requester''s questions) before finalizing the invoice and shipment. The ABL is available to the public to browse. In order to request biospecimens from the ABL, the researcher will be required to submit the requested required information. Upon submission of the information, shipment of the requested biospecimen(s) will be dependent on the scientific and institutional review approval. Account required. Registration is open to everyone.. Documented on August 19,2019.The goal of The Gene Index Project is to use the available Expressed Sequence Transcript (EST) and gene sequences, along with the reference genomes wherever available, to provide an inventory of likely genes and their variants and to annotate these with information regarding the functional roles played by these genes and their products. The promise of genome projects has been a complete catalog of genes in a wide range of organisms. While genome projects have been successful in providing reference genome sequences, the problem of finding genes and their variants in genomic sequence remains an ongoing challenge. TGI has created an inventory that contains genes and their variants together with description. In addition, this resource is attempting to use these catalogs to find links between genes and pathways in different species and to provide lists of features within completed genomes that can aid in the understanding of how gene expression is regulated. DATABASES *Eukaryotic Gene Orthologues (formerly known as TOGA - TIGR Orthologous Gene Alignment): Eukaryotic Gene Orthologues (EGO) at DFGI are generated by pair-wise comparison between the Tentative Consensus (TC) sequences that comprise the Dana Farber Gene Indices from individual organisms. The reciprocal pairs of the best match were clustered into individual groups and multiple sequence alignments were displayed for each group. *GeneChip Oncology Database (GCOD):Cancer gene expression database is a collection of publicly available microarray expression data on Affymetrix GeneChip Arrays related to human cancers. Currently only datasets with available raw data (Affymetrix .CEL files) are processed. All processed datasets were subjected to extensive manual curation, uniform processing and consistent quality control. You can browse the experiments in our collection, perform statistical analysis, and download processed data; or to search gene expression profiles using Entrez gene symbol, Unigene ID, or Affymetrix probeset ID. *Gene Indices: As of July 1, 2008, there are 111 publicly available gene indices. They are separated into 4 categories for better organization and easier access. Animal: 41, Plant: 45, Protist: 15, Fungal: 10 *Genomic Maps: Human, mouse, rat, chicken, drosophila melanogaster, zebrafish, mosquito, caenorhabditis elegans, Arabidopsis thaliana, rice, yeast, fission yeast Dana-Farber Cancer Institute (DFCI) Gene Indices Software Tools: *TGI Clustering tools (TGICL): a software system for fast clustering of large EST datasets. *GICL: this package contains the scripts and all the necessary pre-compiled binaries for 32bit Linux systems. *clview: an assembly file viewer. *SeqClean:a script for automated trimming and validation of ESTs or other DNA sequences by screening for various contaminants, low quality and low-complexity sequences. *cdbfasta/cdbyank: fast indexing/retrieval of fasta records from flat file databases. *DAS/XML Genomic Viewer The Genomic viewer borrows modules from http://www.biodas.org (lstein (at) cshl.org) & http://webreference.com.

Proper citation: Gene Index Project (RRID:SCR_002148) Copy   

•   Source: SciCrunch Registry

http://www.aspgd.org/

Database of genetic and molecular biological information about the filamentous fungi of the genus Aspergillus including information about genes and proteins of Aspergillus nidulans and Aspergillus fumigatus; descriptions and classifications of their biological roles, molecular functions, and subcellular localizations; gene, protein, and chromosome sequence information; tools for analysis and comparison of sequences; and links to literature information; as well as a multispecies comparative genomics browser tool (Sybil) for exploration of orthology and synteny across multiple sequenced Sgenus species. Also available are Gene Ontology (GO) and community resources. Based on the Candida Genome Database, the Aspergillus Genome Database is a resource for genomic sequence data and gene and protein information for Aspergilli. Among its many species, the genus contains an excellent model organism (A. nidulans, or its teleomorph Emericella nidulans), an important pathogen of the immunocompromised (A. fumigatus), an agriculturally important toxin producer (A. flavus), and two species used in industrial processes (A. niger and A. oryzae). Search options allow you to: *Search AspGD database using keywords. *Find chromosomal features that match specific properties or annotations. *Find AspGD web pages using keywords located on the page. *Find information on one gene from many databases. *Search for keywords related to a phenotype (e.g., conidiation), an allele (such as veA1), or an experimental condition (e.g., light). Analysis and Tools allow you to: *Find similarities between a sequence of interest and Aspergillus DNA or protein sequences. *Display and analyze an Aspergillus sequence (or other sequence) in many ways. *Navigate the chromosomes set. View nucleotide and protein sequence. *Find short DNA/protein sequence matches in Aspergillus. *Design sequencing and PCR primers for Aspergillus or other input sequences. *Display the restriction map for a Aspergillus or other input sequence. *Find similarities between a sequence of interest and fungal nucleotide or protein sequences. AspGD welcomes data submissions.

Proper citation: ASPGD (RRID:SCR_002047) Copy   

•   Source: SciCrunch Registry

http://mips.gsf.de/genre/proj/yeast/index.jsp

The MIPS Comprehensive Yeast Genome Database (CYGD) aims to present information on the molecular structure and functional network of the entirely sequenced, well-studied model eukaryote, the budding yeast Saccharomyces cerevisiae. In addition, the data of various projects on related yeasts are used for comparative analysis.

Proper citation: CYGD - Comprehensive Yeast Genome Database (RRID:SCR_002289) Copy   

•   Source: SciCrunch Registry

http://microbes.ucsc.edu/cgi-bin/hgGateway?db=neisMeni_MC58_1

Portal contains detailed information for Neisseria meningitidis MC58. Information include DNA molecule summary, primary annotation summary, and taxonomy. It is a tool that allows the researcher to access all of the bacterial genome sequences completed to date. Users may access information on all of the bacterial genomes or any subset of them. Information in the website about its DNA molecule includes: total number of DNA molecules, total size of all DNA molecules, number of primary annotation coding bases, and number of G + C bases. Its primary annotation summary include: total genes, protein coding genes, tRNA genes, and rRNA genes. Sponsors: The CMR was previously funded by two grants, one from the U.S. Department of Energy (DOE) and one from the National Science Foundation (NSF). It is currently partially funded by a Microbial Sequence Center (MSC) grant from the National Institute of Allergy and Infectious Diseases (NIAID)

Proper citation: Neisseria meningitidis MC58 Genome Page (RRID:SCR_002200) Copy   

•   Source: SciCrunch Registry

http://insulatordb.uthsc.edu/

A comprehensive collection of experimentally determined and computationally predicted CCCTC-binding factor (CTCF) binding sites (CTCFBS) from the literature. The database is designed to facilitate the studies on insulators and their roles in demarcating functional genomic domains. The CTCFBS Prediction Tool allows users to scan sequences for the single best match to CTCF position weight matrices. Currently (March 2014), the database contains almost 15 million experimentally determined CTCF binding sites across several species. CTCF binding sites were collected from published papers containing CTCF binding sites identified using ChIPSeq or similar methods, data from the ENCODE project, and a set of approximately 100 manually curated binding sites identified by low-throughput experiments. Users can browse insulator sequence features, function annotations, genomic contexts including histone methylation profiles, flanking gene expression patterns and orthologous regions in other mammalian genomes. Users can also retrieve data by text search, sequence search and genomic range search.

Proper citation: CTCFBSDB (RRID:SCR_002279) Copy   

•   Source: SciCrunch Registry

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

Database of high-throughput genome sequences from large-scale genome sequencing centers, including unfinished and finished sequences. It was created to accommodate a growing need to make unfinished genomic sequence data rapidly available to the scientific community in a coordinated effort among the International Nucleotide Sequence databases, DDBJ, EMBL, and GenBank. Sequences are prepared for submission by using NCBI's software tools Sequin or tbl2asn. Each center has an FTP directory into which new or updated sequence files are placed. Sequence data in this division are available for BLAST homology searches against either the htgs database or the month database, which includes all new submissions for the prior month. Unfinished HTG sequences containing contigs greater than 2 kb are assigned an accession number and deposited in the HTG division. A typical HTG record might consist of all the first-pass sequence data generated from a single cosmid, BAC, YAC, or P1 clone, which together make up more than 2 kb and contain one or more gaps. A single accession number is assigned to this collection of sequences, and each record includes a clear indication of the status (phase 1 or 2) plus a prominent warning that the sequence data are unfinished and may contain errors. The accession number does not change as sequence records are updated; only the most recent version of a HTG record remains in GenBank.

Proper citation: High Throughput Genomic Sequences Division (RRID:SCR_002150) Copy   

•   Source: SciCrunch Registry

http://www.ark-genomics.org/

Portal for studies of genome structure and genetic variation, gene expression and gene function. Provides services including DNA sequencing of model and non-model genomes using both Next Generation and Sanger sequencing , Gene expression analysis using both microarrays and Next Generation Sequencing, High throughput genotyping of SNP and copy number variants, Data collection and analysis supported in-house high performance computing facilities and expertise, Extensive EST clone collections for a number of animal species, all of commercially available microarray tools from Affymetrix, Illumina, Agilent and Nimblegen, Parentage testing using microsatellites and smaller SNP panels. ARK-Genomics has developed network of researchers whom they support through each stage of their genomics research, from grant application, experimental design and technology selection, performing wet laboratory protocols, through to analysis of data often in conjunction with commercial partners.

Proper citation: ARK-Genomics: Centre for Functional Genomics (RRID:SCR_002214) Copy   

•   Source: SciCrunch Registry

http://www.genome.gov/27549169

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on October 30,2025. 2012 workshop to establish a Central Resource of Data from Genome Sequencing Projects. The workshop addressed the challenges to aggregating and analyzing data sets from genome sequencing studies, such as: * Data sets being generally hard to access. * Data residing in various databases. * Variant and exposure/phenotype data not being comparable across studies. Participants in the workshop discussed options for dealing with these challenges, along with their costs and tradeoffs. Videos and accompanying slides from the workshop are available. Also available as a video playlist on GenomeTV

Proper citation: NHGRI: Establishing a Central Resource of Data from Genome Sequencing Projects (RRID:SCR_003205) Copy   

•   Source: SciCrunch Registry

http://www.genedb.org/Homepage/Tbruceibrucei927

Database of the most recent sequence updates and annotations for the T. brucei genome. New annotations are constantly being added to keep up with published manuscripts and feedback from the Trypanosomatid research community. You may search by Protein Length, Molecular Mass, Gene Type, Date, Location, Protein Targeting, Transmembrane Helices, Product, GO, EC, Pfam ID, Curation and Comments, and Dbxrefs. BLAST and other tools are available. T. brucei possesses a two-unit genome, a nuclear genome and a mitochondrial (kinetoplast) genome with a total estimated size of 35Mb/haploid genome. The nuclear genome is split into three classes of chromosomes according to their size on pulsed-field gel electrophoresis, 11 pairs of megabase chromosomes (0.9-5.7 Mb), intermediate (300-900 kb) and minichromosomes (50-100 kb). The T. brucei genome contains a ~0.5Mb segmental duplication affecting chromosomes 4 and 8, which is responsible for some 75 gene duplicates unique to this species. A comparative chromosome map of the duplicons can be accessed here (PubmedID 18036214). Protozoan parasites within the species Trypanosoma brucei are the etiological agent of human sleeping sickness and Nagana in animals. Infections are limited to patches of sub-Saharan Africa where insects vectors of the Glossina genus are endemic. The most recent estimates indicate between 50,000 - 70,000 human cases currently exist, with 17 000 new cases each year (WHO Factsheet, 2006). In collaboration with GeneDB, the EuPathDB genomic sequence data and annotations are regularly deposited on TriTrypDB where they can be integrated with other datasets and queried using customized queries.

Proper citation: GeneDB Tbrucei (RRID:SCR_004786) Copy   

•   Source: SciCrunch Registry

http://gds.nih.gov/

NIH established expectations for sharing data obtained through NIH-funded genome-wide association studies (GWAS) with the implementation of the GWAS Policy. Information and resources related to the GWAS Policy can be found on this website.

Proper citation: Genomic Datasharing (RRID:SCR_005233) Copy   

•   Source: SciCrunch Registry

http://seqant.genetics.emory.edu/

A free web service and open source software package that performs rapid, automated annotation of DNA sequence variants (single base mutations, insertions, deletions) discovered with any sequencing platform. Variant sites are characterized with respect to their functional type (Silent, Replacement, 5' UTR, 3' UTR, Intronic, Intergenic), whether they have been previously submitted to dbSNP, and their evolutionary conservation. Annotated variants can be viewed directly on the web browser, downloaded in a tab delimited text file, or directly uploaded in a Browser Extended Data (BED) format to the UCSC genome browser. SeqAnt further identifies all loci harboring two or more coding sequence variants that help investigators identify potential compound heterozygous loci within exome sequencing experiments. In total, SeqAnt resolves a significant bottleneck by allowing an investigator to rapidly prioritize the functional analysis of those variants of interest.

Proper citation: SeqAnt (RRID:SCR_005186) Copy   

•   Source: SciCrunch Registry

http://compbio.cs.brown.edu/projects/gasv/

Software tool combining both paired read and read depth signals into probabilistic model which can analyze multiple alignments of reads. Used to find structural variation in both normal and cancer genomes using data from variety of next-generation sequencing platforms. Used to predict structural variants directly from aligned reads in SAM/BAM format.Combines read depth information along with discordant paired read mappings into single probabilistic model two common signals of structural variation. When multiple alignments of read are given, GASVPro utilizes Markov Chain Monte Carlo procedure to sample over the space of possible alignments.

Proper citation: GASVPro (RRID:SCR_005259) Copy   

•   Source: SciCrunch Registry