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http://gbrowse.org/

A database and interactive web site for manipulating and displaying annotations on genomes. Features include: detailed views of the genome; use of a variety of premade or personally made glyphs ; customizable order and appearance of tracks by administrators and end-users; search by annotation ID, name, or comment; support of third party annotation using GFF formats; DNA and GFF dumps; connectivity to different databases, including BioSQL and Chado; and a customizable plug-in architecture (e.g. run BLAST, find oligonucleotides, design primers, etc.). GBrowse is distributed as source code for Macintosh OS X, UNIX and Linux platforms, and as pre-packaged binaries for Windows machines. It can be installed using the standard Perl module build procedure, or automated using a network-based install script. In order to use the net installer, you will need to have Perl 5.8.6 or higher and the Apache web server installed. The wiki portion accepts data submissions.

Proper citation: GBrowse (RRID:SCR_006829) Copy   

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http://owlsim.org

Software package that provides the ability to do a number of standard semantic similarity methods and includes novel methods for combining these with dynamic selection of anonymous grouping classes. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: OwlSim (RRID:SCR_006819) Copy   

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http://tripod.nih.gov/npc/

The NCGC Pharmaceutical Collection (NPC) is a comprehensive, publically-accessible collection of approved and investigational drugs for high-throughput screening that provides a valuable resource for both validating new models of disease and better understanding the molecular basis of disease pathology and intervention. The NPC has already generated several useful probes for studying a diverse cross section of biology, including novel targets and pathways. NCGC provides access to its set of approved drugs and bioactives through the Therapeutics for Rare and Neglected Diseases (TRND) program and as part of the compound collection for the Tox21 initiative, a collaborative effort for toxicity screening among several government agencies including the US Environmental Protection Agency (EPA), the National Toxicology Program (NTP), the US Food and Drugs Administration (FDA), and the NCGC. Of the nearly 2750 small molecular entities (MEs) that have been approved for clinical use by US (FDA), EU (EMA), Japanese (NHI), and Canadian (HC) authorities and that are amenable to HTS screening, we currently possess 2,400 as part of our screening collection. The NPC resource currently consists of (i) the physical collection suitable for high throughput screening (HTS) and (ii) the informatics browser and database. Putting together the physical collection has been surprisingly challenging in terms of the time and effort required in the informatics, compound management and synthetic chemistry related activities required for this endeavor. We provide access to the NPC screening library through collaboration. Please contact our Scientific Director Dr. Chris Austin for additional information. The other half of the NPC resource is the NPC browser. This is a self-contained software that is actively developed and maintained by the informatics group to provide electronic access to the NPC content. The latest version of the NPC browser for various platforms can be downloaded.

Proper citation: NCGC Pharmaceutical Collection (RRID:SCR_006909) Copy   

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http://lincsportal.ccs.miami.edu/dcic-portal/

Portal which provides a unified interface for searching LINCS dataset packages and reagents. Users can use the portal to access datasets, small molecules, cells, genes, proteins and peptides, and antibodies.

Proper citation: LINCS Data Portal (RRID:SCR_014939) Copy   

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https://github.com/kstreet13/slingshot

Software R package for identifying and characterizing continuous developmental trajectories in single cell data. Cell lineage and pseudotime inference for single-cell transcriptomics.

Proper citation: Slingshot (RRID:SCR_017012) Copy   

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http://avis.princeton.edu/pixie/index.php

bioPIXIE is a general system for discovery of biological networks through integration of diverse genome-wide functional data. This novel system for biological data integration and visualization, allows you to discover interaction networks and pathways in which your gene(s) (e.g. BNI1, YFL039C) of interest participate. The system is based on a Bayesian algorithm for identification of biological networks based on integrated diverse genomic data. To start using bioPIXIE, enter your genes of interest into the search box. You can use ORF names or aliases. If you enter multiple genes, they can be separated by commas or returns. Press ''submit''. bioPIXIE uses a probabilistic Bayesian algorithm to identify genes that are most likely to be in the same pathway/functional neighborhood as your genes of interest. It then displays biological network for the resulting genes as a graph. The nodes in the graph are genes (clicking on each node will bring up SGD page for that gene) and edges are interactions (clicking on each edge will show evidence used to predict this interaction). Most likely, the first results to load on the results page will be a list of significant Gene Ontology terms. This list is calculated for the genes in the biological network created by the bioPIXIE algorithm. If a gene ontology term appears on this list with a low p-value, it is statistically significantly overrepresented in this biological network. As you move the mouse over genes in the network, interactions involving these genes are highlighted. If you click on any of the highlighted interactions graph, evidence pop-up window will appear. The Evidence pop-up lists all evidence for this interaction, with links to the papers that produced this evidence - clicking these links will bring up the relevant source citation(s) in PubMed. You may need to download the Adobe Scalable Vector Graphic (SVG) plugin to utilize the visualization tool (you will be prompted if you need it).

Proper citation: bioPIXIE (RRID:SCR_004182) Copy   

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http://life.ccs.miami.edu/life/

LIFE search engine contains data generated from LINCS Pilot Phase, to integrate LINCS content leveraging semantic knowledge model and common LINCS metadata standards. LIFE makes LINCS content discoverable and includes aggregate results linked to Harvard Medical School and Broad Institute and other LINCS centers, who provide more information including experimental conditions and raw data. Please visit LINCS Data Portal.

Proper citation: LINCS Information Framework (RRID:SCR_003937) Copy   

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http://www.hgsc.bcm.tmc.edu/content/hapmap-3-and-encode-3

Draft release 3 for genome-wide SNP genotyping and targeted sequencing in DNA samples from a variety of human populations (sometimes referred to as the HapMap 3 samples). This release contains the following data: * SNP genotype data generated from 1184 samples, collected using two platforms: the Illumina Human1M (by the Wellcome Trust Sanger Institute) and the Affymetrix SNP 6.0 (by the Broad Institute). Data from the two platforms have been merged for this release. * PCR-based resequencing data (by Baylor College of Medicine Human Genome Sequencing Center) across ten 100-kb regions (collectively referred to as ENCODE 3) in 712 samples. Since this is a draft release, please check this site regularly for updates and new releases. The HapMap 3 sample collection comprises 1,301 samples (including the original 270 samples used in Phase I and II of the International HapMap Project) from 11 populations, listed below alphabetically by their 3-letter labels. Five of the ten ENCODE 3 regions overlap with the HapMap-ENCODE regions; the other five are regions selected at random from the ENCODE target regions (excluding the 10 HapMap-ENCODE regions). All ENCODE 3 regions are 100-kb in size, and are centered within each respective ENCODE region. The HapMap 3 and ENCORE 3 data are downloadable from the ftp site.

Proper citation: HapMap 3 and ENCODE 3 (RRID:SCR_004563) Copy   

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http://www.yeastgenome.org/

A curated database that provides comprehensive integrated biological information for Saccharomyces cerevisiae along with search and analysis tools to explore these data. SGD allows researchers to discover functional relationships between sequence and gene products in fungi and higher organisms. The SGD also maintains the S. cerevisiae Gene Name Registry, a complete list of all gene names used in S. cerevisiae which includes a set of general guidelines to gene naming. Protein Page provides basic protein information calculated from the predicted sequence and contains links to a variety of secondary structure and tertiary structure resources. Yeast Biochemical Pathways allows users to view and search for biochemical reactions and pathways that occur in S. cerevisiae as well as map expression data onto the biochemical pathways. Literature citations are provided where available.

Proper citation: SGD (RRID:SCR_004694) Copy   

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https://reich.hms.harvard.edu/software

Software application that finds skews in ancestry that are potentially associated with disease genes in recently mixed populations like African Americans. It can be downloaded for either UNIX or Linux.

Proper citation: Ancestrymap (RRID:SCR_004353) 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://worfdb.dfci.harvard.edu/

Database that integrates and disseminates the data from the cloning of complete set of predicted protein-encoding ORFs of Caenorhabditis elegans. It also allows the community to search for availability and quality of cloned ORFs. So far, ORF sequence tags (OSTs) obtained for all individual clones have allowed exon structure corrections for ORFs originally predicted by the C. elegans sequencing consortium. The database contains this OST information along with data pertinent to the cloning process.

Proper citation: WorfDB (RRID:SCR_006028) Copy   

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https://pathvisio.org/

Software visualization tool for biological pathways. Pathway analysis and drawing software which allows drawing, editing, and analyzing biological pathways. Developed in Java and can be extended with plugins.

Proper citation: PathVisio (RRID:SCR_023789) Copy   

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http://mousesnp.roche.com/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 17, 2013. This website contains a database of the mouse SNP. DNA sequencing was performed along with genotyping. There is information on genotyping, mouse strain, and haplotype map.

Proper citation: Mouse Single Nucleotide Polymorphism Database (RRID:SCR_000033) Copy   

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http://www.uniprot.org/

Collection of data of protein sequence and functional information. Resource for protein sequence and annotation data. Consortium for preservation of the UniProt databases: UniProt Knowledgebase (UniProtKB), UniProt Reference Clusters (UniRef), and UniProt Archive (UniParc), UniProt Proteomes. Collaboration between European Bioinformatics Institute (EMBL-EBI), SIB Swiss Institute of Bioinformatics and Protein Information Resource. Swiss-Prot is a curated subset of UniProtKB.

Proper citation: UniProt (RRID:SCR_002380) Copy   

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http://www.t-profiler.org

One of the key challenges in the analysis of gene expression data is how to relate the expression level of individual genes to the underlying transcriptional programs and cellular state. The T-profiler tool hosted on this website uses the t-test to score changes in the average activity of pre-defined groups of genes. The gene groups are defined based on Gene Ontology categorization, ChIP-chip experiments, upstream matches to a consensus transcription factor binding motif, and location on the same chromosome, respectively. If desired, an iterative procedure can be used to select a single, optimal representative from sets of overlapping gene groups. A jack-knife procedure is used to make calculations more robust against outliers. T-profiler makes it possible to interpret microarray data in a way that is both intuitive and statistically rigorous, without the need to combine experiments or choose parameters. Currently, gene expression data from Saccharomyces cerevisiae and Candida albicans are supported. Users can submit their microarray data for analysis by clicking on one of the two organism-specific tabs above. Platform: Online tool

Proper citation: T-profiler (RRID:SCR_003452) Copy   

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https://github.com/shendurelab/LACHESIS

Software tool for chromosome scale scaffolding of de novo genome assemblies based on chromatin interactions.Method exploits signal of genomic proximity in Hi-C datasets for ultra long range scaffolding of de novo genome assemblies.

Proper citation: LACHESIS (RRID:SCR_017644) Copy   

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https://github.com/Kingsford-Group/kourami

Software graph guided assembly for novel human leukocyte antigen allele discovery. Graph guided assembly for HLA haplotypes covering typing exons using high coverage whole genome sequencing data.Implemented in Java and supported on Linux and Mac OS X.

Proper citation: Kourami (RRID:SCR_022280) Copy   

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https://gillisweb.cshl.edu/Primate_MTG_coexp/

We aligned single-nucleus atlases of middle temporal gyrus (MTG) of 5 primates (human, chimp, gorilla, macaque and marmoset) and identified 57 consensus cell types common to all species. We provide this resource for users to: 1) explore conservation of gene expression across primates at single cell resolution; 2) compare with conservation of gene coexpression across metazoa, and 3) identify genes with changes in expression or connectivity that drive rapid evolution of human brain.

Proper citation: Gene functional conservation across cell types and species (RRID:SCR_023292) Copy   

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http://www.fruitfly.org

Database on the sequence of the euchromatic genome of Drosophila melanogaster In addition to genomic sequencing, the BDGP is 1) producing gene disruptions using P element-mediated mutagenesis on a scale unprecedented in metazoans; 2) characterizing the sequence and expression of cDNAs; and 3) developing informatics tools that support the experimental process, identify features of DNA sequence, and allow us to present up-to-date information about the annotated sequence to the research community. Resources * Universal Proteomics Resource: Search for clones for expression and tissue culture * Materials: Request genomic or cDNA clones, library filters or fly stocks * Download Sequence data sets and annotations in fasta or xml format by http or ftp * Publications: Browse or download BDGP papers * Methods: BDGP laboratory protocols and vector maps * Analysis Tools: Search sequences for CRMs, promoters, splice sites, and gene predictions * Apollo: Genome annotation viewer and editor September 15, 2009 Illumina RNA-Seq data from 30 developmental time points of D. melanogaster has been submitted to the Short Read Archive at NCBI as part of the modENCODE project. The data set currently contains 2.2 billion single-end and paired reads and over 201 billion base pairs.

Proper citation: Berkeley Drosophila Genome Project (RRID:SCR_013094) Copy   

•   Source: SciCrunch Registry