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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   

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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   

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http://htslib.org/

Original SAMTOOLS package has been split into three separate repositories including Samtools, BCFtools and HTSlib. Samtools for manipulating next generation sequencing data used for reading, writing, editing, indexing,viewing nucleotide alignments in SAM,BAM,CRAM format. BCFtools used for reading, writing BCF2,VCF, gVCF files and calling, filtering, summarising SNP and short indel sequence variants. HTSlib used for reading, writing high throughput sequencing data.

Proper citation: SAMTOOLS (RRID:SCR_002105) Copy   

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http://sourceforge.net/projects/gmato/files/?source=navbar

A software tool used for simple sequence repeats (SSR) or microsatellite characterization. It also facilitates SSR marker design on a genomic scale, microsatellite mining at any length, and comprehensive statistical analysis for DNA sequences in any genome at any size. Analysis parameters are customizable.

Proper citation: GMATo (RRID:SCR_000165) Copy   

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http://genboree.org

A software application and database viewing system for genomic research, more specifically formulti-genome comparison and pattern discovery via genome self-comparison. Data are available for a range of species including Human Chr3, Human Chr12, Sea Urchin, Tribolium, and cow. The Genboree Discovery System is the largest software system developed at the bioinformatics laboratory at Baylor in close collaboration with the Human Genome Sequencing Center. Genboree is a turnkey software system for genomic research. Genboree is hosted on the Internet and, as of early 2007, the number of registered users exceeds 600. While it can be configured to support almost any genome-centric discovery process, a number of configurations already exist for specific applications. Current focus is on enabling studies of genome variation, including array CGH studies, PCR-based resequencing, genome resequencing using comparative sequence assembly, genome remapping using paired-end tags and sequences, genome analysis and annotation, multi-genome comparison and pattern discovery via genome self-comparison. Genboree database and visualization settings, tools, and user roles are configurable to fit the needs of specific discovery processes. Private permanent project-specific databases can be accessed in a controlled way by collaborators via the Internet. Project-specific data is integrated with relevant data from public sources such as genome browsers and genomic databases. Data processing tools are integrated using a plug-in model. Genboree is extensible via flexible data-exchange formats to accommodate project specific tools and processing steps. Our Positional Hashing method, implemented in the Pash program, enables extremely fast and accurate sequence comparison and pattern discovery by employing low-level parallelism. Pash enables fast and sensitive detection of orthologous regions across mammalian genomes, and fast anchoring of hundreds of millions of short sequences produced by next-generation sequencing technologies. We are further developing the Pash program and employing it in the context of various discovery pipelines. Our laboratory participates in the pilot stage of the TCGA (The Cancer Genome Atlas) project. We aim to develop comprehensive, rapid, and economical methods for detecting recurrent chromosomal aberrations in cancer using next-generation sequencing technologies. The methods will allow detection of recurrent chromosomal aberrations in hundreds of small (

Proper citation: Genboree Discovery System (RRID:SCR_000747) Copy   

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http://franklin.imgen.bcm.tmc.edu/

The mission of the Baylor College of Medicine - Shaw Laboratory is to apply methods of statistics and bioinformatics to the analysis of large scale genomic data. Our vision is data integration to reveal the underlying connections between genes and processes in order to cure disease and improve healthcare.

Proper citation: Baylor College of Medicine - Shaw Laboratory (RRID:SCR_000604) Copy   

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https://www.med.unc.edu/pgc/

Consortium conducting meta-analyses of genome-wide genetic data for psychiatric disease. Focused on autism, attention-deficit hyperactivity disorder, bipolar disorder, major depressive disorder, schizophrenia, anorexia nervosa (AN), Tourette syndrome (TS), and obsessive-compulsive disorder (OCD). Used to investigate common single nucleotide polymorphisms (SNPs) genotyped on commercial arrays, structural variation (copy number variation) and uncommon or rare genetic variation. To participate you are asked to upload data from your study to central computer used by this consortium. Genetic Cluster Computer serves as data warehouse and analytical platform for this study . When data from your study have been incorporated, account will be provided on central server and access to all GWAS genotypes, phenotypes, and meta-analytic results relevant to deposited data and participation aims. NHGRI GWAS Catalog contains updated information about all GWAS in biomedicine, and is usually excellent starting point to find comprehensive list of studies. Files can be obtained by any PGC member for any disease to which they contributed data. These files can also be obtained by application to NIMH Genetics Repository. Individual-level genotype and phenotype data requires application, material transfer agreement, and informed consent consideration. Some datasets are also in controlled-access dbGaP and Wellcome Trust Case-Control Consortium repositories. PGC members can also receive back cleaned and imputed data and results for samples they contributed to PGC analyses.

Proper citation: Psychiatric Genomics Consortium (RRID:SCR_004495) Copy   

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

Web service for permanent archiving and sharing of all types of personally identifiable genetic and phenotypic data resulting from biomedical research projects. The repository allows you to explore datasets from numerous genotype experiments, supplied by a range of data providers. The EGA''s role is to provide secure access to the data that otherwise could not be distributed to the research community. The EGA contains exclusive data collected from individuals whose consent agreements authorize data release only for specific research use or to bona fide researchers. Strict protocols govern how information is managed, stored and distributed by the EGA project. As an example, only members of the EGA team are allowed to process data in a secure computing facility. Once processed, all data are encrypted for dissemination and the encryption keys are delivered offline. The EGA also supports data access only for the consortium members prior to publication.

Proper citation: European Genome phenome Archive (RRID:SCR_004944) Copy   

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http://glioblastoma.alleninstitute.org/

Platform for exploring the anatomic and genetic basis of glioblastoma at the cellular and molecular levels that includes two interactive databases linked together by de-identified tumor specimen numbers to facilitate comparisons across data modalities: * The open public image database, here, providing in situ hybridization data mapping gene expression across the anatomic structures inherent in glioblastoma, as well as associated histological data suitable for neuropathological examination * A companion database (Ivy GAP Clinical and Genomic Database) offering detailed clinical, genomic, and expression array data sets that are designed to elucidate the pathways involved in glioblastoma development and progression. This database requires registration for access. The hope is that researchers all over the world will mine these data and identify trends, correlations, and interesting leads for further studies with significant translational and clinical outcomes. The Ivy Glioblastoma Atlas Project is a collaborative partnership between the Ben and Catherine Ivy Foundation, the Allen Institute for Brain Science and the Ben and Catherine Ivy Center for Advanced Brain Tumor Treatment.

Proper citation: Ivy Glioblastoma Atlas Project (RRID:SCR_005044) Copy   

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http://bejerano.stanford.edu/prism/public/html/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 5,2022.Tool that predicts interactions between transcription factors and their regulated genes from binding motifs. Understanding vertebrate development requires unraveling the cis-regulatory architecture of gene regulation. PRISM provides accurate genome-wide computational predictions of transcription factor binding sites for the human and mouse genomes, and integrates the predictions with GREAT to provide functional biological context. Together, accurate computational binding site prediction and GREAT produce for each transcription factor: 1. putative binding sites, 2. putative target genes, 3. putative biological roles of the transcription factor, and 4. putative cis-regulatory elements through which the factor regulates each target in each functional role., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: PRISM (Stanford database) (RRID:SCR_005375) Copy   

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http://202.97.205.78/CpG_MPs/

Tool for identification and analysis of CpG methylation patterns of genomic regions from high-throughput bisulfite sequencing data. It may identify the unmethylated and methylated regions for a single sample, the conserved and differential methylation regions with different methylation patterns for paired or multiple samples. It includes four main modules as follows: # Normalization of the sequencing reads of cytosines following guanines; # Identification of the unmethylated (methylated) regions using hotspot extension algorithm; # Identification of conservatively and differentially methylated regionsby combining the combinatorial algorithm for determination of potentially functional regions with the algorithm of analysis of variance (ANOVA) for assess the statistical significance of differentially methylated regions; # Extraction of sequence features and visualization of these potentially functional regions.

Proper citation: CpG MPs (RRID:SCR_005441) Copy   

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http://www.garban.org/garban/home.php

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 12, 2012. GARBAN is a tool for analysis and rapid functional annotation of data arising from cDNA microarrays and proteomics techniques. GARBAN has been implemented with bioinformatic tools to rapidly compare, classify, and graphically represent multiple sets of data (genes/ESTs, or proteins), with the specific aim of facilitating the identification of molecular markers in pathological and pharmacological studies. GARBAN has links to the major genomic and proteomic databases (Ensembl, GeneBank, UniProt Knowledgebase, InterPro, etc.), and follows the criteria of the Gene Ontology Consortium (GO) for ontological classifications. Source may be shared: e-mail garban (at) ceit.es. Platform: Online tool

Proper citation: GARBAN (RRID:SCR_005778) Copy   

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http://corneliu.henegar.info/FunCluster.htm

FunCluster is a genomic data analysis algorithm which performs functional analysis of gene expression data obtained from cDNA microarray experiments. Besides automated functional annotation of gene expression data, FunCluster functional analysis aims to detect co-regulated biological processes through a specially designed clustering procedure involving biological annotations and gene expression data. FunCluster''''s functional analysis relies on Gene Ontology and KEGG annotations and is currently available for three organisms: Homo Sapiens, Mus Musculus and Saccharomyces Cerevisiae. FunCluster is provided as a standalone R package, which can be run on any operating system for which an R environment implementation is available (Windows, Mac OS, various flavors of Linux and Unix). Download it from the FunCluster website, or from the worldwide mirrors of CRAN. FunCluster is provided freely under the GNU General Public License 2.0. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: FunCluster (RRID:SCR_005774) Copy   

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http://great.stanford.edu/public/html/splash.php

Data analysis service that predicts functions of cis-regulatory regions identified by localized measurements of DNA binding events across an entire genome. Whereas previous methods took into account only binding proximal to genes, GREAT is able to properly incorporate distal binding sites and control for false positives using a binomial test over the input genomic regions. GREAT incorporates annotations from 20 ontologies and is available as a web application. The utility of GREAT extends to data generated for transcription-associated factors, open chromatin, localized epigenomic markers and similar functional data sets, and comparative genomics sets. Platform: Online tool

Proper citation: GREAT: Genomic Regions Enrichment of Annotations Tool (RRID:SCR_005807) 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://www.nervenet.org/main/dictionary.html

A mouse-related portal of genomic databases and tables of mouse brain data. Most files are intended for you to download and use on your own personal computer. Most files are available in generic text format or as FileMaker Pro databases. The server provides data extracted and compiled from: The 2000-2001 Mouse Chromosome Committee Reports, Release 15 of the MIT microsatellite map (Oct 1997), The recombinant inbred strain database of R.W. Elliott (1997) and R. W. Williams (2001), and the Map Manager and text format chromosome maps (Apr 2001). * LXS genotype (Excel file): Updated, revised positions for 330 markers genotyped using a panel of 77 LXS strain. * MIT SNP DATABASE ONLINE: Search and sort the MIT Single Nucleotide Polymorphism (SNP) database ONLINE. These data from the MIT-Whitehead SNP release of December 1999. * INTEGRATED MIT-ROCHE SNP DATABASE in EXCEL and TEXT FORMATS (1-3 MB): Original MIT SNPs merged with the new Roche SNPs. The Excel file has been formatted to illustrate SNP haplotypes and genetic contrasts. Both files are intended for statistical analyses of SNPs and can be used to test a method outlined in a paper by Andrew Grupe, Gary Peltz, and colleagues (Science 291: 1915-1918, 2001). The Excel file includes many useful equations and formatting that will help in navigating through this large database and in testing the in silico mapping method. * Use of inbred strains for the study of individual differences in pain related phenotypes in the mouse: Elissa J. Chesler''s 2002 dissertation, discussing issues relevant to the integration of genomic and phenomic data from standard inbred strains including genetic interactions with laboratory environmental conditions and the use of various in silico inbred strain haplotype based mapping algorithms for QTL analysis. * SNP QTL MAPPER in EXCEL format (572 KB, updated January 2002 by Elissa Chesler): This Excel workbook implements the Grupe et al. mapping method and outputs correlation plots. The main spreadsheet allows you to enter your own strain data and compares them to haplotypes. Be very cautious and skeptical when using this spreadsheet and the technique. Read all of the caveates. This excel version of the method was developed by Elissa Chesler. This updated version (Jan 2002) handles missing data. * MIT SNP Database (tab-delimited text format): This file is suitable for manipulation in statistics and spreadsheet programs (752 KB, Updated June 27, 2001). Data have been formatted in a way that allows rapid acquisition of the new data from the Roche Bioscience SNP database. * MIT SNP Database (FileMaker 5 Version): This is a reformatted version of the MIT Single Nucleotide Polymorphism (SNP) database in FileMaker 5 format. You will need a copy of this application to open the file (Mac and Windows; 992 KB. Updated July 13, 2001 by RW). * Gene Mapping and Map Manager Data Sets: Genetic maps of mouse chromosomes. Now includes a 10th generation advanced intercross consisting of 500 animals genetoyped at 340 markers. Lots of older files on recombinant inbred strains. * The Portable Dictionary of the Mouse Genome, 21,039 loci, 17,912,832 bytes. Includes all 1997-98 Chromosome Committee Reports and MIT Release 15. * FullDict.FMP.sit: The Portable Dictionary of the Mouse Genome. This large FileMaker Pro 3.0/4.0 database has been compressed with StuffIt. The Dictionary of the Mouse Genome contains data from the 1997-98 chromosome committee reports and MIT Whitehead SSLP databases (Release 15). The Dictionary contains information for 21,039 loci. File size = 4846 KB. Updated March 19, 1998. * MIT Microsatellite Database ONLINE: A database of MIT microsatellite loci in the mouse. Use this FileMaker Pro database with OurPrimersDB. MITDB is a subset of the Portable Dictionary of the Mouse Genome. ONLINE. Updated July 12, 2001. * MIT Microsatellite Database: A database of MIT microsatellite loci in the mouse. Use this FileMaker Pro database with OurPrimersDB. MITDB is a subset of the Portable Dictionary of the Mouse Genome. File size = 3.0 MB. Updated March 19, 1998. * OurPrimersDB: A small database of primers. Download this database if you are using numerous MIT primers to map genes in mice. This database should be used in combination with the MITDB as one part of a relational database. File size = 149 KB. Updated March 19, 1998. * Empty copy (clone) of the Portable Dictionary in FileMaker Pro 3.0 format. Download this file and import individual chromosome text files from the table into the database. File size = 231 KB. Updated March 19, 1998. * Chromosome Text Files from the Dictionary: The table lists data on gene loci for individual chromosomes.

Proper citation: Mouse Genome Databases (RRID:SCR_007147) Copy   

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http://www.genoscope.cns.fr/externe/tetraodon/

The initial objective of Genoscope was to compare the genomic sequences of this fish to that of humans to help in the annotation of human genes and to estimate their number. This strategy is based on the common genetic heritage of the vertebrates: from one species of vertebrate to another, even for those as far apart as a fish and a mammal, the same genes are present for the most part. In the case of the compact genome of Tetraodon, this common complement of genes is contained in a genome eight times smaller than that of humans. Although the length of the exons is similar in these two species, the size of the introns and the intergenic sequences is greatly reduced in this fish. Furthermore, these regions, in contrast to the exons, have diverged completely since the separation of the lineages leading to humans and Tetraodon. The Exofish method, developed at Genoscope, exploits this contrast such that the conserved regions which can be identified by comparing genomic sequences of the two species, correspond only to coding regions. Using preliminary sequencing results of the genome of Tetraodon in the year 2000, Genoscope evaluated the number of human genes at about 30,000, whereas much higher estimations were current. The progress of the annotation of the human genome has since supported the Genoscope hypothesis, with values as low as 22,000 genes and a consensus of around 25,000 genes. The sequencing of the Tetraodon genome at a depth of about 8X, carried out as a collaboration between Genoscope and the Whitehead Institute Center for Genome Research (now the Broad Institute), was finished in 2002, with the production of an assembly covering 90 of the euchromatic region of the genome of the fish. This has permitted the application of Exofish at a larger scale in comparisons with the genome of humans, but also with those of the two other vertebrates sequenced at the time (Takifugu, a fish closely related to Tetraodon, and the mouse). The conserved regions detected in this way have been integrated into the annotation procedure, along with other resources (cDNA sequences from Tetraodon and ab initio predictions). Of the 28,000 genes annotated, some families were examined in detail: selenoproteins, and Type 1 cytokines and their receptors. The comparison of the proteome of Tetraodon with those of mammals has revealed some interesting differences, such as a major diversification of some hormone systems and of the collagen molecules in the fish. A search for transposable elements in the genomic sequences of Tetraodon has also revealed a high diversity (75 types), which contrasts with their scarcity; the small size of the Tetraodon genome is due to the low abundance of these elements, of which some appear to still be active. Another factor in the compactness of the Tetraodon genome, which has been confirmed by annotation, is the reduction in intron size, which approaches a lower limit of 50-60 bp, and which preferentially affects certain genes. The availability of the sequences from the genomes of humans and mice on one hand, and Takifugu and Tetraodon on the other, provide new opportunities for the study of vertebrate evolution. We have shown that the level of neutral evolution is higher in fish than in mammals. The protein sequences of fish also diverge more quickly than those of mammals. A key mechanism in evolution is gene duplication, which we have studied by taking advantage of the anchoring of the majority of the sequences from the assembly on the chromosomes. The result of this study speaks strongly in favor of a whole genome duplication event, very early in the line of ray-finned fish (Actinopterygians). An even stronger evidence came from synteny studies between the genomes of humans and Tetraodon. Using a high-resolution synteny map, we have reconstituted the genome of the vertebrate which predates this duplication - that is, the last common ancestor to all bony vertebrates (most of the vertebrates apart from cartilaginous fish and agnaths like lamprey). This ancestral karyotype contains 12 chromosomes, and the 21 Tetraodon chromosomes derive from it by the whole genome duplication and a surprisingly small number of interchromosomal rearrangements. On the contrary, exchanges between chromosomes have been much more frequent in the lineage that leads to humans. Sponsors: The project was supported by the Consortium National de Recherche en Genomique and the National Human Genome Research Institute.

Proper citation: Tetraodon Genome Browser (RRID:SCR_007079) Copy   

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http://www.aneurist.org/

Project focused on cerebral aneurysms and provides integrated decision support system to assess risk of aneurysm rupture in patients and to optimize their treatments. IT infrastructure has been developeded for management and processing of vast amount of heterogeneous data acquired during diagnosis.

Proper citation: aneurIST (RRID:SCR_007427) Copy   

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

Software package for TDT with extended haplotypes in the R language. R is the public domain dialect of S. It should be possible to port this library to the commercial Splus product. The main problem would be translation of the help files. (entry from Genetic Analysis Software)

Proper citation: R/TDTHAP (RRID:SCR_007625) Copy   

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http://human.brain-map.org/static/brainexplorer

Multi modal atlas of human brain that integrates anatomic and genomic information, coupled with suite of visualization and mining tools to create open public resource for brain researchers and other scientists. Data include magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), histology and gene expression data derived from both microarray and in situ hybridization (ISH) approaches. Brain Explorer 2 is desktop software application for viewing human brain anatomy and gene expression data in 3D.

Proper citation: Allen Human Brain Atlas (RRID:SCR_007416) Copy   

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