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http://h-invitational.jp/varygene/

It consists of a Genome Browser, an LD Search System, and the VaryGene 2 system. The Generic Genome Browser is a combination of database and interactive Web page for manipulating and displaying annotations on genomes, while LDSearchSystem is a search system for linkage disequilibrium (LD) bins. VaryGene 2 is a system to search, display, and download our research results on human polymorphism based on publicly available data and annotations of transcripts presented by H-InvDB. VaryGene 2 provides information about single nucleotide polymorphisms (SNPs), deletion-insertion polymorphisms (DIPs), short tandem repeats (STRs), single amino acid repeats (SARs), structural variation (or copy number variations: CNVs), and their relations to the genome, transcripts, and functional domains. Users can search by polymorphisms, transcripts, STRs/SARs, and CNVs.

Proper citation: VarySysDB (RRID:SCR_005880) Copy   

•   Source: SciCrunch Registry

http://gst.ornl.gov/

We are the Computational Biology and Bioinformatics Group of the Biosciences Division of Oak Ridge National Laboratory. We conduct genetics research and system development in genomic sequencing, computational genome analysis, and computational protein structure analysis. We provide bioinformatics and analytic services and resources to collaborators, predict prospective gene and protein models for analysis, provide user services for the general community, including computer-annotated genomes in Genome Channel. Our collaborators include the Joint Genome Institute, ORNL''s Computer Science and Mathematics Division, the Tennessee Mouse Genome Consortium, the Joint Institute for Biological Sciences, and ORNL''s Genome Science and Technology Graduate Program.

Proper citation: Computational Biology at ORNL (RRID:SCR_005710) Copy   

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http://www.compbio.dundee.ac.uk/gotcha/gotcha.php

GOtcha provides a prediction of a set of GO terms that can be associated with a given query sequence. Each term is scored independently and the scores calibrated against reference searches to give an accurate percentage likelihood of correctness. These results can be displayed graphically. Why is GOtcha different to what is already out there and why should you be using it? * GOtcha uses a method where it combines information from many search hits, up to and including E-values that are normally discarded. This gives much better sensitivity than other methods. * GOtcha provides a score for each individual term, not just the leaf term or branch. This allows the discrimination between confident assignments that one would find at a more general level and the more specific terms that one would have lower confidence in. * The scores GOtcha provides are calibrated to give a real estimate of correctness. This is expressed as a percentage, giving a result that non-experts are comfortable in interpreting. * GOtcha provides graphical output that gives an overview of the confidence in, or potential alternatives for, particular GO term assignments. The tool is currently web-based; contact David Martin for details of the standalone version. Platform: Online tool

Proper citation: GOtcha (RRID:SCR_005790) Copy   

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http://www.knockoutmouse.org/

Database of the international consortium working together to mutate all protein-coding genes in the mouse using a combination of gene trapping and gene targeting in C57BL/6 mouse embryonic stem (ES) cells. Detailed information on targeted genes is available. The IKMC includes the following programs: * Knockout Mouse Project (KOMP) (USA) ** CSD, a collaborative team at the Children''''s Hospital Oakland Research Institute (CHORI), the Wellcome Trust Sanger Institute and the University of California at Davis School of Veterinary Medicine , led by Pieter deJong, Ph.D., CHORI, along with K. C. Kent Lloyd, D.V.M., Ph.D., UC Davis; and Allan Bradley, Ph.D. FRS, and William Skarnes, Ph.D., at the Wellcome Trust Sanger Institute. ** Regeneron, a team at the VelociGene division of Regeneron Pharmaceuticals, Inc., led by David Valenzuela, Ph.D. and George D. Yancopoulos, M.D., Ph.D. * European Conditional Mouse Mutagenesis Program (EUCOMM) (Europe) * North American Conditional Mouse Mutagenesis Project (NorCOMM) (Canada) * Texas A&M Institute for Genomic Medicine (TIGM) (USA) Products (vectors, mice, ES cell lines) may be ordered from the above programs.

Proper citation: International Knockout Mouse Consortium (RRID:SCR_005574) Copy   

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http://ygac.med.yale.edu

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 15, 2013. TRIPLES provides full public access to the data and reagents generated from ongoing functional analysis of the yeast genome. Using a novel transposon-tagging approach, we have analyzed disruption phenotypes, gene expression, and protein localization on a genome-wide scale in Saccharomyces. The data generated from this study may be accessed through our database, TRIPLES ; additionally, all reagents generated in this study are freely available from on-line order forms (linked to TRIPLES as well). multipurpose, mini-transposon, mutant alleles, phenotypes, protein localization, gene expression, Saccharomyces cerevisiae, Web-accessible database, transposon-mutagenized yeast strains, downloaded, tab-delimited, text file, protein localization data, fluorescent micrographs, staining patterns, indirect immunofluorescence analysis of indicated epitope-tagged proteins, subcellular localization of the yeast proteome, visual library, Nucleic Acid Sequence Data Library (GenBank), clone report, graphic map, transposon insertions (represented as flags)

Proper citation: TRIPLES- a database of TRansposon-Insertion Phenotypes Localization and Expression in Saccharomyces (RRID:SCR_005714) Copy   

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http://transpogene.tau.ac.il/

A publicly available database of Transposed elements (TEs) which are located within protein-coding genes of 7 organisms: human, mouse, chicken, zebrafish, fruilt fly, nematode and sea squirt. Using TranspoGene the user can learn about the many aspects of the effect these TEs have on their hosting genes, such as: exonization events (including alternative splicing-related data), insertion of TEs into introns, exons, and promoters, specific location of the TE over the gene, evolutionary divergence of the TE from its consensus sequence and involvement in diseases. TranspoGene database is quickly searchable through its website, enables many kinds of searches and is available for download. TranspoGene contains information regarding specific type and family of the TEs, genomic and mRNA location, sequence, supporting transcript accession and alignment to the TE consensus sequence. The database also contains host gene specific data: gene name, genomic location, Swiss-Prot and RefSeq accessions, diseases associated with the gene and splicing pattern. The TranspoGene and microTranspoGene databases can be used by researchers interested in the effect of TE insertion on the eukaryotic transcriptome.

Proper citation: TranspoGene (RRID:SCR_005634) Copy   

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http://tabit.ucsd.edu/sdec/

A next-generation web-based application that aims to provide an integrated solution for both visualization and analysis of deep-sequencing data, along with simple access to public datasets.

Proper citation: Systems Transcriptional Activity Reconstruction (RRID:SCR_005622) Copy   

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https://github.com/HajkD/LTRpred

Software package for automated functional annotation of LTR retrotransposons for comparative genomics studies. Used to perform de novo functional annotation of LTR retrotransposons from any genome assembly in fasta format.

Proper citation: LTRpred (RRID:SCR_017031) Copy   

•   Source: SciCrunch Registry

https://blobtoolkit.genomehubs.org/blobtools2/

Software suite for identifying and isolating non-target data in draft and publicly available genome assemblies. Used to process assembly, read and analysis files for fully reproducible interactive exploration in browser-based Viewer. Used for interactive quality assessment of genome assemblies .BlobTools2 is reimplementation of BlobTools, written in Python 3 with fully modular design to make creating new datasets and adding additional analysis types easier.

Proper citation: BlobTools2 (RRID:SCR_023351) Copy   

•   Source: SciCrunch Registry

http://crispor.tefor.net

Web application that helps design, evaluate and clone guide sequences for the CRISPR/Cas9 system. This sgRNA design tool assists with guide selection in a variety of genomes and pre-calculated results for all human coding exons as a UCSC Genome Browser track.

Proper citation: CRISPOR (RRID:SCR_015935) Copy   

•   Source: SciCrunch Registry

http://cell-innovation.nig.ac.jp/maser/AllPipelines/P000001138_en.html

Software pipeline that visualizes mapping results (in BAM format) on Genome Explorer.

Proper citation: loadBAM2ge_db (RRID:SCR_015951) 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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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

http://www.cbs.dtu.dk/services/gwBrowser/

An interactive web application for visualizing genomic data of sequenced prokaryotic chromosomes. It allows users to carry out various analyses such as mapping alignments of homologous genes to other genomes, mapping of short sequencing reads to a reference chromosome, and calculating DNA properties such as curvature or stacking energy along the chromosome. The GeneWiz browser produces an interactive graphic that enables zooming from a global scale down to single nucleotides, without changing the size of the plot. Its ability to disproportionally zoom provides optimal readability and increased functionality compared to other browsers. The tool allows the user to select the display of various genomic features, color setting and data ranges. Custom numerical data can be added to the plot allowing, for example, visualization of gene expression and regulation data. Further, standard atlases are pre-generated for all prokaryotic genomes available in GenBank, providing a fast overview of all available genomes, including recently deposited genome sequences.

Proper citation: GeneWiz browser (RRID:SCR_001454) Copy   

•   Source: SciCrunch Registry

https://gtexportal.org/home/

Database and browser that provides a central resource to archive and display association between genetic variation and high-throughput molecular-level phenotypes. This effort originated with the NIH GTEx roadmap project: however the scope of this resource will be extended to include any available genotype/molecular phenotype datasets.

Proper citation: GTEx eQTL Browser (RRID:SCR_001618) Copy   

•   Source: SciCrunch Registry