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http://dnatraffic.ibb.waw.pl/

DNAtraffic database is dedicated to be an unique comprehensive and richly annotated database of genome dynamics during the cell life. DNAtraffic contains extensive data on the nomenclature, ontology, structure and function of proteins related to control of the DNA integrity mechanisms such as chromatin remodeling, DNA repair and damage response pathways from eight model organisms commonly used in the DNA-related study: Homo sapiens, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans, Saccharomyces cerevisiae, Schizosaccharomyces pombe, Escherichia coli and Arabidopsis thaliana. DNAtraffic contains comprehensive information on diseases related to the assembled human proteins. Database is richly annotated in the systemic information on the nomenclature, chemistry and structure of the DNA damage and drugs targeting nucleic acids and/or proteins involved in the maintenance of genome stability. One of the DNAtraffic database aim is to create the first platform of the combinatorial complexity of DNA metabolism pathway analysis. Database includes illustrations of pathway, damage, protein and drug. Since DNAtraffic is designed to cover a broad spectrum of scientific disciplines it has to be extensively linked to numerous external data sources. Database represents the result of the manual annotation work aimed at making the DNAtraffic database much more useful for a wide range of systems biology applications. DNAtraffic database is freely available and can be queried by the name of DNA network process, DNA damage, protein, disease, and drug.

Proper citation: DNAtraffic (RRID:SCR_008886) Copy   

•   Source: SciCrunch Registry

http://mips.gsf.de/simap/

It provides a database based on a pre-computed similarity matrix covering the similarity space formed by >4 million amino acid sequences from public databases and completely sequenced genomes. The database is capable of handling very large datasets and is updated incrementally. For sequence similarity searches and pairwise alignments, we implemented a grid-enabled software system, which is based on FASTA heuristics and the Smith Waterman algorithm. SimpleSIMAP and AdvancedSIMAP retrieve homologs for given protein sequences that need to be contained in the SIMAP database. While SimpleSIMAP provides only selected parameters and preconfigured search spaces, the AdvancedSIMAP allows the user to specify search space, filtering and sorting parameters in a flexible manner. Both types of queries result in lists of homologs that are linked in turn to their homologs. So the web interfaces allow users to explore quickly and interactively the protein world by homology. Sponsors: SIMAP is supported by the Department of Genome Oriented Bioinformatics of the Technische Universitt Mnchen and the Institute for Bioinformatics of the GSF-National Research Center for Environment and Health.

Proper citation: SIMAP (RRID:SCR_007927) Copy   

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http://systers.molgen.mpg.de/

SYSTERS is a database of protein sequences grouped into homologous families and superfamilies. The SYSTERS project aims to provide a meaningful partitioning of the whole protein sequence space by a fully automatic procedure. A refined two-step algorithm assigns each protein to a family and a superfamily. The sequence data underlying SYSTERS release 4 now comprise several protein sequence databases derived from completely sequenced genomes (ENSEMBL, TAIR, SGD and GeneDB), in addition to the comprehensive Swiss-Prot/TrEMBL databases. To augment the automatically derived results, information from external databases like Pfam and Gene Ontology are added to the web server. Furthermore, users can retrieve pre-processed analyses of families like multiple alignments and phylogenetic trees. New query options comprise a batch retrieval tool for functional inference about families based on automatic keyword extraction from sequence annotations. A new access point, PhyloMatrix, allows the retrieval of phylogenetic profiles of SYSTERS families across organisms with completely sequenced genomes. Gene, Human, Vertebrate, Genome, Human ORFs

Proper citation: SYSTERS (RRID:SCR_007955) Copy   

•   Source: SciCrunch Registry

http://supfam.org/SUPERFAMILY/

SUPERFAMILY is a database of structural and functional protein annotations for all completely sequenced organisms. The SUPERFAMILY annotation is based on a collection of hidden Markov models, which represent structural protein domains at the SCOP superfamily level. A superfamily groups together domains which have an evolutionary relationship. The annotation is produced by scanning protein sequences from over 1,700 completely sequenced genomes against the hidden Markov models.

Proper citation: SUPERFAMILY (RRID:SCR_007952) Copy   

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http://www.thearkdb.org/arkdb/

This website contains the mapping sequence of poultry. The ArkDB database system aims to provide a comprehensive public repository for genome mapping data from farmed and other animal species. In doing so, it aims to provide a route in to genomic and other sequence from the initial viewpoint of linkage mapping, RH mapping, physical mapping or - possibly more importantly - QTL mapping data. It's supported, in part, by the USDA-CSREES National Animal Genome Research Program in order to serve the poultry genome mapping community. This system represents a complete rewrite of the original version with the code migrated to java and the underlying database targeted at postgres (although any standards-compliant database engine should suffice). The initial release records details of maps and the markers that they contain. There are alternative entry points that target either a chromosome or a specific mapping analysis as the starting point. Limited relationships between markers are recorded and displayed. As with the previous version, all maps are drawn using data extracted from the database on the fly.

Proper citation: ChickBase (RRID:SCR_008147) Copy   

•   Source: SciCrunch Registry

http://microbialgenomics.energy.gov/index.shtml

Through its Microbial Genome Program (MGP) and its Genomics:GTL (GTL) program, DOEs Office of Biological and Environmental Research (BER) has sequenced more than 485 microbial genomes and 30 microbial communities having specialized biological capabilities. Identifying these genes will help investigators discern how gene activities in whole living systems are orchestrated to solve myriad life challenges. The MGP was begun in 1994 as a spinoff from the Human Genome Program. The goal of the program was to sequence the genomes of a number of nonpathogenic microbes that would be useful in solving DOE''s mission challenges in environmental-waste cleanup, energy production, carbon cycling, and biotechnology. Past projects include microbial genome program, microbial cell project, and the Laboratory Science Program at the DOE Joint Genome Institute. The two ongoing projects are Genomics: GTL program and Community Sequencing Program at the DOE Joint Genome Institute. Sponsors: Site sponsored by the U.S. Department of Energy Office of Science, Office of Biological and Environmental Research, THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Microbial Genomics Program (RRID:SCR_008140) Copy   

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http://www.sanger.ac.uk/Projects/C_elegans/index.shtml

The Sanger Institute and the Genome Sequencing Center at the Washington University School of Medicine, St. Louis have collaborated to sequence the genomes of both C. elegans and C. briggsae. The completed C. elegans genome sequence is represented by over 3,000 individual clone sequences which can be accessed through this site (or through WormBase). These sequences are submitted to EMBL whenever the sequence or annotation changes (e.g. modification to gene structures) and these submissions are then mirrored to GenBank and DDBJ. These sequences (along with ESTs and proteins) can be searched on our C. elegans BLAST server. WormBase is the repository of mapping, sequencing and phenotypic information for C. elegans. The worm informatics group at the Sanger Institute play a key role in assembling the whole database. They also curate and develop some of the constituent databases that comprise WormBase.

Proper citation: Caenorhabditis Genome Sequencing Projects (RRID:SCR_008155) Copy   

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http://dimer.tamu.edu/doodle/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 15, 2013. Doodle is a database that was developed to store and distribute information about the protein oligomerization domains that are encoded by various genomes. The protein oligomerization domains described here were found using the lambda repressor fusion system. Doodle uses a schema that is based on EnsEMBL, while also utilizing bioperl modules to both store and retrieve data. The frontend was developed entirely in perl, while the backend utilizes MySQL. GMOD was used to develop the genomic view.

Proper citation: Database of oligomerization domains from lambda experiments (RRID:SCR_008107) Copy   

•   Source: SciCrunch Registry

http://chromium.lovd.nl/LOVD2/home.php?select_db=CDKN2A

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. The CDKN2A Database presents the germline and somatic variants of the CDKN2A tumor suppressor gene recorded in human disease through June 2003, annotated with evolutionary, structural, and functional information, in a format that allows the user to either download it or manipulate it for their purposes online. The goal is to provide a database that can be used as a resource by researchers and geneticists and that aids in the interpretation of CDKN2A missense variants. Most online mutation databases present flat files that cannot be manipulated, are often incomplete, and have varying degrees of annotation that may or may not help to interpret the data. They hope to use CDKN2A as a prototype for integrating computational and laboratory data to help interpret variants in other cancer-related genes and other single nucleotide polymorphisms (SNPs) found throughout the genome. Another goal of the lab is to interpret the functional and disease significance of missense variants in cancer susceptibility genes. Eventually, these results will be relevant to the interpretation of single nucleotide polymorphisms (SNPs) in general. The CDKN2A locus is a valuable model for assessing relationships among variation, structure, function, and disease because: Variants of this gene are associated with hereditary cancer: Familial Melanoma (and related syndromes); somatic alterations play a role in carcinogenesis; allelic variants occur whose functional consequences are unknown; reliable functional assays exist; and crystal structure is known. All variants in the database are recorded according to the nomenclature guidelines as outlined by the Human Genome Variation Society. This database is currently designed for research purposes only and is not yet recommended as a clinical resource. Many of the mutations reported here have not been tested for disease association and may represent normal, non-disease causing polymorphisms.

Proper citation: CDKN2A Database (RRID:SCR_008179) Copy   

•   Source: SciCrunch Registry

http://hyperbrowser.uio.no/hb/

A generic web-based system, providing statistical methodology and computing power to handle a variety of biological inquires on genomic datasets.

Proper citation: Genomic HyperBrowser (RRID:SCR_010909) Copy   

•   Source: SciCrunch Registry

https://genome-cancer.ucsc.edu/

A suite of web-based tools to visualize, integrate and analyze cancer genomics and its associated clinical data. It is possible to display your own clinical data within one of their datasets.

Proper citation: UCSC Cancer Genomics Browser (RRID:SCR_011796) Copy   

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http://www.genome.gov/27549169

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

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

•   Source: SciCrunch Registry

https://github.com/Nextomics/NextPolish

Software tool to fix base errors SNV/Indel in genome generated by noisy reads. Used to correct error bases in reference genome.

Proper citation: NextPolish (RRID:SCR_025232) Copy   

•   Source: SciCrunch Registry

https://github.com/BackofenLab/HVSeeker/tree/main

Software tool for distinguishing between bacterial and phage sequences. Consists of two separate models: one analyzing DNA sequences and the other focusing on proteins.

Proper citation: HVSeeker (RRID:SCR_026120) Copy   

•   Source: SciCrunch Registry

https://jmorp.megabank.tohoku.ac.jp/

Japanese multi omics reference panel. Provides multidimensional approach to diversity of Japanese population. Public database for plasma metabolome and proteome analyses. Updated to metabolome, genome, transcriptome, metagenome, number of samples, analysis methods of each dataset, expanding links between each layer and links between hierarchies.

Proper citation: jMORP (RRID:SCR_024755) Copy   

•   Source: SciCrunch Registry

http://ssom.luc.edu/genomics/

Core provides next-generation sequencing capabilities using Illumina MiSeq. Helps with experimental design, quality control analysis, library preparation, and data analysis. MiSeq desktop sequencer allows to access applications such as targeted gene sequencing, metagenomics, small genome sequencing, targeted gene expression, amplicon sequencing, and HLA typing.MiSeq is capable of delivering up to 15 Gb of output with 25 million sequencing reads and 2x300 basepair read lengths.

Proper citation: Loyola University Genomics Core Facility (RRID:SCR_017857) Copy   

•   Source: SciCrunch Registry

https://med.miami.edu/centers-and-institutes/hihg/research-centers/center-for-genome-technology/sequencing-core

Core Facility offers services utilizing Illumina Novaseq X Plus, Pacific Biosciences Revio, ONT Promethion, and 10x Genomics platforms. The core has extensive knowledge of DNA/RNA library preparation for short-read, long-read and single cell sequencing. Sample preparation is fully automated on Perkin Elmer robotic workstations and tracked via the Clarity LIMS. Services include, but are not limited to, whole genome, exome and custom capture protocols, as well as, bulk RNAseq, small RNAseq, and single cell RNA sequencing.

Proper citation: University of Miami Hussman Institute for Human Genomics Sequencing Core Facility (RRID:SCR_017828) Copy   

•   Source: SciCrunch Registry

http://cqls.oregonstate.edu/

Formerly Center for Genome Research and Biocomputing Core Facility. Functions and facilities include services in genomics, functional genomics, genotyping and imaging.Biocomputing facilities with computing infrastructure, which includes managed cloud and shared resources, data analyses and training are customized to individual needs, including genome assembly and annotation, analysis of RNAseq, GBS, and metagenomics data, and GPU-enabled deep learning analyses.

Proper citation: Oregon State University Center for Quantitative Life Sciences Core Facility (RRID:SCR_018373) Copy   

•   Source: SciCrunch Registry

http://sourceforge.net/projects/dmetanalyzer/

Software tool for the automatic association analysis among the variation of the patient genomes and the clinical conditions of patients, i.e. the different response to drugs. The system allows: (i) to automatize the workflow of analysis of DMET (drug metabolism enzymes and transporters)-SNP (Single Nucleotide Polymorphism) data avoiding the use of multiple tools; (ii) the automatic annotation of DMET-SNP data and the search in existing databases of SNPs (e.g. dbSNP), (iii) the association of SNP with pathway through the search in PharmaKGB, a major knowledge base for pharmacogenomic studies. It has a simple graphical user interface that allows users (doctors/biologists) to upload and analyze DMET files produced by Affymetrix DMET-Console in an interactive way.

Proper citation: DMET-Analyzer (RRID:SCR_002030) Copy   

•   Source: SciCrunch Registry

https://www.hgsc.bcm.edu/software/mercury

An automated, flexible, and extensible analysis workflow that provides accurate and reproducible genomic results at scales ranging from individuals to large cohorts. The analysis pipeline is deployed in local hardware and the Amazon Web Services cloud via the DNAnexus platform.

Proper citation: Mercury (RRID:SCR_004231) Copy   

•   Source: SciCrunch Registry