Tools   Select Another Resource Report Type

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

http://hiv-web.lanl.gov/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 4, 2023. HIV Sequence Database is a database of annotated HIV sequences, plus a variety of tools and information for researchers studying HIV and SIV. The main aim of this website is to provide easy access to our sequence database, alignments, and the tools and interfaces we have produced. The HIV Sequence Database focuses on five primary goals: * Collecting HIV and SIV sequence data (all sequences since 1987) * Curating and annotating this data, and making it available to the scientific community * Computer analysis of HIV and related sequences * Production of software for the analysis of (sequence) data * The data and analyses on this site and published in a yearly printed publication, the HIV sequence Compendium, which is available free of charge.

Proper citation: HIV Sequence Database (RRID:SCR_002906) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/Tools/msa/clustalw2/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on January 19, 2022. Command line version of multiple sequence alignment program Clustal for DNA or proteins. Alignment is progressive and considers sequence redundancy. No longer being maintained. Please consider using Clustal Omega instead which accepts nucleic acid or protein sequences in multiple sequence formats NBRF/PIR, EMBL/UniProt, Pearson (FASTA), GDE, ALN/ClustalW, GCG/MSF, RSF.

Proper citation: Clustal W2 (RRID:SCR_002909) Copy   

•   Source: SciCrunch Registry

http://www.bioperl.org

BioPerl is a community effort to produce Perl code which is useful in biology. This toolkit of perl modules is useful in building bioinformatics solutions in Perl. It is built in an object-oriented manner so that many modules depend on each other to achieve a task. The collection of modules in the bioperl-live repository consist of the core of the functionality of bioperl. Additionally auxiliary modules for creating graphical interfaces (bioperl-gui), persistent storage in RDMBS (bioperl-db), running and parsing the results from hundreds of bioinformatics applications (Run package), software to automate bioinformatic analyses (bioperl-pipeline) are all available as Git modules in our repository. The BioPerl toolkit provides a library of hundreds of routines for processing sequence, annotation, alignment, and sequence analysis reports. It often serves as a bridge between different computational biology applications assisting the user to construct analysis pipelines. This chapter illustrates how BioPerl facilitates tasks such as writing scripts summarizing information from BLAST reports or extracting key annotation details from a GenBank sequence record. BioPerl includes modules written by Sohel Merchant of the GO Consortium for parsing and manipulating OBO ontologies. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: BioPerl (RRID:SCR_002989) Copy   

•   Source: SciCrunch Registry

http://iimcb.genesilico.pl/MetaLocGramN/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 5, 2023.A tool for subcellular localization prediction of Gram-negative proteins. You can also use MetaGramLocN via SOAP. SOAP enables you to invoke our method from scripts written in your programming language of choice.

Proper citation: MetaLocGramN (RRID:SCR_003154) Copy   

•   Source: SciCrunch Registry

http://www.mirbase.org/

Central online repository for microRNA nomenclature, sequence data, annotation and target prediction.Collection of published miRNA sequences and annotation.

Proper citation: miRBase (RRID:SCR_003152) Copy   

•   Source: SciCrunch Registry

http://www.xenbase.org/

Data collection for Xenopus laevis and Xenopus tropicalis biology and genomics.

Proper citation: Xenbase (RRID:SCR_003280) Copy   

•   Source: SciCrunch Registry

http://brainarray.mbni.med.umich.edu/Brainarray/Database/ProbeMatchDB/ncbi_probmatch_para_step1.asp

Matches a list of microarray probes across different microrarray platforms (GeneChip, EST from different vendors, Operon Oligos) and species (human, mouse and rat), based on NCBI UniGene and HomoloGene. The capability to match protein sequence IDs has just been added to facilitate proteomic studies. The ProbeMatchDB is mainly used for the design of verification experiments or comparing the microarray results from different platforms. It can be used for finding equivalent EST clones in the Research Genetics sequence verified clone set based on results from Affymetirx GeneChips. It will also help to identify probes representing orthologous genes across human, mouse and rat on different microarray platforms.

Proper citation: ProbeMatchDB 2.0 (RRID:SCR_003433) Copy   

•   Source: SciCrunch Registry

http://virome.diagcomputing.org/#view=home

A web-application designed for scientific exploration of metagenome sequence data collected from viral assemblages occurring within a number of different environmental contexts. The VIROME informatics pipeline focuses on the classification of predicted open-reading frames (ORFs) from viral metagenomes. The portal allows you to submit your viral metagenome to be processed through the VIROME analysis pipeline, and enable you to investigate your data via the VIROME user interface., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: VIROME (RRID:SCR_004362) Copy   

•   Source: SciCrunch Registry

http://sequenceontology.org/

A collaborative ontology for the definition of sequence features used in biological sequence annotation. SO was initially developed by the Gene Ontology Consortium. Contributors to SO include the GMOD community, model organism database groups such as WormBase, FlyBase, Mouse Genome Informatics group, and institutes such as the Sanger Institute and the EBI. Input to SO is welcomed from the sequence annotation community. The OBO revision is available here: http://sourceforge.net/p/song/svn/HEAD/tree/ SO includes different kinds of features which can be located on the sequence. Biological features are those which are defined by their disposition to be involved in a biological process. Biomaterial features are those which are intended for use in an experiment such as aptamer and PCR_product. There are also experimental features which are the result of an experiment. SO also provides a rich set of attributes to describe these features such as polycistronic and maternally imprinted. The Sequence Ontologies use the OBO flat file format specification version 1.2, developed by the Gene Ontology Consortium. The ontology is also available in OWL from Open Biomedical Ontologies. This is updated nightly and may be slightly out of sync with the current obo file. An OWL version of the ontology is also available. The resolvable URI for the current version of SO is http://purl.obolibrary.org/obo/so.owl.

Proper citation: SO (RRID:SCR_004374) Copy   

•   Source: SciCrunch Registry

http://caintegrator-info.nci.nih.gov/rembrandt

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on April 28,2023. REMBRANDT is a data repository containing diverse types of molecular research and clinical trials data related to brain cancers, including gliomas, along with a wide variety of web-based analysis tools that readily facilitate the understanding of critical correlations among the different data types. REMBRANDT aims to be the access portal for a national molecular, genetic, and clinical database of several thousand primary brain tumors that is fully open and accessible to all investigators (including intramural and extramural researchers), as well as the public at-large. The main focus is to molecularly characterize a large number of adult and pediatric primary brain tumors and to correlate those data with extensive retrospective and prospective clinical data. Specific data types hosted here are gene expression profiles, real time PCR assays, CGH and SNP array information, sequencing data, tissue array results and images, proteomic profiles, and patients'''' response to various treatments. Clinical trials'''' information and protocols are also accessible. The data can be downloaded as raw files containing all the information gathered through the primary experiments or can be mined using the informatics support provided. This comprehensive brain tumor data portal will allow for easy ad hoc querying across multiple domains, thus allowing physician-scientists to make the right decisions during patient treatments., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Repository of molecular brain neoplasia data (RRID:SCR_004704) Copy   

•   Source: SciCrunch Registry

http://blast.ncbi.nlm.nih.gov/Blast.cgi

Web search tool to find regions of similarity between biological sequences. Program compares nucleotide or protein sequences to sequence databases and calculates statistical significance. Used for identifying homologous sequences.

Proper citation: NCBI BLAST (RRID:SCR_004870) Copy   

•   Source: SciCrunch Registry

http://www.aftol.org/

THIS RESOURCE IS NO LONGER IN SERVICE, documented Jan 13, 2022; To enhance the understanding of the evolution of the Kingdom Fungi, 1500+ species were sampled for eight gene loci across all major fungal clades, plus a subset of taxa for a suite of morphological and ultrastructural characters with resulting data: AFTOL Molecular Database (generated by WASABI - Web Accessible Sequence Analysis for Biological Inference), Blast search the AFTOL Database (generated by WASABI), AFTOL primers (generated by WASABI), AFTOL primers by species (generated by WASABI), AFTOL alignments, and the AFTOL Structural and Biochemical Database. Users may submit samples to the AFTOL project. AFTOL is a collaboration centered around four universities in the United States: Duke University (Francois Lutzoni and Rytas Vilgalys), Clark University (David Hibbett), Oregon State University (Joey Spatafora), and University of Minnesota (David McLaughlin). Participants throughout the world have donated vouchers, taxon samples, and gene sequences. The aim of the project is to reconstruct the fungal tree of life using all available data for eight loci (nuclear ribosomal DNA: LSU, SSU, ITS (including 5.8s, ITS1 and ITS2); RNA polymerase II: RPB1, RPB2; elongation factor 1-alpha; mitochondrial SSU rDNA, and mitochondrial ATP synthase protein subunit 6). A further objective of this study is to summarize and integrate current knowledge regarding fungal subcellular features within this new phylogenetic framework. The name of the bioinformatic package developed for AFTOL is WASABI which provides an efficient communication platform to facilitate the collection and dissemination of molecular data to (and from) the laboratories and participants. All molecular data can be viewed, downloaded, verified, and corrected by the participants of AFTOL. A central goal of the WASABI interface is to establish an automated analysis framework that includes basecalling of newly generated chromatograms, contig assembly, quality verification of sequences (including a local BLAST), sequence alignment, and congruence test. Gene sequences that pass all tests and are finally verified by their authors will undergo automated phylogenetic analysis on a regular schedule. Although all steps are initially carried out noninteractively, the users can verify and correct the results at any step and thus initiate the reanalysis of dependent data.

Proper citation: AFTOL (RRID:SCR_004650) Copy   

•   Source: SciCrunch Registry

http://phast.wishartlab.com/

A web server designed to rapidly and accurately identify, annotate and graphically display prophage sequences within bacterial genomes or plasmids. It accepts either raw DNA sequence data or partially annotated GenBank formatted data and rapidly performs a number of database comparisons as well as phage cornerstone feature identification steps to locate, annotate and display prophage sequences and prophage features. Relative to other prophage identification tools, PHAST is up to 40 times faster and up to 15% more sensitive. It is also able to process and annotate both raw DNA sequence data and Genbank files, provide richly annotated tables on prophage features and prophage quality and distinguish between intact and incomplete prophage. PHAST also generates downloadable, high quality, interactive graphics that display all identified prophage components in both circular and linear genomic views. Databases available for download include Virus DB, Prophage and virus DB, Bacteria DB, and PHAST result DB. Pre-calculated genomes for viewing are also available.

Proper citation: PHAge Search Tool (RRID:SCR_005184) Copy   

•   Source: SciCrunch Registry

http://skatebase.org/

Portal supporting the North East Bioinformatics Collaborative''s project to sequence the genome of the Little Skate. Provided is a clearinghouse for Little Skate Genome Project and other publicly available Skate and Ray (Batoidea) genome data, and tools for data visualization and analysis. Little Skate Genome Project The little skate (Leucoraja erinacea) is a chondrichthyan (cartilaginous) fish native to the east coast of North America. Elasmobranchs (Skates, Rays, and Sharks) exhibit many fundamental vertebrate characteristics, including a neural crest, jaws and teeth, an adaptive immune system, and a pressurized circulatory system. These characteristics have been exploited to promote understanding about human physiology, immunology, stem cell biology, toxicology, neurobiology and regeneration. The development of standardized experimental protocols in elasmobranchs such as L. erinacea and the spiny dogfish shark (Squalus acanthias) has further positioned these organisms as important biomedical and developmental models. Despite this distinction, the only reported chondrichthyan genome is the low coverage (1.4x) draft genome of the elephant shark (Callorhinchus milii). To close the evolutionary gaps in available elasmobranch genome sequence data, and generate critical genomic resources for future biomedical study, the genome of L. erinacea is being sequenced by the North East Bioinformatics Collaborative (NEBC). As close evolutionary relatives, the little skate sequence will facilitate studies that employ dogfish shark and other elasmobranchs as model organisms. Skate tools include the SkateBLAST and the Skate Genome Browsers: Little Skate Mitochondrion, Thorny Skate Mitochondrion, and Ocellate Spot Skate Mitochondrion.

Proper citation: SkateBase (RRID:SCR_005302) Copy   

•   Source: SciCrunch Registry

http://www.glycosciences.de/modeling/sweet2/

Program that rapidly converts the primary sequence of a complex carbohydrate, as defined by standard nomenclature, directly into a reliable 3D molecular model by linking together preconstructed 3D molecular templates of monosaccharides in the manner specified by the sequence and then optimizing the 3D structure using the MM3 force field. The user interaction is supported by an input spreadsheet consisting of a grid of sugar symbol and connection type cells. Several ways to visualize and to output the generated structures and related information are implemented.

Proper citation: SWEET-DB (RRID:SCR_005324) Copy   

•   Source: SciCrunch Registry

http://www.scienceexchange.com/facilities/high-throughput-sequencing-and-microarray-facility-princeton

Core facility provides researchers with access to high-throughput sequencing technologies. The staff provide consultation on experimental design, library preparation, and data analysis. The Sequencing Core Facility works closely with Bioinformatics staff in the Center for Quantitative Biology to provide researchers with computing power and consulting services to analyze sequencing data.

Proper citation: Princeton High Throughput Sequencing and Microarray Facility (RRID:SCR_012619) Copy   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

http://www.ornl.gov/sci/techresources/Human_Genome/home.shtml

This resource gives information about the U.S. Human Genome Project, which was was a 13-year effort to to discover all the estimated 20,000-25,000 human genes and make them accessible for further biological study. The primary project goals were to: - identify all the approximately 20,000-25,000 genes in human DNA, - determine the sequences of the 3 billion chemical base pairs that make up human DNA, - store this information in databases, - improve tools for data analysis, - transfer related technologies to the private sector, and - address the ethical, legal, and social issues (ELSI) that may arise from the project. To help achieve these goals, researchers also studied the genetic makeup of several nonhuman organisms. These include the common human gut bacterium Escherichia coli, the fruit fly, and the laboratory mouse. These parallel studies helped to develop technology and interpret human gene function. Sponsors: The DOE Human Genome Program and the NIH National Human Genome Research Institute (NHGRI) together sponsored the U.S. Human Genome Project.

Proper citation: Human Genome Project Information (RRID:SCR_013028) Copy   

•   Source: SciCrunch Registry

http://www.mrc-lmb.cam.ac.uk/genomes/dolop/

DOLOP is an exclusive knowledge base for bacterial lipoproteins by processing information from 510 entries to provide a list of 199 distinct lipoproteins with relevant links to molecular details. Features include functional classification, predictive algorithm for query sequences, primary sequence analysis and lists of predicted lipoproteins from 43 completed bacterial genomes along with interactive information exchange facility. This website along will have additional information on the biosynthetic pathway, supplementary material and other related figures. DOLOP also contains information and links to molecular details for about 278 distinct lipoproteins and predicted lipoproteins from 234 completely sequenced bacterial genomes. Additionally, the website features a tool that applies a predictive algorithm to identify the presence or absence of the lipoprotein signal sequence in a user-given sequence. The experimentally verified lipoproteins have been classified into different functional classes and more importantly functional domain assignments using hidden Markov models from the SUPERFAMILY database that have been provided for the predicted lipoproteins. Other features include: primary sequence analysis, signal sequence analysis, and search facility and information exchange facility to allow researchers to exchange results on newly characterized lipoproteins.

Proper citation: DOLOP: A Database of Bacterial Lipoproteins (RRID:SCR_013487) Copy   

•   Source: SciCrunch Registry