Tools   Select Another Resource Report Type

http://biositemaps.ncbcs.org/rds/search.html

Resource Discovery System is a web-accessible and searchable inventory of biomedical research resources. Powered by the Resource Discovery System (RDS) that includes a standards-based informatics infrastructure * Biositemaps Information Model * Biomedical Resource Ontology Extensions * Web Services distributed web-accessible inventory framework * Biositemap Resource Editor * Resource Discovery System Source code and project documentation to be made available on an open-source basis. Contributing institutions: University of Pittsburgh, University of Michigan, Stanford University, Oregon Health & Science University, University of Texas Houston. Duke University, Emory University, University of California Davis, University of California San Diego, National Institutes of Health, Inventory Resources Working Group Members

Proper citation: Resource Discovery System (RRID:SCR_005554) Copy   

•   Source: SciCrunch Registry

http://www.neuroepigenomics.org/methylomedb/

A database containing genome-wide brain DNA methylation profiles for human and mouse brains. The DNA methylation profiles were generated by Methylation Mapping Analysis by Paired-end Sequencing (Methyl-MAPS) method and analyzed by Methyl-Analyzer software package. The methylation profiles cover over 80% CpG dinucleotides in human and mouse brains in single-CpG resolution. The integrated genome browser (modified from UCSC Genome Browser allows users to browse DNA methylation profiles in specific genomic loci, to search specific methylation patterns, and to compare methylation patterns between individual samples. Two species were included in the Brain Methylome Database: human and mouse. Human postmortem brain samples were obtained from three distinct cortical regions, i.e., dorsal lateral prefrontal cortex (dlPFC), ventral prefrontal cortex (vPFC), and auditory cortex (AC). Human samples were selected from our postmortem brain collection with extensive neuropathological and psychopathological data, as well as brain toxicology reports. The Department of Psychiatry of Columbia University and the New York State Psychiatric Institute have assembled this brain collection, where a validated psychological autopsy method is used to generate Axis I and II DSM IV diagnoses and data are obtained on developmental history, history of psychiatric illness and treatment, and family history for each subject. The mouse sample (strain 129S6/SvEv) DNA was collected from the entire left cerebral hemisphere. The three human brain regions were selected because they have been implicated in the neuropathology of depression and schizophrenia. Within each cortical region, both disease and non-psychiatric samples have been profiled (matching subjects by age and sex in each group). Such careful matching of subjects allows one to perform a wide range of queries with the ability to characterize methylation features in non-psychiatric controls, as well as detect differentially methylated domains or features between disease and non-psychiatric samples. A total of 14 non-psychiatric, 9 schizophrenic, and 6 depression methylation profiles are included in the database.

Proper citation: MethylomeDB (RRID:SCR_005583) Copy   

•   Source: SciCrunch Registry

http://rulai.cshl.edu/tred

Collects mammalian cis- and trans-regulatory elements together with experimental evidence. Regulatory elements were mapped on to assembled genomes. Resource for gene regulation and function studies. Users can retrieve primers, search TF target genes, retrieve TF motifs, search Gene Regulatory Networks and orthologs, and make use of sequence analysis tools. Uses databases such as Genbank, EPD and DBTSS, and employ promoter finding program FirstEF combined with mRNA/EST information and cross-species comparisons. Manually curated.

Proper citation: Transcriptional Regulatory Element Database (RRID:SCR_005661) Copy   

•   Source: SciCrunch Registry

http://www.jcvi.org/charprotdb/index.cgi/home

The Characterized Protein Database, CharProtDB, is designed and being developed as a resource of expertly curated, experimentally characterized proteins described in published literature. For each protein record in CharProtDB, storage of several data types is supported. It includes functional annotation (several instances of protein names and gene symbols) taxonomic classification, literature links, specific Gene Ontology (GO) terms and GO evidence codes, EC (Enzyme Commisssion) and TC (Transport Classification) numbers and protein sequence. Additionally, each protein record is associated with cross links to all public accessions in major protein databases as ��synonymous accessions��. Each of the above data types can be linked to as many literature references as possible. Every CharProtDB entry requires minimum data types to be furnished. They are protein name, GO terms and supporting reference(s) associated to GO evidence codes. Annotating using the GO system is of importance for several reasons; the GO system captures defined concepts (the GO terms) with unique ids, which can be attached to specific genes and the three controlled vocabularies of the GO allow for the capture of much more annotation information than is traditionally captured in protein common names, including, for example, not just the function of the protein, but its location as well. GO evidence codes implemented in CharProtDB directly correlate with the GO consortium definitions of experimental codes. CharProtDB tools link characterization data from multiple input streams through synonymous accessions or direct sequence identity. CharProtDB can represent multiple characterizations of the same protein, with proper attribution and links to database sources. Users can use a variety of search terms including protein name, gene symbol, EC number, organism name, accessions or any text to search the database. Following the search, a display page lists all the proteins that match the search term. Click on the protein name to view more detailed annotated information for each protein. Additionally, each protein record can be annotated.

Proper citation: CharProtDB: Characterized Protein Database (RRID:SCR_005872) Copy   

•   Source: SciCrunch Registry

http://www.broadinstitute.org/mammals/haploreg/haploreg.php

HaploReg is a tool for exploring annotations of the noncoding genome at variants on haplotype blocks, such as candidate regulatory SNPs at disease-associated loci. Using linkage disequilibrium (LD) information from the 1000 Genomes Project, linked SNPs and small indels can be visualized along with their predicted chromatin state in nine cell types, conservation across mammals, and their effect on regulatory motifs. HaploReg is designed for researchers developing mechanistic hypotheses of the impact of non-coding variants on clinical phenotypes and normal variation.

Proper citation: HaploReg (RRID:SCR_006796) Copy   

•   Source: SciCrunch Registry

http://www.uniprot.org/help/uniref

Databases which provide clustered sets of sequences from UniProt Knowledgebase and selected UniParc records, in order to obtain complete coverage of sequence space at several resolutions while hiding redundant sequences from view. The UniRef100 database combines identical sequences and sub-fragments with 11 or more residues (from any organism) into a single UniRef entry. The sequence of a representative protein, the accession numbers of all the merged entries, and links to the corresponding UniProtKB and UniParc records are all displayed in the entry. UniRef90 and UniRef50 are built by clustering UniRef100 sequences with 11 or more residues such that each cluster is composed of sequences that have at least 90% (UniRef90) or 50% (UniRef50) sequence identity to the longest sequence (UniRef seed sequence). All the sequences in each cluster are ranked to facilitate the selection of a representative sequence for the cluster.

Proper citation: UniRef (RRID:SCR_010646) Copy   

•   Source: SciCrunch Registry

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

Community model organism database for laboratory mouse and authoritative source for phenotype and functional annotations of mouse genes. MGD includes complete catalog of mouse genes and genome features with integrated access to genetic, genomic and phenotypic information, all serving to further the use of the mouse as a model system for studying human biology and disease. MGD is a major component of the Mouse Genome Informatics.Contains standardized descriptions of mouse phenotypes, associations between mouse models and human genetic diseases, extensive integration of DNA and protein sequence data, normalized representation of genome and genome variant information. Data are obtained and integrated via manual curation of the biomedical literature, direct contributions from individual investigators and downloads from major informatics resource centers. MGD collaborates with the bioinformatics community on the development and use of biomedical ontologies such as the Gene Ontology (GO) and the Mammalian Phenotype (MP) Ontology.

Proper citation: Mouse Genome Database (RRID:SCR_012953) Copy   

•   Source: SciCrunch Registry

http://hb.flatironinstitute.org/

Formerly known as GIANT (Genome-scale Integrated Analysis of gene Networks in Tissues), HumanBase applies machine learning algorithms to learn biological associations from massive genomic data collections. These integrative analyses reach beyond existing "biological knowledge" represented in the literature to identify novel, data-driven associations.

Proper citation: HumanBase (RRID:SCR_016145) Copy   

•   Source: SciCrunch Registry

https://phenodb.org/

Database for phenotype genotype associations for humans. Used by clinical researchers to store standardized phenotypic information, diagnosis, and pedigree data and then run analyses on VCF files from individuals, families or cohorts with suspected Mendelian disease.

Proper citation: PhenoDB (RRID:SCR_016551) Copy   

•   Source: SciCrunch Registry

https://dfam.org/home

Open collection of Transposable Element DNA sequence alignments, hidden Markov Models, consensus sequences, and genome annotations.Dfam 3.2 provides early access to uncurated, de novo generated families.

Proper citation: Dfam (RRID:SCR_021168) Copy   

•   Source: SciCrunch Registry

http://pgfe.umassmed.edu/ffs/

Database of Drosophila transcription factor DNA binding specificity using the bacterial one-hybrid method, DNase I or SELEX methods. The database provides community access to recognition motifs and position weight matrices for transcription factors (TFs), including many unpublished motifs. Search tools and flat file downloads are provided to retrieve binding site information (as sequences, matrices and sequence logos) for individual TFs, groups of TFs or for all TFs with characterized binding specificities. Linked analysis tools allow users to identify motifs within the database that share similarity to a query matrix or to view the distribution of occurrences of an individual motif throughout the Drosophila genome. This database and its associated tools provide computational and experimental biologists with resources to predict interactions between Drosophila TFs and target cis-regulatory sequences.

Proper citation: FlyFactorSurvey (RRID:SCR_002113) Copy   

•   Source: SciCrunch Registry

http://zfin.org

Model organism database that serves as central repository and web-based resource for zebrafish genetic, genomic, phenotypic and developmental data. Data represented are derived from three primary sources: curation of zebrafish publications, individual research laboratories and collaborations with bioinformatics organizations. Data formats include text, images and graphical representations.Serves as primary community database resource for laboratory use of zebrafish. Developed and supports integrated zebrafish genetic, genomic, developmental and physiological information and link this information extensively to corresponding data in other model organism and human databases.

Proper citation: Zebrafish Information Network (ZFIN) (RRID:SCR_002560) Copy   

•   Source: SciCrunch Registry

http://www.genome.wisc.edu/

The E. coli Genome Project has the goal of completely sequencing the E. coli and human genomes. They began isolation of an overlapping lambda clonebank of E. coli K-12 strain MG1655. Those clones served as the starting material in our initial efforts to sequence the whole genome. Improvements in sequencing technology have since reached the point where whole-genome sequencing of microbial genomes is routine, and the human genome has in fact been completed. They initiated additional sequencing efforts, concentrating on pathogenic members of the family Enterobacteriaceae -- to which E. coli belongs. They also began a systematic functional characterization of E. coli K-12 genes and their regulation, using the whole genome sequence to address how the over 4000 genes of this organism act together to enable its survival in a wide range of environments.

Proper citation: E. coli Genome project (RRID:SCR_008139) Copy   

•   Source: SciCrunch Registry

http://www.hgsc.bcm.tmc.edu/content/bovine-genome-project

Downloadable files of the bos taurus genome. Draft assemblies available for download as contigs or linearized scaffolds of the genomic sequence of cow, Bos taurus, including the final draft assembly (7.1 coverage) and the two previous assemblies. The genome is sequenced to 6- to 8-fold sequence depth, with high-quality finished sequence in some areas. Accompanying EST and SNP analyses is also included. The bovine genome assembly and analysis and the study of cattle genetic history were published in April 24, 2009 issue of Science. The Human Genome Sequencing Center provides BLAST searches of the genome assemblies, either as contigs or as linearized chromosome sequences. The WGS sequence enriched BAC assemblies and the unassembled reads (sequencing reads that did not end up in the genome assembly) can also be searched by BLAST. Traces are available from the NCBI Trace Archive by using the link in the sidebar or by using NCBI MegaBLAST with a same species or cross species query.

Proper citation: Bovine Genome Project (RRID:SCR_008370) Copy   

•   Source: SciCrunch Registry

https://www.archrproject.com/

Software R package for processing and analyzing single-cell ATAC-seq data. Used for integrative single cell chromatin accessibility analysis.Provides intuitive, user focused interface for complex single cell analysis, including doublet removal, single cell clustering and cell type identification, unified peak set generation, cellular trajectory identification, DNA element-to-gene linkage, transcription factor footprinting, mRNA expression level prediction from chromatin accessibility and multi-omic integration with single-cell RNA sequencing.

Proper citation: ArchR (RRID:SCR_020982) Copy   

•   Source: SciCrunch Registry

https://bioconductor.org/packages/release/bioc/html/PhenStat.html

Software R package for statistical analysis of phenotypic data.Tool kit for standardized analysis of high throughput phenotypic data.

Proper citation: PhenStat (RRID:SCR_021317) Copy   

•   Source: SciCrunch Registry

https://my.locuszoom.org

Web tool to investigate genome wide association results in their local genomic context. Adds new features to LocusZoom such as Manhattan plots, annotation options, and calculations that put findings in context. Used for interactive and embeddable visualization of genetic association study results.Javascript/d3 embeddable plugin for interactively visualizing statistical genetic data from customizable sources.

Proper citation: LocusZoom.org (RRID:SCR_021374) Copy   

•   Source: SciCrunch Registry

https://crispresso.pinellolab.partners.org/submission

Software suite of tools to qualitatively and quantitatively evaluate outcomes of genome editing experiments in which target loci are subject to deep sequencing and provides integrated, user friendly interface. Used for analysis of CRISPR-Cas9 genome editing outcomes from sequencing data. CRISPResso2 provides accurate and rapid genome editing sequence analysis.Used for analysis of deep sequencing data for rapid and intuitive interpretation of genome editing experiments.

Proper citation: CRISPResso (RRID:SCR_021538) Copy   

•   Source: SciCrunch Registry

https://github.com/marbl/salsa

Software tool for scaffold long read assemblies with Hi-C data.

Proper citation: SALSA (RRID:SCR_022013) Copy   

•   Source: SciCrunch Registry

http://ccr.coriell.org/Sections/Collections/NHGRI/?SsId=11

DNA samples and cell lines from fifteen populations, including the samples used for the International HapMap Project, the HapMap 3 Project and the 1000 Genomes Project (except for the CEPH samples). All of the samples were contributed with consent to broad data release and to their use in many future studies, including for extensive genotyping and sequencing, gene expression and proteomics studies, and all other types of genetic variation research. NHGRI led the contribution of the NIH to the International HapMap Project, which developed a haplotype map of the human genome. This haplotype map, called the HapMap is a publicly available tool that allows researchers to find genes and genetic variations that affect health and disease. The samples from four populations used to develop the HapMap were initially housed in the Human Genetic Cell Repository of the National Institute of General Medical Sciences (NIGMS). Except for the Utah CEPH samples that were in the NIGMS Repository before the initiation of the HapMap Project and remain there, the NHGRI Repository now houses all of the HapMap samples. The NHGRI repository also houses the extended set of HapMap samples, which includes additional samples from the HapMap populations and samples from seven additional populations. All of the samples were collected with extensive community engagement, including discussions with members of the donor communities about the ethical and social implications of human genetic variation research. These samples were studied as part of the HapMap 3 Project. The NHGRI repository also houses the samples for the International 1000 Genomes Project. This Project is lightly sequencing genome-wide 2500 samples from 27 populations. This project aims to provide a detailed map of human genetic variation, including common and rare SNPs and structural variants. This map will allow more precise localization of genomic regions that contribute to health and disease. The 1000 Genomes Project includes many of the samples from the HapMap and extended set of HapMap samples, as well as samples being collected from additional populations. Currently, samples from five additional populations are available; the others will become available during 2011 and 2012. No identifying or phenotypic information is available for the samples. Donors gave broad consent for use of the samples, including for genotyping, sequencing, and cellular phenotype studies. Samples collected from other populations for the study of human genetic variation may be added to the collection in the future. The NHGRI Repository distributes high quality lymphoblastoid cell lines and DNA from the samples to researchers. DNA is provided in plates or panels of 70 to 100 samples or as individual samples. Cell cultures and DNA samples are distributed only to qualified professional persons who are associated with recognized research, medical, educational, or industrial organizations engaged in health-related research or health delivery.

Proper citation: NHGRI Sample Repository for Human Genetic Research (RRID:SCR_004528) Copy   

•   Source: SciCrunch Registry