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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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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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http://www.nisc.nih.gov/projects/comp_seq.html

Generates data for use in developing and refining computational tools for comparing genomic sequence from multiple species. The NISC Comparative Sequencing Program's goal is to establish a data resource consisting of sequences for the same set of targeted genomic regions derived from multiple animal species. The broader program includes plans for a diverse set of analytical studies using the generated sequence and the publication of a series of papers describing the results of those analysis in peer-reviewed journals in a timely fashion. Experimentally, this project involves the shotgun sequencing of mapped BAC clones. For each BAC, an assembly is first performed when a sufficient number of sequence reads have been generated to provide full shotgun coverage of the clone. At that time, the assembled sequence is submitted to the HTGS division of GenBank. Subsequent refinements of the sequence, including the generation of higher-accuracy finished sequence, results in the updating of the sequence record in GenBank. By immediately submitting our BAC-derived sequences to GenBank, it makes their data available as a public service to allow colleagues to speed up their research, consistent with the now well-established routine of sequencing centers participating in the Human Genome Project. However, at the same time, it has made considerable investment in acquiring these mapping and sequence data, including sizable efforts of graduate students, postdoctoral fellows, and other trainees. Furthermore, in most cases, large data sets involving multiple BAC sequences from multiple species must first be generated, often taking many months to accumulate, before the planned analysis can be performed and the resulting papers written and submitted for publication.

Proper citation: Comparative Vertebrate Sequencing (RRID:SCR_008213) 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://sbpdiscovery.org/research/centers/conrad-prebys-center-for-chemical-genomics/

The Conrad Prebys Center for Chemical Genomics (CPCCG) uses advanced screening technologies to identify high level chemical probes that interact with proteins involved in cellular processes. Optimization of these probes using medicinal chemistry and informatics will form the basis of a new generation of medicines. CPCCG is 1 of 4 Comprehensive Centers chosen nationally to be a part of the Molecular Libraries Probe Program (MLP), which established the Molecular Libraries Probe Production Centers Network (MLPCN). The goal is to produce small molecule probes that allow research into health and disease on the cellular level. CPCCG core services span a range of biochemical and cell-based screens for obtaining hits and provide chemistry resources for optimizing hits into probes or drug development. - Full scale screening capabilities and technology which can provide rapid screening on a broad diversity of assays and detection platforms - Several fully-integrated industrial-scale high-throughput screening (HTS) workstations - HTS microscopy/HCS and novel algorithm development for image analysis - Full hit-to-probe chemistry and exploratory pharmacology - Powerful NMR based Chemical Fragment Screening - Highly integrated informatics infrastructure and efficient data mining capabilities - Protein production facility - Cell production facility for scale-up tissue culture The CPCCG Screening Core can screen 96, 384 or 1536 well formats using either biochemical or cell-based assays, and can process over 300,000 wells per day. Total throughput capacity will climb to over 2 million compounds per day following the opening of Burnhams east coast campus in Lake Nona, Florida.

Proper citation: Conrad Prebys Center for Chemical Genomics (RRID:SCR_001687) Copy   

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http://www.softberry.com/

Developer of software tools for genomic research focused on computational methods of high throughput biomedical data analysis, including software to support next generation sequencing technologies, transcriptome analysis with RNASeq data, SNP detection and selection of disease specific SNP subsets. Provides custom genome annotation services.

Proper citation: SoftBerry (RRID:SCR_000902) Copy   

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http://soap.genomics.org.cn/

Software package that provides full solution to next generation sequencing data analysis consisting of an alignment tool (SOAPaligner/soap2), a re-sequencing consensus sequence builder (SOAPsnp), an indel finder ( SOAPindel ), a structural variation scanner ( SOAPsv ), a de novo short reads assembler ( SOAPdenovo ), and a GPU-accelerated alignment tool for aligning short reads with a reference sequence. (SOAP3/GPU)., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: SOAP (RRID:SCR_000689) Copy   

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

Software application that plots measures of pairwise linkage disequilibria for SNPs (entry from Genetic Analysis Software)

Proper citation: LDHEATMAP (RRID:SCR_006312) Copy   

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http://www.gepas.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 12,2023. An integrated packages of tools for microarray data analysis. GEPAS provides a web-based interface that offers diverse analysis options from the early step of preprocessing (normalization of Affymetrix and two-color microarray experiments and other preprocessing options), to the final step of the functional profiling of the experiment (using Gene Ontology, pathways, PubMed abstracts etc.), which include different possibilities for clustering, gene selection, class prediction and array-comparative genomic hybridization management.

Proper citation: Gene Expression Profile Analysis Suite (RRID:SCR_008341) Copy   

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http://wpicr.wpic.pitt.edu/WPICCompGen/hclust/hclust.htm

Software application that is a simple clustering method that can be used to rapidly identify a set of tag SNP's based upon genotype data (entry from Genetic Analysis Software), THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: HCLUST (RRID:SCR_009154) Copy   

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http://zfin.org

Model organism database that serves as central repository and web-based resource for zebrafish genetic, genomic, phenotypic and developmental data. Data represented are derived from three primary sources: curation of zebrafish publications, individual research laboratories and collaborations with bioinformatics organizations. Data formats include text, images and graphical representations.Serves as primary community database resource for laboratory use of zebrafish. Developed and supports integrated zebrafish genetic, genomic, developmental and physiological information and link this information extensively to corresponding data in other model organism and human databases.

Proper citation: Zebrafish Information Network (ZFIN) (RRID:SCR_002560) Copy   

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http://www.ifti.org/ootfd/

ooTFD (object-oriented Transcription Factors Database) is a successor to TFD, the original Transcription Factors Database. This database is aimed at capturing information regarding the polypeptide interactions which comprise and define the properties of transcription factors. ooTFD contains information about transcription factor binding sites, as well as composite relationships within transcription factors, which frequently occur as multisubunit proteins that form a complex interface to cellular processes outside the transcription machinery through protein-protein interactions. ooTFD contains information represented in TFD but also allows the representation of containment, composite, and interaction relationships between transcription factor polypeptides. It is designed to represent information about all transcription factors, both eukaryotic and prokaryotic, basal as well as regulatory factors, and multiprotein complexes as well as monomers.

Proper citation: object-oriented Transcription Factors Database (RRID:SCR_002435) Copy   

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http://romi.bu.edu/elisa/

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. ELISA is an online database that combines functional annotation with structure and sequence homology modeling to place proteins into sequence-structure-function neighborhoods. The atomic unit of the database is a set of sequences and structural templates that those sequences encode. A graph that is built from the structural comparison of these templates is called PDUG (protein domain universe graph). It introduces a method of functional inference through a probabilistic calculation done on an arbitrary set of PDUG nodes. Further, all PDUG structures are mapped onto all fully sequenced proteomes allowing an easy interface for evolutionary analysis and research into comparative proteomics. ELISA is the first database with applicability to evolutionary structural genomics explicitly in mind.

Proper citation: Evolutionary Lineage Inferred from Structural Analysis (RRID:SCR_002343) Copy   

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http://www.allgenes.org/

DoTS (Database Of Transcribed Sequences) is a human and mouse transcript index created from all publicly available transcript sequences. The input sequences are clustered and assembled to form the DoTS Consensus Transcripts that comprise the index. These transcripts are assigned stable identifiers of the form DT.123456 (and are often referred to as dots). The transcripts are in turn clustered to form putative DoTS Genes. These are assigned stable identifiers of the form DG.1234356. As of September 1, 2004, the DoTS annotation team has manually annotated 43,164 human and 78,054 mouse DoTS Transcripts (DTs), corresponding to 3,939 human and 7,752 mouse DoTS Genes (DGs). Use the manually annotated gene query to see the DoTS Transcripts that have been manually annotated. The focus of the DoTS project is integrating the various types of data (e.g., EST sequences, genomic sequence, expression data, functional annotation) in a structured manner which facilitates sophisticated queries that are otherwise not easy to perform. DoTS is built on the GUS Platform which includes a relational database that uses controlled vocabularies and ontologies to ensure that biologically meaningful queries can be posed in a uniform fashion. An easy way to start using the site is to search for DoTS Transcripts using an existing cDNA or mRNA sequence. Click on the BLAST tab at the top of the page and enter your sequence in the form provided. All the transcripts with significant sequence similarity to your query sequence will be displayed. Or use one of the provided queries to retrieve transcripts using a number of criteria. These queries are listed on the query page, which can also be reached by clicking on the tab marked query at the top of the page. Finally, the boolean query page allows these queries to be combined in a variety of ways. Sponsors: Funding provided by -NIH grant RO1-HG-01539-03 -DOE grant DE-FG02-00ER62893

Proper citation: Database of Transcribed Sequences (RRID:SCR_002334) Copy   

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http://sonorus.princeton.edu/hefalmp/

HEFalMp (Human Experimental/FunctionAL MaPper) is a tool developed by Curtis Huttenhower in Olga Troyanskaya's lab at Princeton University. It was created to allow interactive exploration of functional maps. Functional mapping analyzes portions of these networks related to user-specified groups of genes and biological processes and displays the results as probabilities (for individual genes), functional association p-values (for groups of genes), or graphically (as an interaction network). HEFalMp contains information from roughly 15,000 microarray conditions, over 15,000 publications on genetic and physical protein interactions, and several types of DNA and protein sequence analyses and allows the exploration of over 200 H. sapiens process-specific functional relationship networks, including a global, process-independent network capturing the most general functional relationships. Looking to download functional maps? Keep an eye on the bottom of each page of results: every functional map of any kind is generated with a Download link at the bottom right. Most functional maps are provided as tab-delimited text to simplify downstream processing; graphical interaction networks are provided as Support Vector Graphics files, which can be viewed using the Adobe Viewer, any recent version of Firefox, or the excellent open source Inkscape tool.

Proper citation: Human Experimental/FunctionAL MaPper: Providing Functional Maps of the Human Genome (RRID:SCR_003506) Copy   

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https://www.hsph.harvard.edu/alkes-price/software/

Software application that uses genotyping data from SNP arrays for accurately inferring chromosomal segments of distinct continental ancestry in admixed populations, using dense genetic data. (entry from Genetic Analysis Software)

Proper citation: Hapmix (RRID:SCR_004203) Copy   

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