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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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Ratings or validation data are available for this resource

http://genome.ucsc.edu/

Portal to interactively visualize genomic data. Provides reference sequences and working draft assemblies for collection of genomes and access to ENCODE and Neanderthal projects. Includes collection of vertebrate and model organism assemblies and annotations, along with suite of tools for viewing, analyzing and downloading data.

Proper citation: UCSC Genome Browser (RRID:SCR_005780) 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.broadinstitute.org/

Biomedical and genomic research center located in Cambridge, Massachusetts, United States. Nonprofit research organization under the name Broad Institute Inc., and is partners with Massachusetts Institute of Technology, Harvard University, and the five Harvard teaching hospitals. Dedicated to advance understanding of biology and treatment of human disease to improve human health.

Proper citation: Broad Institute (RRID:SCR_007073) 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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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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http://bio2rdf.blogspot.com/

This is a blog about post genomic knowledge. The website''s goal is to make public datasets from the bioinformatics community available in RDF format via standard SPARQL endpoints.

Proper citation: Bio2RDF atlas of post genomic knowledge (RRID:SCR_007991) 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://locus.jouy.inra.fr/cgi-bin/lgbc/mapping/common/intro2.pl?BASE=goat

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 16, 2013. This website contains information about the mapping of the caprine genome. It contains loci list, phenes list, cartography, gene list, and other sequence information about goats. This website contains 731 loci, 271 genes, and 1909 homologue loci on 112 species. It also allows users to summit their own data for Goatmap. ARK-Genomics is not-for-profit and has collaborators from all over the world with an interest in farm animal genomics and genetics. ARK-Genomics was initially set up in 2000 with a grant awarded from the BBSRC IGF (Investigating Gene Function) initiative and from core resources of the Roslin Institute to provide a laboratory for automated analysis of gene expression using state-of-the-art genomic facilities. Since then, ARK-Genomics has expanded considerably, building up considerable expertise and resources.

Proper citation: GoatMap Database (RRID:SCR_008144) Copy   

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http://www.sanger.ac.uk/Projects/Microbes/

This website includes a list of projects that the Sanger Institute is currently working on or completed. All projects consist of the genomic sequencing of different bacteria. Each description of the bacteria includes its classification, a description, and the types of diseases that the bacteria is likely to cause. The Sanger Institute bacterial sequencing effort is concentrated on pathogens and model organisms. Data is accessible in a number of ways; for each organism there is a BLAST server, allowing users to search the sequences with their own query and retrieve the matching contigs. Sequences can also be downloaded directly by FTP. Data is accessible in a number of ways; for each organism there is a BLAST server, allowing you to search the sequences with your own query and retrieve the matching contigs. Sequences can also be downloaded directly by FTP. The primary sequence viewer and annotation tool, Artemis is available for download. This is a portable Java program which is used extensively within the Microbial Genomes group for the analysis and annotation of sequence data from cosmids to whole genomes. The Artemis Comparison Tool (ACT) is also useful for interactive viewing of the comparisons between large and small sequences.

Proper citation: Bacterial Genomes (RRID:SCR_008141) Copy   

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http://www.animalgenome.org/pigs/nagrp.html

Database and resources on the pig genome.

Proper citation: U.S. Pig Genome Project (RRID:SCR_008151) Copy   

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http://genewindow.nci.nih.gov/

Software tool for pre- and post-genetic bioinformatics and analytical work, developed and used at the Core Genotyping Facility (CGF) at the National Cancer Institute. While Genewindow is implemented for the human genome and integrated with the CGF laboratory data, it stands as a useful tool to assist investigators in the selection of variants for study in vitro, or in novel genetic association studies. The Genewindow application and source code is publicly available for use in other genomes, and can be integrated with the analysis, storage, and archiving of data generated in any laboratory setting. This can assist laboratories in the choice and tracking of information related to genetic annotations, including variations and genomic positions. Features of GeneWindow include: -Intuitive representation of genomic variation using advanced web-based graphics (SVG) -Search by HUGO gene symbol, dbSNP ID, internal CGF polymorphism ID, or chromosome coordinates -Gene-centric display (only when a gene of interest is in view) oriented 5 to 3 regardless of the reference strand and adjacent genes -Two views, a Locus Overview, which varies in size depending on the gene or genomic region being viewed and, below it, a Sequence View displaying 2000 base pairs within the overview -Navigate the genome by clicking along the gene in the Locus Overview to change the Sequence View, expand or contract the genomic interval, or shift the view in the 5 or 3 direction (relative to the current gene) -Lists of available genomic features -Search for sequence matches in the Locus Overview -Genomic features are represented by shape, color and opacity with contextual information visible when the user moves over or clicks on a feature -Administrators can insert newly-discovered polymorphisms into the Genewindow database by entering annotations directly through the GUI -Integration with a Laboratory Information Management System (LIMS) or other databases is possible

Proper citation: GeneWindow (RRID:SCR_008183) Copy   

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http://athina.biol.uoa.gr/bioinformatics/GENEVITO/

A JAVA-based computer application that serves as a workbench for genome-wide analysis through visual interaction. GeneViTo offers an inspectional view of genomic functional elements, concerning data stemming both from database annotation and analysis tools for an overall analysis of existing genomes. The application deals with various experimental information concerning both DNA and protein sequences (derived from public sequence databases or proprietary data sources) and meta-data obtained by various prediction algorithms, classification schemes or user-defined features. Interaction with a Graphical User Interface (GUI) allows easy extraction of genomic and proteomic data referring to the sequence itself, sequence features, or general structural and functional features. Emphasis is laid on the potential comparison between annotation and prediction data in order to offer a supplement to the provided information, especially in cases of poor annotation, or an evaluation of available predictions. Moreover, desired information can be output in high quality JPEG image files for further elaboration and scientific use. GeneViTo has already been applied to visualize the genomes of two microbial organisms: the bacterion Chlamydia trachomatis and the archaeon Methanococcus jannaschii. The application is compatible with Linux or Windows ME-2000-XP operating systems, provided that the appropriate Java Runtime Environment (Java 1.4.1) is already installed in the system.

Proper citation: GeneVito (RRID:SCR_006211) Copy   

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

The Office of Public Health Genomics (OPHG) aims to integrate genomics into public health research, policy, and programs. Doing so could improve interventions designed to prevent and control the country''s leading chronic, infectious, environmental, and occupational diseases. OPHG''s efforts focus on conducting population-based genomic research, assessing the role of family health history in disease risk and prevention, supporting a systematic process for evaluating genetic tests, translating genomics into public health research and programs, and strengthening capacity for public health genomics in disease prevention programs. Goals: To improve public health interventions of diseases of major public health importance, including chronic, infectious, environmental, and occupational diseases, through six major initiatives: * Evaluation of Genomic Applications in Practice and Prevention (EGAPP), * Human Genome Epidemiology Network (HuGENet), * NHANES Collaborative Genomics Project, * Family History Public Health Initiative, * Genomics Translation Research and Programs, and, * Genomic Applications in Practice and Prevention Network (GAPPNet).

Proper citation: Public Health Genomics (RRID:SCR_006462) Copy   

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

A portal to biomedical and genomic information. NCBI creates public databases, conducts research in computational biology, develops software tools for analyzing genome data, and disseminates biomedical information for the better understanding of molecular processes affecting human health and disease.

Proper citation: NCBI (RRID:SCR_006472) Copy   

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http://bioconductor.org/packages/bioc/html/GeneAnswers.html

GeneAnswers provide an integrated tool for given genes biological or medical interpretation. It includes statistical test of given genes and specified categories. Microarray techniques have been widely employed in genomic scale studies for more than one decade. The standard analysis of microarray data is to filter out a group of genes from thousands of probes by certain statistical criteria. These genes are usually called significantly differentially expressed genes. Recently, next generation sequencing (NGS) is gradually adopted to explore gene transcription, methylation, etc. Also a gene list can be obtained by NGS preliminary data analysis. However, this type of information is not enough to understand the potential linkage between identified genes and interested functions. The integrated functional and pathway analysis with gene expression data would be very helpful for researchers to interpret the relationship between the identified genes and proposed biological or medical functions and pathways. The GeneAnswers package provides an integrated solution for a group of genes and specified categories (biological or medical functions, such as Gene Ontology, Disease Ontology, KEGG, etc) to reveal the potential relationship between them by means of statistical methods, and make user-friendly network visualization to interpret the results. Besides the package has a function to combine gene expression profile and category analysis together by outputting concept-gene cross tables, keywords query on NCBI Entrez Gene and application of human based Disease ontology analysis of given genes from other species can help people to understand or discover potential connection between genes and functions. Sponsors: This project was supported in part by Award Number UL1RR025741 from the National Center for Research Resources.

Proper citation: GeneAnswers (RRID:SCR_006498) Copy   

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http://gmd.mpimp-golm.mpg.de/

It facilitates the search for and dissemination of mass spectra from biologically active metabolites quantified using Gas chromatography (GC) coupled to mass spectrometry (MS). Use the Search Page to search for a compound of your interest, using the name, mass, formula, InChI etc. as query input. Additionally, a Library Search service enables the search of user submitted mass spectra within the GMD. In parallel to the library search, a prediction of chemical sub-groups is performed. This approach has reached beta level and a publication is currently under review. Using several sub-group specific Decision Trees (DTs), mass spectra are classified with respect to the presence of the chemical moieties within the linked (unknown) compound. Prediction of functional groups (ms analysis) facilitates the search of metabolites within the GMD by means of user submitted GC-MS spectra consisting of retention index (n-alkanes, if vailable) and mass intensities ratios. In addition, a functional group prediction will help to characterize those metabolites without available reference mass spectra included in the GMD so far. Instead, the unknown metabolite is characterized by predicted presence or absence of functional groups. For power users this functionality presented here is exposed as soap based web services. Functional group prediction of compounds by means of GC-EI-MS spectra using Microsoft analysis service decision trees All currently available trained decision trees and sub-structure predictions provided by the GMD interface. Table describes the functional group, optional use of an RI system, record date of the trained decision tree, number of MSTs with proportion of MSTs linked to metabolites with the functional group present for each tree. Average and standard deviation of the 50-fold CV error, namely the ratio false over correctly sorted MSTs in the trained DT, are listed. The GMD website offers a range of mass spectral reference libraries to academic users which can be downloaded free of charge in various electronic formats. The libraries are constituted by base peak normalized consensus spectra of single analytes and contain masses in the range 70 to 600 amu, while the ubiquitous mass fragments typically generated from compounds carrying a trimethylsilyl-moiety, namely the fragments at m/z 73, 74, 75, 147, 148, and 149, were excluded.

Proper citation: GMD (RRID:SCR_006625) Copy   

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https://www.fludb.org/brc/home.spg?decorator=influenza

The Influenza Research Database (IRD) serves as a public repository and analysis platform for flu sequence, experiment, surveillance and related data.

Proper citation: Influenza Research Database (IRD) (RRID:SCR_006641) Copy   

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