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http://cello.life.nctu.edu.tw/

A subCELlular LOcalization predictor based on a multi-class support vector machine (SVM) classification system. CELLO uses 4 types of sequence coding schemes: the amino acid composition, the di-peptide composition, the partitioned amino acid composition and the sequence composition based on the physico-chemical properties of amino acids. They combine votes from these classifiers and use the jury votes to determine the final assignment.

Proper citation: CELLO (RRID:SCR_011968) Copy   

•   Source: SciCrunch Registry

http://iomics.us/

A genomics data analysis platform which generates decision models for healthcare organizations and medical research. This service is meant to utilize data through machine learning methods.

Proper citation: iOMICS (RRID:SCR_000239) Copy   

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https://github.com/wtsi-npg/Illuminus

A fast and accurate algorithm for assigning single nucleotide polymorphism (SNP) genotypes to microarray data from the Illumina BeadArray technology.

Proper citation: ILLUMINUS (RRID:SCR_000388) 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   

•   Source: SciCrunch Registry

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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http://www.broadinstitute.org/mpg/snap/

A computer program and web-based service for the rapid retrieval of linkage disequilibrium proxy single nucleotide polymorphism (SNP) results given input of one or more query SNPs and based on empirical observations from the International HapMap Project and the 1000 Genomes Project. A series of filters allow users to optionally retrieve results that are limited to specific combinations of genotyping platforms, above specified pairwise r2 thresholds, or up to a maximum distance between query and proxy SNPs. SNAP can also generate linkage disequilibrium plots

Proper citation: SNAP - SNP Annotation and Proxy Search (RRID:SCR_002127) Copy   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

http://rgp.dna.affrc.go.jp/E/index.html

Rice Genome Research Program (RGP) is an integral part of the Japanese Ministry of Agriculture, Forestry and Fisheries (MAFF) Genome Research Project. RGP now aims to completely sequence the entire rice genome and subsequently to pursue integrated goals in functional genomics, genome informatics and applied genomics. It is jointly coordinated by the National Institute of Agrobiological Sciences (NIAS), a government research institute under MAFF and the Society for Techno-innovation of Agriculture, Forestry and Fisheries (STAFF), a semi-private research organization managed and supported by MAFF and a consortium of some twenty Japanese companies. The research is funded with yearly grants from MAFF and additional funds from the Japan Racing Association (JRA). It is now the leading member of the International Rice Genome Sequencing Project (IRGSP), a consortium of ten countries sharing the sequencing of the 12 rice chromosomes. The IRGSP adopts the clone-by-clone shotgun sequencing strategy so that each sequenced clone can be associated with a specific position on the genetic map and adheres to the policy of immediate release of the sequence data to the public domain. In December 2004, the IRGSP completed the sequencing of the rice genome. The high-quality and map-based sequence of the entire genome is now available in public databases.

Proper citation: Rice Genome Research Project (RRID:SCR_002268) Copy   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

https://rgd.mcw.edu/rgdweb/portal/home.jsp?p=4

An integrated resource for information on genes, QTLs and strains associated with diabetes. The portal provides easy acces to data related to both Type 1 and Type 2 Diabetes and Diabetes-related Obesity and Hypertension, as well as information on Diabetic Complications. View the results for all the included diabetes-related disease states or choose a disease category to get a pull-down list of diseases. A single click on a disease will provide a list of related genes, QTLs, and strains as well as a genome wide view of these via the GViewer tool. A link from GViewer to GBrowse shows the genes and QTLs within their genomic context. Additional pages for Phenotypes, Pathways and Biological Processes provide one-click access to data related to diabetes. Tools, Related Links and Rat Strain Models pages link to additional resources of interest to diabetes researchers.

Proper citation: Diabetes Disease Portal (RRID:SCR_001660) Copy   

•   Source: SciCrunch Registry

https://software.broadinstitute.org/gatk/

A software package to analyze next-generation resequencing data. The toolkit offers a wide variety of tools, with a primary focus on variant discovery and genotyping as well as strong emphasis on data quality assurance. Its robust architecture, powerful processing engine and high-performance computing features make it capable of taking on projects of any size. This software library makes writing efficient analysis tools using next-generation sequencing data very easy, and second it's a suite of tools for working with human medical resequencing projects such as 1000 Genomes and The Cancer Genome Atlas. These tools include things like a depth of coverage analyzers, a quality score recalibrator, a SNP/indel caller and a local realigner. (entry from Genetic Analysis Software)

Proper citation: GATK (RRID:SCR_001876) Copy   

•   Source: SciCrunch Registry

http://csg.sph.umich.edu//abecasis/MACH/index.html

A Markov Chain based software tool for haplotyping, genotype imputation and disease association analysis that can resolve long haplotypes or infer missing genotypes in samples of unrelated individuals.

Proper citation: MACH 1.0 (RRID:SCR_001759) Copy   

•   Source: SciCrunch Registry

http://mousecyc.jax.org/

A manually curated database of both known and predicted metabolic pathways for the laboratory mouse. It has been integrated with genetic and genomic data for the laboratory mouse available from the Mouse Genome Informatics database and with pathway data from other organisms, including human. The database records for 1,060 genes in Mouse Genome Informatics (MGI) are linked directly to 294 pathways with 1,790 compounds and 1,122 enzymatic reactions in MouseCyc. (Aug. 2013) BLAST and other tools are available. The initial focus for the development of MouseCyc is on metabolism and includes such cell level processes as biosynthesis, degradation, energy production, and detoxification. MouseCyc differs from existing pathway databases and software tools because of the extent to which the pathway information in MouseCyc is integrated with the wealth of biological knowledge for the laboratory mouse that is available from the Mouse Genome Informatics (MGI) database.

Proper citation: MouseCyc (RRID:SCR_001791) Copy   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

https://www.a-star.edu.sg/gis/Our-Science/Technology-Platforms/Scientific-and-Research-Computing

Core provides research computing resources including bioinformatics, application development, data management and IT infrastructure to support next generation sequencing technologies, human genotyping, high throughput screening and computational biology researchers.

Proper citation: Genome Institute of Singapore Scientific and Research Computing Core Facility (RRID:SCR_017193) Copy   

•   Source: SciCrunch Registry

https://ircm.qc.ca/en/technological-services/bioinformatics

Core to support scientists within and outside IRCM in analysis of biological and clinical data, in particular high throughput genomic data. Operating on collaborative basis and paid services. Provides assistance with Data analysis for RNA-Seq, ChIP-Seq, RIP-Seq, DNA methylation, DNA-Seq, targeted sequencing of rRNAs, microarrays, customized training courses.

Proper citation: Montreal Clinical Research Bioinformatics Core Facility (RRID:SCR_017176) Copy   

•   Source: SciCrunch Registry

https://github.com/hillerlab/GenomeAlignmentTools

Software tool to incorporate newly detected repeat overlapping alignments into pairwise alignment chains. It only aligns local genomic regions that are bounded by colinear aligning blocks, as provided in chains, which makes it feasible to consider all seeds including those that overlap repetitive regions. Used to improve genome alignments by incorporating previously undetected local alignments between repetitive sequences.

Proper citation: RepeatFiller (RRID:SCR_017414) Copy   

•   Source: SciCrunch Registry