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http://www.hdbr.org/

Collection of human embryonic and fetal material (Tissue and RNA) ranging from 3 to 20 weeks of development available to the international scientific community. Material can either be sent to registered users or our In House Gene Expression Service (IHGES) can carry out projects on user''''s behalf, providing high quality images and interpretation of gene expression patterns. Gene expression data emerging from HDBR material is added to our gene expression database which is accessible via our HUDSEN (Human Developmental Studies Network) website. A significant proportion of the material has been cytogenetically karyotyped, and normal karyotyped material is provided for research.

Proper citation: Human Developmental Biology Resource (RRID:SCR_006326) Copy   

•   Source: SciCrunch Registry

http://www.ppmi-info.org/

An observational longitudinal clinical study partnership to identify and validate biomarkers of Parkinson disease (PD) progression and provide easy and open web-based access to the comprehensive set of correlated clinical data and biospecimens, information, and biosamples acquired from PD and age and gender matched healthy control subjects to the research community. The data and specimens have been collected in a standardized manner under strict protocols and includes clinical (demographic, motor and non-motor, cognitive and neurobehavioral), imaging (raw and processed MRI, SPECT and DAT), and blood chemistry and hematology subject assessments and biospecimen inventories (serum, plasma, whole blood, CSF, DNA, RNA and urine). All data are de-identified to protect patient privacy. PPMI will be carried out over five years at 21 clinical sites in the United States and Europe and requires the participation of 400 Parkinson's patients and 200 control participants. The PPMI database provides researchers with access to correlated clinical and imaging data, along with annotated biospecimens, all available within an open access system that encourages data sharing (http://www.ppmi-info.org/access-data-specimens/). The website hosts an Ongoing Analysis section to keep the scientific community apprised of analyses being completed, in hopes of stimulating collaborations between researchers who are using PPMI data and specimens.

Proper citation: Parkinson's Progression Markers Initiative (RRID:SCR_006431) Copy   

•   Source: SciCrunch Registry

http://zork.wustl.edu/niaaa/

A multi-site, multi-disciplinary undertaking with the overall goals of characterizing the familial transmission of alcoholism and related phenotypes and identifying susceptibility genes using genetic linkage. The study is being coordinated by the SUNY Health Science Center at Brooklyn (HSCB) under the leadership of Henri Begleiter. The study was initially funded by the National Institute of Alcohol Abuse and Alcoholism (NIAAA) in 1989. Additional useful information at http://www.niaaa.nih.gov/ResearchInformation/ExtramuralResearch/SharedResources/projcoga.htm

Proper citation: Collaborative Study on the Genetics of Alcoholism (RRID:SCR_013395) Copy   

•   Source: SciCrunch Registry

http://www.funrich.org/

A software tool used for functional enrichment and interaction network analysis of genes and proteins. Users can search against a default background database or load customized database. The results can be depicted as venn, bar, column, pie and doughnut charts.

Proper citation: FunRich: Functional Enrichment analysis tool (RRID:SCR_014467) Copy   

•   Source: SciCrunch Registry

https://mendel.imp.ac.at/gpi/gpi_server.html

Prediction tool locating potential GPI-modification sites in precursor sequences applied for large-scale protein sequence database searches. The composite prediction function (with separate parametrization for metazoan and protozoan proteins) consists of terms evaluating both amino acid type preferences at sequence positions near a supposed omega-site as well as the concordance with general physical properties encoded in multi-residue correlation within the motif sequence. The latter terms are especially successful in rejecting non-appropriate sequences from consideration. The algorithm has been validated with a self-consistency and two jack-knife tests for the learning set of fully annotated sequences from the SWISS-PROT database as well as with a newly created database big-Pi (more than 300 GPI-motif mutations extracted from original literature sources). The accuracy of predicting the effect of mutations in the GPI sequence motif was above 83 %.

Proper citation: big-PI Predictor (RRID:SCR_001599) Copy   

•   Source: SciCrunch Registry

http://babelomics.bioinfo.cipf.es

An integrative platform for the analysis of transcriptomics, proteomics and genomic data with advanced functional profiling. Version 4 of Babelomics integrates primary (normalization, calls, etc.) and secondary (signatures, predictors, associations, TDTs, clustering, etc.) analysis tools within an environment that allows relating genomic data and/or interpreting them by means of different functional enrichment or gene set methods. Such interpretation is made not only using functional definitions (GO, KEGG, Biocarta, etc.) but also regulatory information (from Transfac, Jaspar, etc.) and other levels of regulation such as miRNA-mediated interference, protein-protein interactions, text-mining module definitions and the possibility of producing de novo annotations through the Blast2GO system . Babelomics has been extensively re-engineered and now it includes the use of web services and Web 2.0 technology features, a new user interface with persistent sessions and a new extended database of gene identifiers. In this release GEPAS and Babelomics have integrated into a unique web application with many new features and improvements: * Data input: import and quality control for the most common microarray formats * Normalization and base calling: for the most common expression, tiling and SNP microarrays (Affymetrix and Agilent). * Transcriptomics: diverse analysis options that include well established as well as novel algorithms for normalization, gene selection, class prediction, clustering and time-series analysis. * Genotyping: stratification analysis, association, TDT. * Functional profiling: functional enrichment and gene set enrichment analysis with functional terms (GO, KEGG, Biocarta, etc.), regulatory (Transfac, Jaspar, miRNAs, etc.), text-mining, derived bioentities, protein-protein interaction analysis. * Integrative analysis: Different variables can be related to each other (e.g. gene expression to gnomic copy number) and the results subjected to functional analysis. Platform: Online tool

Proper citation: Babelomics (RRID:SCR_002969) Copy   

•   Source: SciCrunch Registry

http://diyhpl.us/~bryan/irc/protocol-online/protocol-cache/TFSEARCH.html

The TFSEARCH searches highly correlated sequence fragments against TFMATRIX transcription factor binding site profile database in the "TRANSFAC" databases developed at GBF-Braunschweig, Germany. The TFSEARCH program was written by Yutaka Akiyama (Kyoto University, currently at RWCP) in 1995.

Proper citation: TFSEARCH: Searching Transcription Factor Binding Sites (RRID:SCR_004262) Copy   

•   Source: SciCrunch Registry

http://www.hgsc.bcm.tmc.edu/

Center for high-throughput DNA sequence generation and the accompanying analysis. The sequence data generated by the center's machines are analyzed in a complex bioinformatics pipeline, and the data are deposited regularly in the public databases at the National Center for Biotechnology Information (NCBI).

Proper citation: Baylor College of Medicine Human Genome Sequencing Center (RRID:SCR_013605) Copy   

•   Source: SciCrunch Registry

http://gnomad.broadinstitute.org/

Database that aggregates exome and genome sequencing data from large-scale sequencing projects. The gnomAD data set contains individuals sequenced using multiple exome capture methods and sequencing chemistries. Raw data from the projects have been reprocessed through the same pipeline, and jointly variant-called to increase consistency across projects.

Proper citation: Genome Aggregation Database (RRID:SCR_014964) Copy   

•   Source: SciCrunch Registry

http://www.phosphonet.ca/

PhosphoNET is an open-access, online knowledgebase developed by Kinexus Bioinformatics Corporation to foster the study of cell signaling systems to advance biomedical research in academia and industry. PhosphoNET is the world''s largest repository of known and predicted information on human phosphorylation sites, their evolutionary conservation and the identities of protein kinases that may target these sites. Search by protein name, UniProt number, IPI number, or 15 AA P-site sequence. PhosphoNET presently holds data on over 650,000 known and putative phosphorylation sites (P-sites) in over 23,000 human proteins that have been collected from the scientific literature and other reputable websites. Over 14% of these phospho-sites have been experimentally validated. The rest have been predicted with a novel P-Site Predictor algorithm developed at Kinexus with academic partners at the University of British Columbia and Simon Fraser University. With the PhosphoNET Evolution module, this website also provides information about cognate proteins in over 20 other species that may share these human phospho-sites. This helps to define the most functionally important phospho-sites as these are expected to be highly conserved in nature. With the Kinase Predictor module, listings are provided for the top 50 human protein kinases that are likely to phosphorylate each of these phospho-sites using another proprietary kinase substrate prediction algorithm developed at Kinexus. Our kinase substrate predictions are based on deduced consensus phosphorylation site amino acid frequency scoring matrices that we have determined for each of ~500 different human protein kinases. The specificity matrices are generated directly from the primary amino acid sequences of the catalytic domains of these kinases, and when available, have proven to correlate strongly with substrate prediction matrices based on alignment of known substrates of these kinases. The higher the score, the better the prospect that a kinase will phosphorylate a given site. Over 30 million kinase-substrate phospho-site pairs are quantified in PhosphoNET. Kinexus Bioinformatics Corporation has the capability to test most of these putative interactions in vitro for our clients.

Proper citation: PhosphoNET (RRID:SCR_013070) Copy   

•   Source: SciCrunch Registry

http://www.regulomedb.org/

Database that annotates SNPs with known and predicted regulatory elements in intergenic regions of H. sapiens genome. Known and predicted regulatory DNA elements include regions of DNAase hypersensitivity, binding sites of transcription factors, and promoter regions that have been biochemically characterized to regulation transcription. Source of these data include public datasets from GEO, ENCODE project, and published literature.

Proper citation: RegulomeDB (RRID:SCR_017905) Copy   

•   Source: SciCrunch Registry