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http://www.comp-sys-bio.org/yeastnet/

This is a portal to the consensus yeast metabolic network as reconstructed from the genome sequence and literature. It is a highly annotated metabolic map that is periodically updated by a team of collaborators from various research groups. The first version of this reconstruction was published in Herrgrd, Swainston et al. (2008) A consensus yeast metabolic reconstruction obtained from a community approach to systems biology Nature Biotechnol. 26, 1155-1160 (you can access that network here). A second version has now been released and is awaiting publication. We plan on continuing to update this resource towards a complete metabolic network of yeast. All versions will remain accessible for historical purposes, however it is highly recommended that you always use the latest one since that is the most up to date. This effort started on the shoulders of a number of reconstructions of the metabolic network of yeast based on genomic and literature data that were published separately. (iMM904 and iLL672) However, due to the different approaches utilized in them, those earlier reconstructions had a significant number of differences. In addition they suffered from the use of non-standard names and overall they were not annotated with methods that are machine-readable. A community effort in 2007, led by the Manchester Centre for Integrative Systems Biology and the YSBN resulted in a consensus network representation of yeast metabolism, reconciling the earlier results. That effort is now ongoing under the leadership of the MCISB and with collaboration with colleagues under the UNICELLSYS FP7 project. Availability The network reconstruction is primarily assembled and provided as an SBML file enriched with MIRIAM-compliant annotations (which are embedded in the SBML through RDF). All small and macro- molecules are referenced to an authoritative database (e.g. Uniprot, ChEBI, etc.). All molecules and reactions are also annotated with appropriate publications that contain supporting evidence. Thus this network is entirely traceable and is presented in a computational framework. SBML is a format that is understood by a large number of software applications (see sbml.org). While the SBML file is the most efficient computational resource for these data, casual users also need access to the network. That is provided by a searchable relational database accessed directly from this website. The database pages also allow readers to add comments to any chemical species or reaction. Such comments are taken into consideration by the team collating new versions of the network and can lead to corrections and additions to the network. This reconstruction is provided in the following formats: :* an SBML file containing the reaction network and annotations, located to specific sub-cellular compartments :* an SBML file containing the reaction network and annotations without subcellular compartmentation (all reactions happening in a single compartment). :* a searcheable relational database, which uses the B-Net software from Pedro Mendes' group. The database version of this data set is managed with the B-Net software created in Pedro Mendes' group at the Virginia Bioinformatics Institute. B-Net's schema is a detailed representation of the underlying biochemistry and regulation. A number of reconstructions of the metabolic network of yeast based on genomic and literature data have been published. However, due to different approaches utilized in the reconstruction as well as different interpretations of the literature, the earlier reconstructions have significant number of differences. A community effort resulted in a consensus network model of yeast metabolism, combining results from previous models.

Proper citation: Yeast consensus metabolic network - A consensus reconstruction of yeast metabolism (RRID:SCR_002135) Copy   

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https://scicrunch.org/resolver/SCR_002250

THIS RESOURCE IS NO LONGER IN SERVICE. Documented Jul 19, 2024. Metadatabase manually curated that provides web accessible tools related to genomics, transcriptomics, proteomics and metabolomics. Used as informative directory for multi-omic data analysis.

Proper citation: OMICtools (RRID:SCR_002250) Copy   

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http://www.structuralgenomics.org/

The Structural Genomics Project aims at determination of the 3D structure of all proteins. It also aims to reduce the cost and time required to determine three-dimensional protein structures. It supports selection, registration, and tracking of protein families and representative targets. This aim can be achieved in four steps : -Organize known protein sequences into families. -Select family representatives as targets. -Solve the 3D structure of targets by X-ray crystallography or NMR spectroscopy. -Build models for other proteins by homology to solved 3D structures. PSI has established a high-throughput structure determination pipeline focused on eukaryotic proteins. NMR spectroscopy is an integral part of this pipeline, both as a method for structure determinations and as a means for screening proteins for stable structure. Because computational approaches have estimated that many eukaryotic proteins are highly disordered, about 1 year into the project, CESG began to use an algorithm. The project has been organized into two separate phases. The first phase was dedicated to demonstrating the feasibility of high-throughput structure determination, solving unique protein structures, and preparing for a subsequent production phase. The second phase, PSI-2, has focused on implementing the high-throughput structure determination methods developed in PSI-1, as well as homology modeling and addressing bottlenecks like modeling membrane proteins. The first phase of the Protein Structure Initiative (PSI-1) saw the establishment of nine pilot centers focusing on structural genomics studies of a range of organisms, including Arabidopsis thaliana, Caenorhabditis elegans and Mycobacterium tuberculosis. During this five-year period over 1,100 protein structures were determined, over 700 of which were classified as unique due to their < 30% sequence similarity with other known protein structures. The primary goal of PSI-1 was to develop methods to streamline the structure determination process, resulted in an array of technical advances. Several methods developed during PSI-1 enhanced expression of recombinant proteins in systems like Escherichia coli, Pichia pastoris and insect cell lines. New streamlined approaches to cell cloning, expression and protein purification were also introduced, in which robotics and software platforms were integrated into the protein production pipeline to minimize required manpower, increase speed, and lower costs. The goal of the second phase of the Protein Structure Initiative (PSI-2) is to use methods introduced in PSI-1 to determine a large number of proteins and continue development in streamlining the structural genomics pipeline. Currently, the third phase of the PSI is being developed and will be called PSI: Biology. The consortia will propose work on substantial biological problems that can benefit from the determination of many protein structures Sponsors: PSI is funded by the U.S. National Institute of General Medical Sciences (NIGMS),

Proper citation: Protein Structure Initiative (RRID:SCR_002161) Copy   

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http://mendel.stanford.edu/SidowLab/downloads/gerp/

Software that identifies constrained elements in multiple alignments by quantifying substitution deficits. These deficits represent substitutions that would have occurred if the element were neutral DNA, but did not occur because the element has been under functional constraint. We refer to these deficits as Rejected Substitutions. Rejected substitutions are a natural measure of constraint that reflects the strength of past purifying selection on the element. GERP estimates constraint for each alignment column; elements are identified as excess aggregations of constrained columns. A false-positive rate (which is user-settable) is calculated using "shuffled" alignments in which the order of columns is randomized., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: GERP (RRID:SCR_000563) Copy   

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http://www.genomeweb.com/

An independent, privately-held online and print publisher based in New York that serves the global community of scientists, technology professionals, and executives who use and develop the latest advanced tools in molecular biology research and molecular diagnostics. GenomeWeb's editorial mission is to serve readers with exclusive, in-depth coverage of the technology, institutions, and scientists that make up the worldwide research enterprise of molecular biology. We operate the largest online news organization focused on advanced research tools in genomics, proteomics, and bioinformatics. Our expert editors report and write with precision and clarity. GenomeWeb users can be found in major scientific organizations around the world, including biopharmaceutical companies, important research universities, biomedical institutes, and government laboratories. Our advertisers include leading suppliers of research tools, analytical instruments, and information technology. Getting started is easy - just register, and use your workplace e-mail address to maximize your access to content. Once you're logged in, you'll have complete free access to GenomeWeb Daily News, The Daily Scan, all of Genome Technology magazine, every GenomeWeb blog, and much more. GenomeWeb Free Content * GenomeWeb Daily News offers breaking news as well as feature articles on genomics, proteomics, bioinformatics, and more. Daily News covers not only the science and business news, but also regulatory and policy updates. Published online and twice daily by e-mail bulletin. * The Daily Scan is a roundup of the most interesting mainstream media articles, blog posts, and peer-reviewed literature relevant to genomic and proteomic scientists. Published daily online and by e-mail bulletin. * Genome Technology: GenomeWeb's magazine covers news, trends, people, and technologies in the systems biology field. It also includes Tech Guides, which feature expert troubleshooting advice on specific lab challenges, and Research Trend Digests. Published 10 times per year. Subscriptions to the print edition are free to active researchers in the US and $29 per year for non-scientists or anyone outside the US. Non-US researchers are eligible for a free subscription to the digital edition of Genome Technology. We may contact subscribers from time to time to requalify for the magazine, in compliance with our third-party readership audit. * Careers: Our Careers page includes content to help scientists in their jobs, with links to relevant external blog posts, profiles of alternative job paths, and more. Careers also includes our Job Listings board, where anyone can post job ads for free. * Cancer Minute: Updated daily and published by e-mail bulletin weekly, Cancer Minute rounds up the latest oncology peer-reviewed literature as well as news and blog posts. * Informatics Iron: This blog covers high-performance computing and the hardware side of bioinformatics, from GPUs to compute clusters and more. * The Sample: This blog focuses on a range of topics of interest to clinical labs, including the adoption of molecular tools, issues related to lab management, in-depth coverage of the major reference labs, and more. GenomeWeb Premium Content All GenomeWeb premium content provides readers with in-depth, exclusive coverage in key technology or application areas. These publications include business, technology, and research news; patent and IP information; product launches and upgrades; and hirings, promotions, and other people news.

Proper citation: GenomeWeb (RRID:SCR_000650) Copy   

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https://monarchinitiative.org/

Repository of information about model organisms, in vitro models, genes, pathways, gene expression, protein and genetic interactions, orthology, disease, phenotypes, publications, and authors, and ability to navigate multi-scale spatial and temporal phenotypes across in vivo and in vitro model systems in context of genetic and genomic data, using semantics and statistics. Discovery system provides basic and clinical science researchers, informaticists, and medical professionals with integrated interface and set of discovery tools to reveal genetic basis of disease, facilitate hypothesis generation, and identify novel candidate drug targets. Database that indexes authoritative information on experimental models of disease from MGI, RGD and ZFIN.

Proper citation: MONARCH Initiative (RRID:SCR_000824) Copy   

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http://mlemire.freeshell.org/SimM.README

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on April 6th,2023. Gene dropping simulation software. The program is a gzip'ed tar archive and is designed to run under UNIX/Linux operating system.

Proper citation: SIMM (RRID:SCR_000849) Copy   

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https://doi.org/10.1016/j.genrep.2019.100414

Eutherian comparative genomic analysis protocol as one framework of eutherian gene data set revisions. Protocol integrated gene annotations, phylogenetic analysis and protein molecular evolution analysis with 3 new tests including test of reliability of public eutherian genomic sequences using genomic sequence redundancies, test of contiguity of public eutherian genomic sequences using multiple pairwise genomic sequence alignments and test of protein molecular evolution using relative synonymous codon usage statistics. Public eutherian reference genomic sequence data sets.

Proper citation: Eutherian comparative genomic analysis protocol (RRID:SCR_014401) 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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http://mayoresearch.mayo.edu/mayo/research/schaid_lab/software.cfm

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 24,2023. Software application that simultaneously estimates a trait-locus position and its genetic effects for affected relative pairs (ARP) by one of two methods. Either allow a different trait-locus effect for each ARP type, or constrain the trait-locus effects according to the marginal effect of a single susceptibility locus. We include a goodness of fit statistic for the constrained model. (entry from Genetic Analysis Software)

Proper citation: ARP.GEE (RRID:SCR_013134) 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://www.mogene.com/

Core provides services to its partners in deploying genomic capabilities to bring critical solutions to both agriculture and industrial biotech operations. Core facility also provides assistance to research, biotech/pharma and government facilities. In addition to being an Agilent Certified Service Provider MOgene is also a CLIA certified genomics service facility offering one stop service and solution from Tissue/Cells to Analysis. Core offers RNA/DNA isolation, Microarrays, NextGen sequencing, Real time PCR and bioinformatics services.

Proper citation: MOgene (RRID:SCR_012433) Copy   

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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://gfpweb.aecom.yu.edu/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 12,2023. Database of expression patterns of C. elegans promoter::GFP constructs. A text description of the observed pattern is provided, indicating the stage(s) and tissue(s) in which GFP is expressed. Also available for some strains are the corresponding 2D and 3D images. Investigators may browse the entire list, search by gene name, tissue, stage, and pattern. Search results may be downloaded in .csv and .txt formats. All of the strains in the expression pattern database are displayed in the browse page. The records are organized by gene; information such as locus name, genomic location (WormBase), the presence of images and videos, and the actual expression pattern are shown in a tabular format.

Proper citation: Expression Patterns for C. elegans promoter GFP fusions (RRID:SCR_001619) Copy   

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http://hanalyzer.sourceforge.net/

An open-source data integration system designed to assist biologists in explaining the results observed in genome-scale experiments as well as generating new hypotheses. It combines information extraction techniques, semantic data integration, and reasoning and facilitates network visualization. The Hanalyzer source code and binaries are available for download.

Proper citation: Hanalyzer (RRID:SCR_000923) Copy   

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http://genecards.org

Database of human genes that provides concise genomic, proteomic, transcriptomic, genetic and functional information on all known and predicted human genes. Information featured in GeneCards includes orthologies, disease relationships, mutations and SNPs, gene expression, gene function, pathways, protein-protein interactions, related drugs and compounds and direct links to cutting edge research reagents and tools such as antibodies, recombinant proteins, clones, expression assays and RNAi reagents.

Proper citation: GeneCards (RRID:SCR_002773) 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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