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http://ndct.nimh.nih.gov

A database which houses human subjects clinical trial data. NDCT currently contains data on 13,409 subjects and has access to data on 100,500 subjects from the NIMH Data Archive. Users can also sign up for news updates and watch video tutorials.

Proper citation: National Database for Clinical Trials related to Mental Illness (RRID:SCR_013795) Copy   

•   Source: SciCrunch Registry

https://www.rebuildingakidney.org

A consortium of research projects working to optimize approaches for the isolation, expansion, and differentiation of appropriate kidney cell types and their integration into complex structures that replicate human kidney function. Their goal is to coordinate and integrate research to support the development and implementation of strategies such as de novo repair of nephrons, the re-generation of nephrons, and the in vitro engineering of a biological kidney to enhance renal repair and promote the generation of new nephrons in the postnatal organ. Investigators may apply for funding of a kidney-related project through the RBK Partnership Project. Funded projects would join the consortium.

Proper citation: ReBuilding a Kidney (RRID:SCR_014442) Copy   

•   Source: SciCrunch Registry

http://www.brainsimagebank.ac.uk

A searchable collection of anonymised images and associated clinical data. It includes normal individuals at all ages (from prenatal to old age). The image bank contains integrated data sets already collected as part of research studies which include control subjects. New data is added as they become available.

Proper citation: BRAINS Imagebank (RRID:SCR_014576) Copy   

•   Source: SciCrunch Registry

https://github.com/zuoxinian/CCS

Software tool for multimodal human brain imaging data analysis. Computational pipeline for discovery science of human brain connectomes at macroscale with multimodal magnetic resonance imaging technologies.

Proper citation: Connectome Computation System (RRID:SCR_017342) Copy   

•   Source: SciCrunch Registry

http://www.suiplus.com

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on December 1, 2023. System developed under guidance of experts in reproduction and andrology from Andrological Branch of Chinese Medical Association and Research Institute of National Health Planning Commission. Designed according to standard of 5th edition of World Health Organization laboratory manual for examination and processing of human semen.

Proper citation: Suiplus: SSA-II sperm analysis system (RRID:SCR_017387) Copy   

•   Source: SciCrunch Registry

http://www.informatics.jax.org/homology.shtml

MGI contains homology information for mouse, human, rat, chimp, dog and other species. Complete set of human, chimpanzee, rhesus macaque, dog, cattle, rat, chicken, western clawed frog and zebrafish Homology Classes for mouse genes. Report includes Chromosome and EntrezGene and OMIM IDs. Report of Human and Mouse Homology Classes sorted by HomoloGene ID includes associated nucleotide and protein sequences, Chromosome and OMIM IDs. Report of Human and Mouse Homology with phenotype annotations. Several additional MGI reports are available, including those for Gene Ontology, Phenotypes and Nomenclature.

Proper citation: Vertebrate Homology (RRID:SCR_017517) Copy   

•   Source: SciCrunch Registry

https://anvilproject.org/

Portal to facilitate integration and computing on and across large datasets generated by NHGRI programs, as well as initiatives funded by National Institutes of Health or by other agencies that support human genomics research. Resource for genomic scientific community, that leverages cloud based infrastructure for democratizing genomic data access, sharing and computing across large genomic, and genomic related data sets. Component of federated data ecosystem, and is expected to collaborate and integrate with other genomic data resources through adoption of FAIR (Findable, Accessible, Interoperable, Reusable) principles, as their specifications emerge from scientific community. Will provide collaborative environment, where datasets and analysis workflows can be shared within consortium and be prepared for public release to broad scientific community through AnVIL user interfaces.

Proper citation: Analysis, Visualization, and Informatics Lab-space (AnVIL) (RRID:SCR_017469) Copy   

•   Source: SciCrunch Registry

https://portal.brain-map.org/atlases-and-data/rnaseq

Software tool to visualize and analyze transcriptomics data and transcriptomic cell types for mouse and human, all directly in web browser. To explore gene expression heatmap across cell types in datasets, search for genes of interest, explore tSNE visualization, colored by cell types or expression of genes of interest, visualize dataset’s sampling strategy to see how cells and nuclei were sampled across brain areas, cortical layer, and other dimensions, find cell type of interest in one visualization and see its characteristics in different visualization.Used for Allen Brain Map Cell Types Database to Browse Data: Human - Multiple Cortical Areas, and Mouse - Cortex and Hippocampus.

Proper citation: Transcriptomics Explorer (RRID:SCR_017567) Copy   

•   Source: SciCrunch Registry

https://kg.ebrains.eu/

Metadata management system built for EBRAINS. Multi modal metadata store which brings together information from different areas of Human Brain Project as well as from external partners. Graph database tracks linkage between experimental data and neuroscientific data science supporting more extensive data reuse and complex computational research.Supports rich terminologies, ontologies and controlled vocabularies. Built by design to support iterative elaborations of common standards and supports these by probabilistic suggestion and review systems.

Proper citation: EBRAINS Knowledge Graph (RRID:SCR_017612) Copy   

•   Source: SciCrunch Registry

https://scdevdb.deepomics.org/

Database for insights into single cell gene expression profiles during human developmental processes. Interactive database provides DE gene lists in each developmental pathway, t-SNE map, and GO and KEGG enrichment analysis based on these differential genes.

Proper citation: Single Cell Developmental Database (RRID:SCR_017546) Copy   

•   Source: SciCrunch Registry

http://cerebrovascularportal.org

Portal enables browsing, searching, and analysis of human genetic information linked to cerebrovascular disease and related traits, while protecting the integrity and confidentiality of the underlying data.

Proper citation: Cerebrovascular Disease Knowledge Portal (RRID:SCR_015628) Copy   

•   Source: SciCrunch Registry

http://www.genetherapyreview.com/gene-therapy-research

The National Gene Vector Laboratories (NGVL) was established as a cooperative national effort to produce and distribute vectors for human gene transfer studies.

Proper citation: National Gene Vector Laboratories (RRID:SCR_015944) Copy   

•   Source: SciCrunch Registry

https://hirnetwork.org/consortium/chib

Consortium that is an independent research initiative of the Human Research Information Network (HIRN). It is combining advances in beta cell biology and cell biology with tissue engineering technologies to develop microdevices that support functional human islets.

Proper citation: HIRN Consortium on Human Islet Biomimetics (RRID:SCR_016199) Copy   

•   Source: SciCrunch Registry

http://projects.tcag.ca/humandup/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 17, 2013. It contains information about segmental duplications in the human genome. 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 Previous studies have suggested that recent segmental duplications, which are often involved in chromosome rearrangements underlying genomic disease, account for some 5 of the human genome. We have developed rapid computational heuristics based on BLAST analysis to detect segmental duplications, as well as regions containing potential sequence misassignments in the human genome assemblies. Results Our analysis of the June 2002 public human genome assembly revealed that 107.4 of 3,043.1 megabases (Mb) (3.53) of sequence contained segmental duplications, each with size equal or more than 5 kb and 90 identity. We have also detected that 38.9 Mb (1.28) of sequence within this assembly is likely to be involved in sequence misassignment errors. Furthermore, we have identified a significant subset (199,965 of 2,327,473 or 8.6) of single-nucleotide polymorphisms (SNPs) in the public databases that are not true SNPs but are potential paralogous sequence variants. Conclusion Using two distinct computational approaches, we have identified most of the sequences in the human genome that have undergone recent segmental duplications. Near-identical segmental duplications present a major challenge to the completion of the human genome sequence. Potential sequence misassignments detected in this study would require additional efforts to resolve. The segmental duplication data and summary statistics are available for download. Data for Human Genome (based on the May 2004 Human Genome Assembly (hg17)) Visualize duplication relationships in GBrowse (GBrowse) Duplicon Pair relationships (GFF) Genes within duplication regions (HTML) Genome duplication content (MS Excel) The segmental duplication data can be visualized in a genome browser in the GBrowse section. Selected human genome 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 human genome assemblies can be found in the Previous Analyses section. 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: Human Genome Segmental Duplication Database (RRID:SCR_007728) Copy   

•   Source: SciCrunch Registry

http://systers.molgen.mpg.de/

SYSTERS is a database of protein sequences grouped into homologous families and superfamilies. The SYSTERS project aims to provide a meaningful partitioning of the whole protein sequence space by a fully automatic procedure. A refined two-step algorithm assigns each protein to a family and a superfamily. The sequence data underlying SYSTERS release 4 now comprise several protein sequence databases derived from completely sequenced genomes (ENSEMBL, TAIR, SGD and GeneDB), in addition to the comprehensive Swiss-Prot/TrEMBL databases. To augment the automatically derived results, information from external databases like Pfam and Gene Ontology are added to the web server. Furthermore, users can retrieve pre-processed analyses of families like multiple alignments and phylogenetic trees. New query options comprise a batch retrieval tool for functional inference about families based on automatic keyword extraction from sequence annotations. A new access point, PhyloMatrix, allows the retrieval of phylogenetic profiles of SYSTERS families across organisms with completely sequenced genomes. Gene, Human, Vertebrate, Genome, Human ORFs

Proper citation: SYSTERS (RRID:SCR_007955) Copy   

•   Source: SciCrunch Registry

http://itfp.biosino.org/itfp/

ITFP is an integrated transcription factor (TF) platform, which included abundant TFs and targets message of mammalian. Support vector machine (SVM) algorithm combined with error-correcting output coding (ECOC) algorithm was utilized to identify and classify transcription factor from protein sequence of Human, Mouse and Rat. For transcription factor targets, a reverse engineering method named ARACNE was used to derive potential interaction pairs between transcription factor and downstream regulated gene from Human, Mouse and Rat gene expression profile data. Detailed information of gene expression profile data can be found in help page. Moreover, all data provided by the platform is free for non-commercial users and can be downloaded through links on help page.

Proper citation: Intergrated Transcription Factor Platform (RRID:SCR_008119) Copy   

•   Source: SciCrunch Registry

https://bioinformatics.oxfordjournals.org/content/21/4/557.full.pdf

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 18, 2016. MAP-O-MAT is a web-based server for automated linkage mapping of human polymorphic DNA markers. The server uses publicly available genotype data for over 15,000 markers. It facilitates the verification of order and map distances for custom mapping sets using genotype data from the CEPH database, and from the Marshfield, SNP Consortium and Rutgers linkage maps. The CRI-MAP program is used for likelihood calculations and some mapping algorithms, and physical map positions are provided from the human genome assembly.

Proper citation: MAP-O-MAT (RRID:SCR_008197) Copy   

•   Source: SciCrunch Registry

http://toxnet.nlm.nih.gov/cgi-bin/sis/htmlgen?iter

ITER is a toxicology data file on the National Library of Medicine''s (NLM) Toxicology Data Network. It contains data in support of human health risk assessments. It is compiled by Toxicology Excellence for Risk Assessment (TERA) and contains over 600 chemical records with key data from the Agency for Toxic Substances & Disease Registry (ATSDR), Health Canada, National Institute of Public Health & the Environment (RIVM) - The Netherlands, U.S. Environmental Protection Agency (EPA), and independent parties whose risk values have undergone peer review. ITER provides a comparison of international risk assessment information in a side-by-side format and explains differences in risk values derived by different organizations. ITER data, focusing on hazard identification and dose-response assessment, is extracted from each agencys assessment and contains links to the source documentation. Among the key data provided in ITER are ATSDRs minimal risk levels; Health Canadas tolerable intakes/concentrations and tumorigenic doses/concentrations; EPAs carcinogen classifications, unit risks, slope factors, oral reference doses, and inhalation reference concentrations; RIVMs maximum permissible risk levels; NSF International''s reference doses and carcinogen risk levels, IARC''s cancer classifications, and noncancer and/or cancer risk values (that have undergone peer review) derived by independent parties. Users can search by chemical or other name, chemical name fragment, or Chemical Abstracts Service Registry Number(RN), and/or subject terms. Search results can easily be viewed, printed or downloaded. Search results are displayed in relevancy ranked order. Users may select to display exact term matches, complete records, or any combination of data from the following broad groupings: -Noncancer Oral -Cancer Oral -Noncancer Inhalation -Cancer Inhalation

Proper citation: International Toxicity Estimates for Risk (RRID:SCR_008196) Copy   

•   Source: SciCrunch Registry

http://mips.gsf.de/proj/ppi/

The MIPS mammalian protein-protein interaction database (MPPI) is a new resource of high-quality experimental protein interaction data in mammals. The content is based on published experimental evidence that has been processed by human expert curators. It is a collection of manually curated high-quality PPI data collected from the scientific literature by expert curators. We took great care to include only data from individually performed experiments since they usually provide the most reliable evidence for physical interactions. To suit different users needs we provide a variety of interfaces to search the database: -Expert interface Simple but powerful boolean query language. -PPI search form Easy to use PPI search -Protein search Just find proteins of interest in the database Sponsors: This work is funded by a grant from the German Federal Ministry of Education and Research.

Proper citation: MIPS Mammalian Protein-Protein Interaction Database (RRID:SCR_008207) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/asd/altsplice/index.html

AltSplice is a computer generated high quality data set of human transcript-confirmed splice patterns, alternative splice events, and the associated annotations. This data is being integrated with other data that is generated by other members of the ASD consortium. The ASD project will provide the following in its three year duration: -human curated database of alternative spliced genes and their properties -a computer generated database of alternatively spliced genes and their properties -the integration of the above and newly found knowledge in a user-friendly interface and research workbench for both bioinformaticists and biologists -DNA chips that are based on the data in the above databases -the DNA chips will be used to test against predisposition for and diagnoses of human diseases ASD aims to analyse this mechanism on a genome-wide scale by creating a database that contains all alternatively spliced exons from human, and other model species. Disease causing mutations seem to induce aberrations in the process of splicing and its regulation. The ASD consortium will develop a DNA microarray (chip) that contains cDNAs of all the splicing regulatory proteins and their isoforms, as well as a chip that contains a number of disease relevant genes. We will concentrate on three models of disease (breast cancer, FTDP-17, male infertility) in which a connection between mis-splicing and a pathological state has been observed. Finally, these chips will be developed as demonstrative kits to detect predisposition for and diagnosis of such diseases. Categories: Nucleotide Sequences: Gene Structure, Introns and Exons, & Splice Sites Databases

Proper citation: AltSplice Database of Alternative Spliced Events (RRID:SCR_008162) Copy   

•   Source: SciCrunch Registry