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http://sift.bii.a-star.edu.sg/

Data analysis service to predict whether an amino acid substitution affects protein function based on sequence homology and the physical properties of amino acids. SIFT can be applied to naturally occurring nonsynonymous polymorphisms and laboratory-induced missense mutations. (entry from Genetic Analysis Software) Web service is also available.

Proper citation: SIFT (RRID:SCR_012813) Copy   

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http://web.bioinformatics.ic.ac.uk/eqtlexplorer/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on June 1,2023. eQTL Explorer was developed as a computational resource to visualize and explore data from combined genome-wide expression and linkage studies is essential for the development of testable hypotheses. This visualization tool stores expression profiles, linkage data and information from external sources in a relational database and enables simultaneous visualization and intuitive interpretation of the combined data via a Java graphical interface. eQTL Explorer also provides a new and powerful tool to interrogate these very large and complex datasets. eQTLexplorer allows users to mine and understand data from a repository of genetical genomics experiments. It will graphically display eQTL information based on a certain number of selection criteria, including: tissue type, p-value, cis/trans, probeset Affymetrix id and PQTL type. Sponsors: This work was funded by the MRC Clinical Sciences Centre and the Wellcome Trust programme for Cardiovascular Functional Genomics.

Proper citation: eQTL Visualization Tool (RRID:SCR_013413) Copy   

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http://wwwn.cdc.gov/dls/genetics/rmmaterials/default.aspx

The goal of the Genetic Testing Reference Materials Coordination Program (GeT-RM) is to coordinate a self-sustaining community process to improve the availability of appropriate and characterized reference materials for: Quality control (QC), Proficiency testing (PT), Test development & validation, Research. The purpose of this program is: - To help the genetic testing community obtain appropriate and characterized reference materials - To facilitate and coordinate information exchange between users and providers of QC and reference materials - To coordinate efforts for contribution, development, characterization and distribution of reference materials for genetic testing Get-RM provides information about cell lines, DNA, and other kinds of materials that could be used as reference materials for molecular genetic testing. Some of these materials have been characterized by the GeT-RM program and can be divided into three categories: - Genetic Inherited Disease & Pharmacogenetics This section includes information about cell lines, DNA, and other samples that can be used as reference materials for various inherited diseases (including cystic fibrosis, fragile X, Huntington disease, and Ashkenazi Jewish-related diseases), pharmacogenetic loci, and biochemical genetics. The GeT-RM program has confirmed the genotype of many of the genomic DNA samples through testing in multiple clinical genetic laboratories. - Molecular Oncology This section includes information about commercially available cell lines, DNA, and other kinds of materials that could be used as reference materials for various types of cancers, including leukemia/lymphoma and solid tumors. - Infectious Disease This section includes information about commercially available cell lines, DNA, and other kinds of materials that could be used as reference materials for various infectious disease pathogens including viruses, bacteria, and protozoa.

Proper citation: Center for Disease Control and Prevention: Genetic Testing Reference Materials Coordination Program (RRID:SCR_013029) Copy   

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http://www.jurgott.org/linkage/ListSoftware.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 4th,2023. Listing of computer software for the gene mapping community on the following topics: genetic linkage analysis for human pedigree data, QTL analysis for animal/plant breeding data, genetic marker ordering, genetic association analysis, haplotype construction, pedigree drawing, and population genetics. The inclusion of a program should not be interpreted as an endorsement to that program from us. In the last few years, new technology produces new types of genetic data, and the scope of genetic analyses change dramatically. It is no longer obvious whether a program should be included or excluded from this list. Topics such as next-generation-sequencing (NGS), gene expression, genomics annotation, etc. can all be relevant to a genetic study, yet be specialized topics by themselves. Though programs on variance calling from NSG can be in, those can sequence alignment might be out; programs on eQTL can be in, those on differential expression might be out. This page was created by Dr. Wentian Li, when he was at Columbia University (1995-1996). It was later moved to Rockefeller University (1996-2002), and now takes its new home at North Shore LIJ Research Institute (2002-now). The present copy is maintained by Jurg Ott as a single file. More than 240 programs have been listed by December 2004, more than 350 programs by August 2005, close to 400 programs by December 2006, and close to 480 programs by November 2008, and over 600 programs by October 2012. A version of the searchable database was developed by Zhiliang Hu of Iowa State University, and a recent round of updating was assisted by Wei JIANG of Harbin Medical School. Some earlier software can be downloaded from EBI: ftp://ftp.ebi.ac.uk/pub/software/linkage_and_mapping/ (Linkage and Mapping Software Repository), and http://genamics.com/software/index.htm may contain archived copy of some programs.

Proper citation: Genetic Analysis Software (RRID:SCR_013155) Copy   

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https://www.stanleygenomics.org/

The Stanley Online Genomics Database uses samples from the Stanley Medical Research Institute (SMRI) Brain Bank. These samples were processed and run on gene expression arrays by a variety of researchers in collaboration with the SMRI. These researchers have performed analyses on their respective studies using a range of analytic approaches. All of the genomic data have been aggregated in this online database, and a consistent set of analyses have been applied to each study. Additionally, a comprehensive set of cross-study analyses have been performed. A thorough collection of gene expression summaries are provided, inclusive of patient demographics, disease subclasses, regulated biological pathways, and functional classifications. Raw data is also available to download. The database is derived from two sets of brain samples, the Stanley Array collection and the Stanley Consortium collection. The Stanley Array collection contains 105 patients, and the Stanley Consortium collection contains 60 patients. Multiple genomic studies have been conducted using these brain samples. From these studies, twelve were selected for inclusion in the database on the basis of number of patients studied, genomic platform used, and data quality. The Consortium collection studies have fewer patients but more diversity in brain regions and array platforms, while the Array collection studies are more homogenous. There are tradeoffs, the Consortium results will be more variable, but findings may be more broadly representative. The collections contain brain samples from subjects in four main groups: Bipolar Schizophrenia, Depression, and Controls Brain regions used in the studies include: Broadman Area 6, Broadman Area 8/9, Broadman Area 10, Broadman Area 46, Cerebellum The 12 studies encompass a range of microarray platforms: Affymetrix HG-U95Av2, Affymetrix HG-U133A, Affymetrix HG-U133 2.0+, Codelink Human 20K, Agilent Human I, Custom cDNA Publications based on any of the clinical or genomic data should credit the Stanley Medical Research Institute, as well as any individual SMRI collaborators whose data is being used. Publications which make use of analytic results/methods in the database should additionally cite Dr. Michael Elashoff. Registration is required to access the data.

Proper citation: Stanley Medical Research Institute Online Genomics Database (RRID:SCR_004859) Copy   

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http://www.broadinstitute.org/cancer/cga/oncotator

A tool for annotating human genomic point mutations and indels with data relevant to cancer researchers. Genomic Annotations, Protein Annotations, and Cancer Annotations are aggregated from many resources. A standalone version of Oncotator is being developed.

Proper citation: Oncotator (RRID:SCR_005183) Copy   

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http://www.yeastract.com

A curated repository of more than 206000 regulatory associations between transcription factors (TF) and target genes in Saccharomyces cerevisiae, based on more than 1300 bibliographic references. It also includes the description of 326 specific DNA binding sites shared among 113 characterized TFs. Further information about each Yeast gene has been extracted from the Saccharomyces Genome Database (SGD). For each gene the associated Gene Ontology (GO) terms and their hierarchy in GO was obtained from the GO consortium. Currently, YEASTRACT maintains a total of 7130 terms from GO. The nucleotide sequences of the promoter and coding regions for Yeast genes were obtained from Regulatory Sequence Analysis Tools (RSAT). All the information in YEASTRACT is updated regularly to match the latest data from SGD, GO consortium, RSA Tools and recent literature on yeast regulatory networks. YEASTRACT includes DISCOVERER, a set of tools that can be used to identify complex motifs found to be over-represented in the promoter regions of co-regulated genes. DISCOVERER is based on the MUSA algorithm. These algorithms take as input a list of genes and identify over-represented motifs, which can then be compared with transcription factor binding sites described in the YEASTRACT database.

Proper citation: Yeast Search for Transcriptional Regulators And Consensus Tracking (RRID:SCR_006076) Copy   

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http://tbdb.org

Database providing integrated access to genome sequence, expression data and literature curation for Tuberculosis (TB) that houses genome assemblies for numerous strains of Mycobacterium tuberculosis (MTB) as well assemblies for over 20 strains related to MTB and useful for comparative analysis. TBDB stores pre- and post-publication gene-expression data from M. tuberculosis and its close relatives, including over 3000 MTB microarrays, 95 RT-PCR datasets, 2700 microarrays for human and mouse TB related experiments, and 260 arrays for Streptomyces coelicolor. (July 2010) To enable wide use of these data, TBDB provides a suite of tools for searching, browsing, analyzing, and downloading the data.

Proper citation: Tuberculosis Database (RRID:SCR_006619) Copy   

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http://wavi.bioinfo.cnio.es/

A versatile web-server application for the analysis and visualization of array-CGH data.

Proper citation: waviCGH (RRID:SCR_006662) Copy   

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http://viralzone.expasy.org/

ViralZone is a SIB Swiss Institute of Bioinformatics web-resource for all viral genus and families, providing general molecular and epidemiological information, along with virion and genome figures. Each virus or family page gives an easy access to UniProtKB/Swiss-Prot viral protein entries. ViralZone project is handled by the virus program of SwissProt group. Proteins popups were developed in collaboration with Prof. Christian von Mering and Andrea Franceschini, Bioinformatics Group , Institute of Molecular Life Sciences, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland, funded in part by the SIB Swiss Institute of bioinformatics. All pictures in ViralZone are copyright of the SIB Swiss Institute of Bioinformatics.

Proper citation: ViralZone (RRID:SCR_006563) Copy   

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http://probeexplorer.cicancer.org/principal.php

Probe Explorer is an open access web-based bioinformatics application designed to show the association between microarray oligonucleotide probes and transcripts in the genomic context, but flexible enough to serve as a simplified genome and transcriptome browser. Coordinates and sequences of the genomic entities (loci, exons, transcripts), including vector graphics outputs, are provided for fifteen metazoa organisms and two yeasts. Alignment tools are used to built the associations between Affymetrix microarrays probe sequences and the transcriptomes (for human, mouse, rat and yeasts). Search by keywords is available and user searches and alignments on the genomes can also be done using any DNA or protein sequence query. Platform: Online tool

Proper citation: ProbeExplorer (RRID:SCR_007116) Copy   

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http://www.broadinstitute.org/annotation/tetraodon/

This database have been funded by the National Human Genome Research Institute (NHGRI) to produce shotgun sequence of the Tetraodon nigriviridis genome. The strategy involves Whole Genome Shotgun (WGS) sequencing, in which sequence from the entire genome is generated. Whole genome shotgun libraries were prepared from Tetraodon genomic DNA obtained from the laboratory of Jean Weissenbach at Genoscope. Additional sequence data of approximately 2.5X coverage of Tetraodon has also been generated by Genoscope in plasmid and BAC end reads. Broad and Genoscope intend to pool their data and generate whole genome assemblies. Tetraodon nigroviridis is a freshwater pufferfish of the order Tetraodontiformes and lives in the rivers and estuaries of Indonesia, Malaysia and India. This species is 20-30 million years distant from Fugu rubripes, a marine pufferfish from the same family. The gene repertoire of T. nigroviridis is very similar to that of other vertebrates. However, its relatively small genome of 385 Mb is eight times more compact than that of human, mostly because intergenic and intronic sequences are reduced in size compared to other vertebrate genomes. These genome characteristics along with the large evolutionary distance between bony fish and mammals make Tetraodon a compact vertebrate reference genome - a powerful tool for comparative genetics and for quick and reliable identification of human genes.

Proper citation: Tetraodon nigroviridis Database (RRID:SCR_007123) Copy   

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http://www.sugp.caltech.edu/SpBase/

SpBase is designed to present the results of the genome sequencing project for the purple sea urchin. The sequences and annotations emerging from this effort are organized in a database that provides the research community access to those data not normally presented through National Center for Biotechnology Information and other large databases. Additionally, the unique information on that links gene identities and sequences to the plate and well location to the library filters from the Sea Urchin genome Resource will also be presented. The software used to organize and present the sea urchin genome comes from GMOD, a collection of open source software tools for creating and managing genome-scale biological databases. That sea urchins eggs and embryos have long remained a popular research subject for cell and developmental biologists is one rationale for sequencing the genome. In addition, studies of embryonic development in the California Purple Sea Urchin, Strongylocentrotus purpuratus , have paralleled the emergence of molecular techniques ranging from the characterization of genomic repeat sequences in the 1970''s to the elucidation of gene regulatory networks in recent times. The parent of this site, SUGP, was meant to provide a focal point for the exchange of genomic information as the genome of the Purple sea urchin was being sequenced. Over these past years it has served as a repository for small sequencing projects and a source of sequence information useful for gene discovery projects. Here one could find information on macro-array libraries of cDNAs from the purple sea urchin and genomic DNA from several species. In addition, a Sequence Tag Connector (STC) collection has been assembled from 5% of the genome sequence and a very extensive repeat sequence catalog prepared. All of the sequence data that we maintained at SUGP was incorporated into the new SPBase. Of course, it is all in public sequence databases such as the National Center for Biological Information as well. Some additional sequence information is available at the Resource Center of the German Human Genome Project. With the publication of The Genome of the Sea Urchin Strongylocentrotus purpuratus by The Sea Urchin Genome Sequencing Consortium a link to the first 9941 gene annotations are now publicly available. The effort to sequence the whole purple sea urchin genome was a cooperative one that included contributions from the Sea Urchin Genome Facility here at the Center for Computational Regulatory Genomics, Beckman Institute, Caltech, and support from the Human Genome Research Institute of the National Institutes of Health. The sequencing was done at the Baylor College of Medicine, Human Genome Sequencing Center, Houston, Texas. Funding was approved based on an initiative submitted by the Sea Urchin Genome Advisory Committee.

Proper citation: SpBase - Strongylocentrotus purpuratus: the Sea Urchin Genome Database (RRID:SCR_007441) Copy   

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http://degradome.uniovi.es

A database of human, chimpanzee, mouse, and rat proteases and protease inhibitors, as well as as the growing number of hereditary diseases caused by mutations in protease genes. Analysis of the human and mouse genomes has allowed us to annotate 581 human, 580 chimpanzee, 667 mouse, and 655 rat protease genes. Proteases are classified in five different classes according to their mechanism of catalysis. Proteases are a diverse and important group of enzymes representing >2% of the human, chimpanzee, mouse and rat genomes. This group of enzymes is implicated in numerous physiological processes. The importance of proteases is illustrated by the existence of 99 different hereditary diseases due to mutations in protease genes. Furthermore, proteases have been implicated in multiple human pathologies, including vascular diseases, rheumatoid arthritis, neurodegenerative processes, and cancer. During the last ten years, our laboratory has identified and characterized more than 60 human protease genes. Due to the importance of proteolytic enzymes in human physiology and pathology, we have recently introduced the concept of Degradome, as the complete repertoire of proteases expressed by a tissue or organism. Thanks to the recent completion of the human, chimpanzee, mouse, and rat genome sequencing projects, we were able to analyze and compare for the first time the complete protease repertoire in those mammalian organisms, as well as the complement of protease inhibitor genes. This webpage also contains the Supplementary Material of Human and mouse proteases: a comparative genomic approach Nat Rev Genet (2003) 4: 544-558, Genome sequence of the brown Norway rat yields insights into mammalian evolution Nature (2004) 428: 493-521, A genomic analysis of rat proteases and protease inhibitors Genome Res. (2004) 14: 609-622, and Comparative genomic analysis of human and chimpanzee proteases Genomics (2005) 86: 638-647.

Proper citation: Mammalian Degradome Database (RRID:SCR_007624) Copy   

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http://www.ncbi.nlm.nih.gov/genomes/GenomesHome.cgi?taxid=2759&hopt=html

Curated sequence data and related information on organelles from NCBI Refseq for the community to use as a standard. The animal mitochondrial records are considered reviewed; that is, they have been manually curated by the NCBI staff. Other mitochondrial and chloroplast genome records are provisional and are presented with varying levels of review compared to the primary record used to build the RefSeq. Additionally, protein clusters for the metazoan and plastid genomes proteins can be reviewed with Entrez Protein Clusters.

Proper citation: Organelle Genome Resources (RRID:SCR_007838) Copy   

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http://statgen.ncsu.edu/asg/

Alternative splicing essentially increases the diversity of the transcriptome and has important implications for physiology, development and the genesis of diseases. This resource uses a different approach to investigate alternative splicing (instead of the conventional case-by case fashion) and integrates all transcripts derived from a gene into a single splicing graph. ASG is a database of splicing graphs for human genes, using transcript information from various major sources (Ensembl, RefSeq, STACK, TIGR and UniGene). Each transcript corresponds to a path in the graph, and alternative splicing is displayed by bifurcations. This representation preserves the relationships between different splicing variants and allows us to investigate systematically all possible putative transcripts. Web interface allows users to display the splicing graphs, to interactively assemble transcripts and to access their sequences as well as neighboring genomic regions. ASG also provide for each gene, an exhaustive pre-computed catalog of putative transcriptsin total more than 1.2 million sequences. It has found that ~65 of the investigated genes show evidence for alternative splicing, and in 5 of the cases, a single gene might produce over 100 transcripts.

Proper citation: Alternate splicing gallery (RRID:SCR_008129) Copy   

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http://www.peroxisomedb.org/

The aim of the PEROXISOME database (PeroxisomeDB) is to gather, organize and integrate curated information on peroxisomal genes, their encoded proteins, their molecular function and metabolic pathway they belong to, and their related disorders. PeroxisomeDB contains the complete peroxisomal proteome of Homo sapiens (encoded by 85 genes) and Saccharomyces cerevisiae (encoded by 61 genes). Now, we have included 34 new organism genomes with the acquisition of 2426 new peroxisomal homolog proteins. PeroxisomeDB 2.0 integrates the peroxisomal metabolome of whole microbody family by the new incorporation of the glycosome proteomes of trypanosomatids and the glyoxysome proteome of Arabidopsis thaliana. The site also provides a Peroxisome Metabolome of peroxisomal genes and proteins, their molecular interactions and metabolic pathways, tools for comparative genomics, predictive tools. Sponsors: Preoxisome Database is funded by Institut de Gntique et deBiologie Molculaire et Cellulaire.

Proper citation: Peroxisome Database (RRID:SCR_008352) Copy   

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https://wiki.cgb.indiana.edu/display/DGC/Home

The Daphnia Genomics Consortium (DGC) is an international network of investigators committed to mounting the freshwater crustacean Daphnia as a model system for ecology, evolution and the environmental sciences. Along with research activities, the DGC is: (1) coordinating efforts towards developing the Daphnia genomic toolbox, which will then be available for use by the general community; (2) facilitating collaborative cross-disciplinary investigations; (3) developing bioinformatic strategies for organizing the rapidly growing genome database; and (4) exploring emerging technologies to improve high throughput analyses of molecular and ecological samples. If we are to succeed in creating a new model system for modern life-sciences research, it will need to be a community-wide effort. Research activities of the DGC are primarily focused on creating genomic tools and information. When completed, the current projects will offer a first view of the Daphnia genome''s topography, including regions of high and low recombination, the distribution of transposable, repetitive and regulatory elements, the size and structure of genes and of their neighborhoods. This information is crucial in formulating testable hypotheses relating genetics and demographics to the evolutionary potential or constraints of natural populations. Projects aiming to compile identifiable genes with their function are also underway, together with robust methods to verify these findings. Finally, these tools are being tested, by exploring their uses in key ecological and toxicological investigations. Each project benefits from the leadership and expertise of many individuals. For further details, begin by contacting the project directors. The DGC consists of biologists from a broad spectrum of subdisciplines, including limnology, ecotoxicology, quantitative and population genetics, systematics, molecular biology and evolution, developmental biology, genomics and bioinformatics. In many regards, the rapid early success of the consortium results from its grass-roots origin promoting an international composition, under a cooperative model, with significant scientific breadth. We hold to this approach in building this network and encourage more people to participate. All the while, the DGC is structured to effectively reach specific goals. The consortium includes an advisory board (composed of experts of the various subdisciplines), whose responsibility is to act as the research community''s agent in guiding the development of Daphnia genomic resources. The advisors communicate directly to DGC members, who are either contributing genomic tools or actively seeking funds for this function. The consortium''s main body (given the widespread interest in applying genomic tools in environmental studies) are the affiliates, who make use of these tools for their research and who are soliciting support.

Proper citation: Daphnia genomics consortium (RRID:SCR_008148) Copy   

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http://baygenomics.ucsf.edu/

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 29, 2016. The BayGenomics gene-trap resource provides researchers with access to thousands of mouse embryonic stem (ES) cell lines harboring characterized insertional mutations in both known and novel genes. The major goal of BayGenomics is to identify genes relevant to cardiovascular and pulmonary disease.

Proper citation: BayGenomics (RRID:SCR_008168) Copy   

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http://www.uni-wh.de/pcogr

THIS RESOURCE IS NO LONGER IN SERVICE, documented on August 20,2019.The COG-database has become a powerful tool in the field of comparative genomics. The construction of this data-base is based on sequence homologies of proteins from different completely sequenced genomes. Highly homologous proteins are assigned to clusters of orthologous groups. The updated collection of orthologous protein sets for prokaryotes and eukaryotes is expected to be a useful platform for functional annotation of newly sequenced genomes, including those of complex eukaryotes, and genome-wide evolutionary studies. The availability of multiple, essentially complete genome sequences of prokaryotes and eukaryotes spurred both the demand and the opportunity for the construction of an evolutionary classification of genes from these genomes. Such a classification system based on orthologous relationships between genes appears to be a natural framework for comparative genomics and should facilitate both functional annotation of genomes and large-scale evolutionary studies. Here is a major update of the previously developed system for delineation of Clusters of Orthologous Groups of proteins (COGs) from the sequenced genomes of prokaryotes and unicellular eukaryotes and the construction of clusters of predicted orthologs for 7 eukaryotic genomes, which we named KOGs after eukaryotic orthologous groups. The COG collection currently consists of 138,458 proteins, which form 4873 COGs and comprise 75% of the 185,505 (predicted) proteins encoded in 66 genomes of unicellular organisms. The eukaryotic orthologous groups (KOGs) include proteins from 7 eukaryotic genomes: three animals (the nematode Caenorhabditis elegans, the fruit fly Drosophila melanogaster and Homo sapiens), one plant, Arabidopsis thaliana, two fungi (Saccharomyces cerevisiae and Schizosaccharomyces pombe), and the intracellular microsporidian parasite Encephalitozoon cuniculi. The current KOG set consists of 4852 clusters of orthologs, which include 59,838 proteins, or approximately 54% of the analyzed eukaryotic 110,655 gene products. Compared to the coverage of the prokaryotic genomes with COGs, a considerably smaller fraction of eukaryotic genes could be included into the KOGs; addition of new eukaryotic genomes is expected to result in substantial increase in the coverage of eukaryotic genomes with KOGs. Examination of the phyletic patterns of KOGs reveals a conserved core represented in all analyzed species and consisting of approximately 20% of the KOG set. This conserved portion of the KOG set is much greater than the ubiquitous portion of the COG set (approximately 1% of the COGs). In part, this difference is probably due to the small number of included eukaryotic genomes, but it could also reflect the relative compactness of eukaryotes as a clade and the greater evolutionary stability of eukaryotic genomes.

Proper citation: Phylogenetic Clusters of Orthologous Groups Ranking (RRID:SCR_008223) Copy   

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