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http://genespeed.ccf.org/home/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 16, 2013. Database and customized tools to study the PFAM protein domain content of the transcriptome for all expressed genes of Homo sapiens, Mus musculus, Drosophila melanogaster, and Caenorhabditis elegans tethered to both a genomics array repository database and a range of external information resources. GeneSpeed has merged information from several existing data sets including the Gene Ontology Consortium, InterPro, Pfam, Unigene, as well as micro-array datasets. GeneSpeed is a database of PFAM domain homology contained within Unigene. Because Unigene is a non-redundant dbEST database, this provides a wide encompassing overview of the domain content of the expressed transcriptome. We have structured the GeneSpeed Database to include a rich toolset allowing the investigator to study all domain homology, no matter how remote. As a result, homology cutoff score decisions are determined by the scientist, not by a computer algorithm. This quality is one of the novel defining features of the GeneSpeed database giving the user complete control of database content. In addition to a domain content toolset, GeneSpeed provides an assortment of links to external databases, a unique and manually curated Transcription Factor Classification list, as well as links to our newly evolving GeneSpeed BetaCell Database. GeneSpeed BetaCell is a micro-array depository combined with custom array analysis tools created with an emphasis around the meta analysis of developmental time series micro-array datasets and their significance in pancreatic beta cells.

Proper citation: GeneSpeed- A Database of Unigene Domain Organization (RRID:SCR_002779) Copy   

•   Source: SciCrunch Registry

http://www.greenphyl.org/

A database designed for plant comparative and functional genomics based on complete genomes. It comprises complete proteome sequences from the major phylum of plant evolution. The clustering of these proteomes was performed to define a consistent and extensive set of homeomorphic plant families. Based on this, lists of gene families such as plant or species specific families and several tools are provided to facilitate comparative genomics within plant genomes. The analyses follow two main steps: gene family clustering and phylogenomic analysis of the generated families. Once a group of sequences (cluster) is validated, phylogenetic analyses are performed to predict homolog relationships such as orthologs and ultraparalogs.

Proper citation: GreenPhylDB (RRID:SCR_002834) Copy   

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http://www.flymine.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 14,2026. Integrated database of genomic, expression and protein data for Drosophila, Anopheles, C. elegans and other organisms. You can run flexible queries, export results and analyze lists of data. FlyMine presents data in categories, with each providing information on a particular type of data (for example Gene Expression or Protein Interactions). Template queries, as well as the QueryBuilder itself, allow you to perform searches that span data from more than one category. Advanced users can use a flexible query interface to construct their own data mining queries across the multiple integrated data sources, to modify existing template queries or to create your own template queries. Access our FlyMine data via our Application Programming Interface (API). We provide client libraries in the following languages: Perl, Python, Ruby and & Java API

Proper citation: FlyMine (RRID:SCR_002694) Copy   

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http://www.ambystoma.org/

Portal that supports Ambystoma-related research and educational efforts. It is composed of several resources: Salamander Genome Project, Ambystoma EST Database, Ambystoma Gene Collection, Ambystoma Map and Marker Collection, Ambystoma Genetic Stock Center, and Ambystoma Research Coordination Network.

Proper citation: Sal-Site (RRID:SCR_002850) Copy   

•   Source: SciCrunch Registry

http://cbio.mskcc.org/

Computational biology research at Memorial Sloan-Kettering Cancer Center (MSKCC) pursues computational biology research projects and the development of bioinformatics resources in the areas of: sequence-structure analysis; gene regulation; molecular pathways and networks, and diagnostic and prognostic indicators. The mission of cBio is to move the theoretical methods and genome-scale data resources of computational biology into everyday laboratory practice and use, and is reflected in the organization of cBio into research and service components ~ the intention being that new computational methods created through the process of scientific inquiry should be generalized and supported as open-source and shared community resources. Faculty from cBio participate in graduate training provided through the following graduate programs: * Gerstner Sloan-Kettering Graduate School of Biomedical Sciences * Graduate Training Program in Computational Biology and Medicine Integral to much of the research and service work performed by cBio is the creation and use of software tools and data resources. The tools that we have created and utilize provide evidence of our involvement in the following areas: * Cancer Genomics * Data Repositories * iPhone & iPod Touch * microRNAs * Pathways * Protein Function * Text Analysis * Transcription Profiling

Proper citation: Computational Biology Center (RRID:SCR_002877) Copy   

•   Source: SciCrunch Registry

http://www.gabipd.org/

Database that collects, integrates and links all relevant primary information from the GABI plant genome research projects and makes them accessible via internet. Its purpose is to support plant genome research in Germany, to yield information about commercial important plant genomes, and to establish a scientific network within plant genomic research.
GreenCards is the main interface for text based retrieval of sequence, SNP, mapping data etc. Sharing and interchange of data among collaborating research groups, industry and the patent- and licensing agency are facilitated.
* GreenCards: Text based search for sequence, mapping, SNP data etc. * Maps: Visualization of genetic or physical maps. * BLAST: Secure BLAST search against different public databases or non-public sequence data stored in GabiPD. * Proteomics: View interactive 2D-gels and view or download information for identified protein spots. Registered users can submit data via secure file upload.

Proper citation: Gabi Primary Database (RRID:SCR_002755) Copy   

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http://www.broadinstitute.org/annotation/genome/magnaporthe_comparative/MultiHome.html

The Magnaporthe comparative genomics database provides accesses to multiple fungal genomes from the Magnaporthaceae family to facilitate the comparative analysis. As part of the Broad Fungal Genome Initiative, the Magnaporthe comparative project includes the finished M. oryzae (formerly M. grisea) genome, as well as the draft assemblies of Gaeumannomyces graminis var. tritici and M. poae. It provides users the tools to BLAST search, browse genome regions (to retrieve DNA, find clones, and graphically view sequence regions), and provides gene indexes and genome statistics. We were funded to attempt 7x sequence coverage comprising paired end reads from plasmids, Fosmids and BACs. Our strategy involves Whole Genome Shotgun (WGS) sequencing, in which sequence from the entire genome is generated and reassembled. Our specific aims are as follows: 1. Generate and assemble sequence reads yielding 7X coverage of the Magnaporthe oryzae genome through whole genome shotgun sequencing. 2. Generate and incorporate BAC and Fosmid end sequences into the genome assembly to provide a paired-end of average every 2 kb. 3. Integrate the genome sequence with existing physical and genetic map information. 4. Perform automated annotation of the sequence assembly. 5. Distribute the sequence assembly and results of our annotation and analysis through a freely accessible, public web server and by deposition of the sequence assembly in GenBank.

Proper citation: Magnaporthe comparative Database (RRID:SCR_003079) Copy   

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http://cgap.nci.nih.gov/

Project to determine the gene expression profiles of normal, precancer, and cancer cells, whose generated resources are available to the cancer community. Interconnected modules provide access to all CGAP data, bioinformatic analysis tools, and biological resources allowing the user to find in silico answers to biological questions in a fraction of the time it once took in the laboratory. * Genes * Tissues * Pathways * RNAi * Chromosomes * SAGE Genie * Tools

Proper citation: Cancer Genome Anatomy Project (RRID:SCR_003072) Copy   

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http://deweylab.biostat.wisc.edu/rsem/

Software package for quantifying gene and isoform abundances from single end or paired end RNA Seq data. Accurate transcript quantification from RNA Seq data with or without reference genome. Used for accurate quantification of gene and isoform expression from RNA-Seq data.

Proper citation: RSEM (RRID:SCR_000262) Copy   

•   Source: SciCrunch Registry

http://gst.tennessee.edu/

Graduate School of Genome Science and Technology (GST) is a Life Science graduate program founded on two premises. First, whole-genome sequences and related large-scale datasets have transformed how we perform biological research, a trend that is gathering momentum and is anticipated to frame the way the biology research is accomplished for many years to come. Second, advances in technology, whether at the level of instrumentation, computation, or wet lab reagents, have long been a powerful driving force in biology. The GST program is home to faculty mentors from many walks of life. The virulence factors of pathogenic fungi and the engineering of photosynthetic reaction complexes for bioenergy harvesting are just two examples from the cornucopia of research projects being pursued in GST.

Proper citation: University of Tennessee Genome Science and Technology Graduate Program (RRID:SCR_000038) Copy   

•   Source: SciCrunch Registry

https://github.com/Brazelton-Lab/seq-annot

Software Python package for annotating and counting genomic features in genomes and metagenomes. Software tools to facilitate annotation and comparison of genomes and metagenomes.

Proper citation: seq-annot (RRID:SCR_018731) Copy   

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https://github.com/lufuhao/GeneSyntenyPipeline

Software pipeline was designed to draw gene synteny plot between genomes and obtain 1 to 1 gene pairs from each genome.

Proper citation: GeneSyntenyPipeline (RRID:SCR_018198) Copy   

•   Source: SciCrunch Registry

http://brainarray.mbni.med.umich.edu/Brainarray/Database/CustomCDF/genomic_curated_CDF.asp

Brainarray custom CDFs for processing raw Affymetrix data. Used to map probe to probesets. Oligonucleotide probes on GeneChips are reorganized based on latest genome and transcriptome information.

Proper citation: CustomCDF (RRID:SCR_018527) Copy   

•   Source: SciCrunch Registry

https://github.com/vgteam/vg#vg

Software toolkit to improve read mapping by representing genetic variation in reference.Provides succinct encoding of sequences of many genomes.

Proper citation: variation graph (RRID:SCR_024369) Copy   

•   Source: SciCrunch Registry

http://projects.tcag.ca/xenodup/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 16, 2013. It contains information about segmental duplications in the genomes of chimpanzee, mouse, and rat. The criteria used to identify regions of segmental duplication are: * Sequence identity of at least 90% * Sequence length of at least 5 kb * Not be entirely composed of repetitive elements. BACKGROUND: The high quality of the mouse genome draft sequence and its associated annotations are an invaluable biological resource. Identifying recent duplications in the mouse genome, especially in regions containing genes, may highlight important events in recent murine evolution. In addition, detecting recent sequence duplications can reveal potentially problematic regions of the genome assembly. We use BLAST-based computational heuristics to identify large (>/= 5 kb) and recent (>/= 90% sequence identity) segmental duplications in the mouse genome sequence. Here we present a database of recently duplicated regions of the mouse genome found in the mouse genome sequencing consortium (MGSC) February 2002 and February 2003 assemblies. RESULTS: We determined that 33.6 Mb of 2,695 Mb (1.2%) of sequence from the February 2003 mouse genome sequence assembly is involved in recent segmental duplications, which is less than that observed in the human genome (around 3.5-5%). From this dataset, 8.9 Mb (26%) of the duplication content consisted of "unmapped" chromosome sequence. Moreover, we suspect that an additional 18.5 Mb of sequence is involved in duplication artifacts arising from sequence misassignment errors in this genome assembly. By searching for genes that are located within these regions, we identified 675 genes that mapped to duplicated regions of the mouse genome. Sixteen of these genes appear to have been duplicated independently in the human genome. From our dataset we further characterized a 42 kb recent segmental duplication of Mater, a maternal-effect gene essential for embryogenesis in mice. CONCLUSION: Our results provide an initial analysis of the recently duplicated sequence and gene content of the mouse genome. Many of these duplicated loci, as well as regions identified to be involved in potential sequence misassignment errors, will require further mapping and sequencing to achieve accuracy. A Genome Browser database was set up to display the identified duplication content presented in this work. This data will also be relevant to the growing number of investigators who use the draft genome sequence for experimental design and analysis. The segmental duplication data and summary statistics are available for download and can also be visualized in a genome browser in the GBrowse section. Selected annotation tracks (except the segmental duplication track) have also been obtained from UCSC and loaded into the genome browser. Detailed information (e.g. overlapping genes, overlapping clones, detailed alignment) can be obtained by clicking on a duplication cluster in GBrowse. Both keyword search and BLAT search are available. Analyses based on previous genome assemblies can be found in the Previous Analyses section. Recent Developments The Non-Human Genome Segmental Duplication Database is continually updated including the archived copies of the analysis of all previous genome assemblies and will include all new species as they become available. Acknowledgments We thank The Centre for Applied Genomics at the Hospital for Sick Children (HSC) as well as collaborators worldwide. Supported by Genome Canada the Howard Hughes Medical Institute International Scholar Program (to S.W.S.) and the HSC Foundation.

Proper citation: Non-Human Genome Segmental Duplication Database (RRID:SCR_000470) Copy   

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http://bovinegenome.org/

Database and integrated tools to improve annotation of the bovine genome and to integrate the genome sequence with other genomics data.

Proper citation: Bovine Genome Database (RRID:SCR_000148) Copy   

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http://www.worm.mpi-cbg.de/phenobank/cgi-bin/ProjectInfoPage.py

A database that provides primary data from two high-content screens that profile the set of ~900 essential C. elegans genes (~5% of the genome) required for embryo production and/or events during the first two embryonic divisions. Phenobank houses the movies, scored defects, and phenotypic classification data for the embryo-filming and gonad morphology screens.

Proper citation: PhenoBank (RRID:SCR_000930) Copy   

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https://fungi.ensembl.org/Neurospora_crassa/Info/Index

It's strategy involves Whole Genome Shotgun (WGS) sequencing, in which sequence from the entire genome is generated and reassembled. This method is standard for microbial genome sequencing, and has been successfully applied to Drosophila. Neurospora is an ideal candidate for this approach because of the low repeat content of the genome. Neurospora crassa Database has expanded the scope of its database by including a mitochondrial annotation, incorporating information from the Neurospora compendium, and assigning NCU numbers to tRNA and rRNAs. They have improved the annotation process to predict untranslated regions and to reduce the number of spurious predictions. As a result, version 3 contains 9,826 genes, 794 fewer than version 2. During the initial phase of a WGS project they sequence both ends of the 4 kb inserts from a plasmid library prepared using randomly sheared and sized-selected DNA. The shotgun reads are assembled by recognizing overlapping regions of sequence and making use of the knowledge of the orientation and distance of the paired reads from each plasmid. Obtaining deep sequence coverage though high levels of sequence redundancy assures that the majority of the genome is represented in the initial assembly and that the consensus sequence is of high quality. Their approach toward the initial assembly was conservative, meaning they would rather fail to join sequence contigs that might overlap each other than risk making false joins between two closely related but non-overlapping genomic regions. Hence, the initial assembly contains many sequence contigs and over time these contigs will increase in size and decrease in number as they are joined together. After shotgun sequencing and assembly there was a second phase of sequencing in which additional sequence was obtained from specific regions that were missing from the original assembly or are recognized to be of low quality in the consensus. The Neurospora crassa sequencing project reflects a close collaboration between the Broad Institute and the Neurospora research community. Principal investigators include Bruce Birren and Chad Nusbaum from the Broad Institute, Matt Sachs at the Oregon Graduate Institute of Science and Technology, Chuck Staben at the University of Kentucky and Jak Kinsey at the Fungal Genetics Stock Center at the University of Kansas Medical Center. In addition, we have a larger Advisory Board made up of a number of Neurospora researchers. Sponsors: They have been funded by the National Science Foundation to sequence the N. crassa genome and make the information publicly available.

Proper citation: Neurospora crassa Database (RRID:SCR_001372) Copy   

•   Source: SciCrunch Registry

http://igenbio.com/

A web-based genome analysis platform that integrates proprietary functional genomic data, metabolic reconstructions, expression profiling, and biochemical and microbiological data with publicly available information. Focused on microbial genomics, it provides better and faster identification of gene function across all organisms. Building upon a comprehensive genomic database integrated with a collection of microbial metabolic and non-metabolic pathways and using proprietary algorithms, it assigns functions to genes, integrates genes into pathways, and identifies previously unknown or mischaracterized genes, cryptic pathways and gene products. . * Automated and manual annotation of genes and genomes * Analysis of metabolic and non-metabolic pathways to understand organism physiology * Comparison of multiple genomes to identify shared and unique features and SNPs * Functional analysis of gene expression microarray data * Data-mining for target gene discovery * In silico metabolic engineering and strain improvement

Proper citation: ERGO (RRID:SCR_001243) Copy   

•   Source: SciCrunch Registry

https://chordate.bpni.bio.keio.ac.jp/chordate/faba/1.4/top.html

Image resource including ascidian's three-dimensional (3D) and cross-sectional images through the developmental time course. These images were reconstructed from more than 3,000 high-resolution real images collected by confocal laser scanning microscopy (CLSM) at newly defined 26 distinct developmental stages (stages 1-26) from fertilized egg to hatching larva, which were grouped into six periods named the zygote, cleavage, gastrula, neurula, tailbud, and larva periods. The data set will be helpful in standardizing developmental stages for morphology comparison as well as for providing guidelines for several functional studies of a body plan in chordate.

Proper citation: Four-dimensional Ascidian Body Atlas (RRID:SCR_001691) Copy   

•   Source: SciCrunch Registry