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http://clkb.ncibi.org

Public data warehouse for searching cell line data extracted from both ATCC and HyperCLDB. The knowledge base uses the Cell Line Ontology, created with the Protege ontology editing tool from the National Center for Biomedical Ontologies (NCBO) and merges concepts from other ontologies, including the Cell Type Ontology. The Cell Line Knowledge Base uses our Cell Line Ontology as the underlying data model. The ontology defines the following cell line attributes: Cell Line ID, Organism, Tissue, Pathology, Growth Mode, MeSH ID. To report errors in the data or to add cell line data to the knowledge base, please email: clbk-data (at) umich.edu

Proper citation: Cell Line Knowledge Base (RRID:SCR_005832) Copy   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

https://database.riken.jp/sw/en/The_RIKEN_integrated_database_of_mammals/ria254i/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 16, 2019.
A database that integrates not only RIKEN''''s original large-scale mammalian databases, such as FANTOM, the ENU mutagenesis program, the RIKEN Cerebellar Development Transcriptome Database and the Bioresource Database, but also imported data from public databases, such as Ensembl, MGI and biomedical ontologies. Our integrated database has been implemented on the infrastructure of publication medium for databases, termed SciNetS/SciNeS, or the Scientists'''' Networking System, where the data and metadata are structured as a semantic web and are downloadable in various standardized formats. The top-level ontology-based implementation of mammal-related data directly integrates the representative knowledge and individual data records in existing databases to ensure advanced cross-database searches and reduced unevenness of the data management operations. Through the development of this database, we propose a novel methodology for the development of standardized comprehensive management of heterogeneous data sets in multiple databases to improve the sustainability, accessibility, utility and publicity of the data of biomedical information.

Proper citation: RIKEN integrated database of mammals (RRID:SCR_006890) Copy   

•   Source: SciCrunch Registry

http://tikus.gsf.de

THIS RESOURCE IS NO LONGER IN SERVICE, documented on October 23, 2014. Consortium that generated a reference library of gene trap sequence tags (GTST) from insertional mutations generated in mouse embryonic stem (ES) cells. The gene trap database represents a repository of sequences produced in a large scale gene trap screen in mouse ES cells using various gene trapping vectors which are delivered either by electroporation or retroviral infections. A type of retroviral gene trap vector has been developed that can induce conditional mutations in most genes expressed in mouse embryonic stem (ES) cells. The vectors rely on directional site-specific recombination systems that can repair and re-induce gene trap mutations when activated in succession. After the gene traps are inserted into the mouse genome, genetic mutations can be produced at a particular time and place in somatic cells. In addition to their conditional features, the vectors create multipurpose alleles amenable to a wide range of post-insertional modifications. Here they have used these directional recombination vectors to assemble the largest library of ES cell lines with conditional mutations in single genes yet assembled, presently totaling 1,000 unique genes. The trapped ES cell lines, which can be ordered from the German Gene Trap Consortium, are freely available to the scientific community.

Proper citation: German Gene Trap Consortium (RRID:SCR_008532) Copy   

•   Source: SciCrunch Registry

http://www.genepaint.org/R0_1.htm

A digital atlas of gene expression patterns in the mouse. Expression patterns are determined by non-radioactive in situ hybridization on serial tissue sections. An accompanying atlas based on maps of sagittal sections at embryonic day 14.5. E14.5 NMRI embryo was prepared, sectioned and imaged identically to the embryos used for in situ hybridization. Maps are accessed from the set viewer page using the appropriate button above the image directory. Both, the in situ hybridization section and the appropriate atlas section can be viewed side-by-side. Section thickness is 20 m and inter-section distance is 100 m. Tissue was stained with cresyl violet (Nissl-method). All sections were digitally scanned using a 5x objective. Structures annotated for gene expression are indicated in the maps with red pointers. Boundaries between brain regions are indicated with dashed yellow lines.

Proper citation: GenePaint Interactive Anatomy Atlas (RRID:SCR_007680) Copy   

•   Source: SciCrunch Registry

http://tulane.edu/som/regenmed/services/index.cfm

The Stem Cell Research and Regenerative Medicine''s Tissue Culture Core provides cells for research use within the department, as well as for distribution to other facilities. The core obtains hMSCs from bone marrow donor samples and expands these cells for research use. The hMSC''s are also characterized for bone, fat and cartilage differentiation, and are stored on site for use. The Tissue Culture Core also handles the expansion and characterization of mouse and rat MSC''s. The animal cells are cultured in a separate area, and never interact with human derived cells. We also have a supply of hMSC''s marked with GFP+, Mito Red and Mito Blue available.

Proper citation: Tulane Stem Cell Research and Regenerative Medicine Tissue Culture Core (RRID:SCR_007342) Copy   

•   Source: SciCrunch Registry

http://ccr.coriell.org/Sections/Collections/CSCB/Default.aspx

Biospecimen repository that provides scientists with the opportunity to bank their pluripotent stem cell lines and develops in-house induced pluripotent stem cell (iPSC) lines for distribution. They have developed core capabilities to maintain, characterize, bank, and distribute important stem cell resources. The SCB performs extensive identification and characterization testing for all submitted human induced pluripotent stem cell (iPSC) and mouse embryonic stem cell (mES) lines. The identification and quality control measures include karyotype analysis, microsatellite analysis for parental cell line identity matching, sterility testing, and assessment of viability after cryopreservation. Pluripotency characterizations performed by SCB vary depending upon the distributing repository. * NIGMS iPSCs: Surface antigen expression, Embryoid body formation, Pluritest Gene Expression assay * NINDS iPSCs: Surface antigen expression, Embryoid body formation * NIA mES: Surface antigen expression, Embryoid body formation, Transgene induction Each characterized human iPSC line and mES line released for distribution is provided with a Certificate of Analysis, which includes information regarding characterization and quality of the line, images and links to original publications. The human iPSCs distributed by Coriell are strictly for research purposes and cannot be used in human subjects. All terms described in the Material Transfer Agreement (NIGMS and NINDS Repositories) or Assurance Form (NIA Repository) for the stem cell line must be agreed to prior to using stem cell lines from Coriell.

Proper citation: Coriell Institute Stem Cell Biobank (RRID:SCR_008745) Copy   

•   Source: SciCrunch Registry

http://ftp://ftp.informatics.jax.org/pub/reports/MGI_PhenotypicAllele.rpt

Data set of collected and annotated expression and activity data for recombinase-containing transgenes and knock-in alleles. As the authoritative source of official names for mouse genes, alleles, and strains, MGI makes this list of transgenes available as a service and includes all known transgenes and synonyms. NIF provides a database interface so that researchers may have a better idea whether the trangene or transgenic animal that they are searching for is available.
Nomenclature follows the rules and guidelines established by the International Committee on Standardized Genetic Nomenclature for Mice.

Proper citation: Mouse Genome Informatics Transgenes (RRID:SCR_003468) Copy   

•   Source: SciCrunch Registry

http://krasnow1.gmu.edu/cn3/L-Neuron/database/

A database of virtually generated anatomically plausible neurons for several morphological classes, including cerebellar Purkinje cells, hippocampal pyramidal and granule cells, and spinal cord motoneurons. It presently contains 542 cells. In the trade neurons collection the database contains an amaral cell archive, neuron morpho reconstructions, and mouse alpha motoneurons. Their collection of generated neurons include motoneurons, Purkinje cells, and hippocampal pyramidal cells.

Proper citation: Virtual NeuroMorphology Electronic Database (RRID:SCR_007118) Copy   

•   Source: SciCrunch Registry

http://degradome.uniovi.es/domains.html

Domains found in human and mouse proteases colour-coded according to the catalytic class in which they appear. Some of them appear in more than one catalytic group, and two-colours are used. Yellow, aspartyl proteases; blue, cysteine proteases; green, metalloproteases; and red, serine proteases.

Proper citation: Ancillary Domains Associated With Human and Mouse Proteases (RRID:SCR_008363) Copy   

•   Source: SciCrunch Registry

https://confluence.crbs.ucsd.edu/display/NIF/StemCellInfo

Data tables providing an overview of information about stem cells that have been derived from mice and humans. The tables summarize published research that characterizes cells that are capable of developing into cells of multiple germ layers (i.e., multipotent or pluripotent) or that can generate the differentiated cell types of another tissue (i.e., plasticity) such as a bone marrow cell becoming a neuronal cell. The tables do not include information about cells considered progenitor or precursor cells or those that can proliferate without the demonstrated ability to generate cell types of other tissues. The tables list the tissue from which the cells were derived, the types of cells that developed, the conditions under which differentiation occurred, the methods by which the cells were characterized, and the primary references for the information.

Proper citation: National Institutes of Health Stem Cell Tables (RRID:SCR_008359) Copy   

•   Source: SciCrunch Registry

http://cell.ccrc.uga.edu/world/glycomics/glycomics.php

Biomedical technology research center that develops and implements new technologies to investigate the glycome of cells, including glycoproteomics and glycoconjugate analysis, transcript analysis and bioinformatics. It develops the tools and technology to analyze in detail the glycoprotein and glycolipid expression of mouse embryonic stem cells and the cells into which they differentiate. The technology developed in the Center will allow an understanding of how glycosylation is controlled during differentiation and will allow the development of tools to promote the use of stem cells to treat human disease. In addition, the technology developed will be applicable to the study of other cell types, including cancer cells that are progressing to a more invasive phenotype. The technology developed will also allow others in the scientific community to participate in glycomics research through dissemination of the new methods developed and through the analytical services provided by the resource to other scientists requesting assistance in glycomic analyses.

Proper citation: Integrated Technology Resource for Biomedical Glycomics (RRID:SCR_009003) Copy   

•   Source: SciCrunch Registry

http://www.openbioinformatics.org/annovar/

An efficient software tool to utilize update-to-date information to functionally annotate genetic variants detected from diverse genomes (including human genome hg18, hg19, as well as mouse, worm, fly, yeast and many others). Given a list of variants with chromosome, start position, end position, reference nucleotide and observed nucleotides, ANNOVAR can perform: 1. gene-based annotation. 2. region-based annotation. 3. filter-based annotation. 4. other functionalities. (entry from Genetic Analysis Software)

Proper citation: ANNOVAR (RRID:SCR_012821) Copy   

•   Source: SciCrunch Registry

http://www.sbpdiscovery.org/technology/sr/Pages/LaJolla_TumorAnalysis.aspx

Facility that provides analysis of animal models of human cancer and other diseases. It provides investigators access to a wide variety of human cancer cell lines for xenograft studies, some primary human xenograft models and additional transgenic mouse solid tumors and leukemia models. The core also offers serial passaging of tumors and derivation of 2D and 3D cultures from xenograft tumors, including patient-derived xenograft (PDX) models. These short-term cell cultures established from PDXs enable in vitro analysis including high throughput screening with compounds or RNAi for functional characterization.

Proper citation: Sanford Burnham Prebys Medical Discovery Institute Tumor Analysis (RRID:SCR_014858) Copy   

•   Source: SciCrunch Registry

http://genomequebec.mcgill.ca/PReMod

Database that describes more than 100,000 computational predicted transcriptional regulatory modules within the human genome. These modules represent the regulatory potential for 229 transcription factors families and are the first genome-wide / transcription factor-wide collection of predicted regulatory modules for the human genome. The algorithm used involves two steps: (i) Identification and scoring of putative transcription factor binding sites using 481 TRANSFAC 7.2 position weight matrices (PWMs) for vertebrate transcription factors. To this end, each non-coding position of the human genome was evaluated for its similarity to each PWM using a log-likelihood ratio score with a local GC-parameterized third-order Markov background model. Corresponding orthologous positions in mouse and rat genomes were evaluated similarly and a weighted average of the human, mouse, and rat log-likelihood scores at aligned positions (based on a Multiz (Blanchette et al. 2004) genome-wide alignment of these three species) was used to define the matrix score for each genomic position and each PWM. (ii) Detection of clustered putative binding sites. To assign a module score to a given region, the five transcription factors with the highest total scoring hits are identified, and a p-value is assigned to the total score observed of the top 1, 2, 3, 4, or 5 factors. The p-value computation takes into consideration the number of factors involved (1 to 5), their total binding site scores, and the length and GC content of the region under evaluation. Users can retrieve all information for a given region, a given PWM, a given gene and so on. Several options are given for textual output or visualization of the data.

Proper citation: PReMod (RRID:SCR_003403) Copy   

•   Source: SciCrunch Registry

http://www.sharmuk.org/

A not for profit organization to accelerate research into aging by sharing resources: providing access to cost and time effective, aged murine tissue through a biorepository and database of live ageing colonies, as well as promoting the networking of researchers and dissemination of knowledge through its online collaborative environment; MiCEPACE. ShARM will provide valuable resources for the scientific community while helping to reduce the number of animals used in vital research into aging. The biobank of tissue and networking facility will enable scientists to access shared research material and data. By making use of collective resources, the number of individual animals required in research experiments can be minimized. The project also has the added value of helping to reduce the costs of research by connecting scientists, pooling resource and combining knowledge. ShARM works in partnership with MRC Harwell and the Centre for Intergrated Research into Musculoskeletal Ageing (CIMA).

Proper citation: ShARM (RRID:SCR_003120) Copy   

•   Source: SciCrunch Registry

http://mirnamap.mbc.nctu.edu.tw

A database of experimentally verified microRNAs and miRNA target genes in human, mouse, rat, and other metazoan genomes. In addition to known miRNA targets, three computational tools previously developed, such as miRanda, RNAhybrid and TargetScan, were applied for identifying miRNA targets in 3'-UTR of genes. In order to reduce the false positive prediction of miRNA targets, several criteria are supported for filtering the putative miRNA targets. Furthermore, miRNA expression profiles can provide valuable clues for investigating the properties of miRNAs, such tissue specificity and differential expression in cancer/normal cell. Therefore, we performed the Q-PCR experiments for monitoring the expression profiles of 224 human miRNAs in eighteen major normal tissues in human. The cross-reference between the miRNA expression profiles and the expression profiles of its target genes can provide effective viewpoint to understand the regulatory functions of the miRNA.

Proper citation: miRNAMap (RRID:SCR_003156) Copy   

•   Source: SciCrunch Registry

http://compbio.uthsc.edu/miRSNP/

Database of naturally occurring DNA variations in microRNA (miRNA) seed regions and miRNA target sites. MicroRNAs pair to the transcripts of protein-coding genes and cause translational repression or mRNA destabilization. SNPs and INDELs in miRNAs and their target sites may affect miRNA-mRNA interaction, and hence affect miRNA-mediated gene repression. The PolymiRTS database was created by scanning 3'UTRs of mRNAs in human and mouse for SNPs and INDELs in miRNA target sites. Then, the potential downstream effects of these polymorphisms on gene expression and higher-order phenotypes are identified. Specifically, genes containing PolymiRTSs, cis-acting expression QTLs, and physiological QTLs in mouse and the results of genome-wide association studies (GWAS) of human traits and diseases are linked in the database. The PolymiRTS database also includes polymorphisms in target sites that have been supported by a variety of experimental methods and polymorphisms in miRNA seed regions.

Proper citation: PolymiRTS (RRID:SCR_003389) Copy   

•   Source: SciCrunch Registry

http://ratmap.gen.gu.se/

THIS RESOURCE IS NO LONGER IN SERVICE, documented May 10, 2017. A pilot effort that has developed a centralized, web-based biospecimen locator that presents biospecimens collected and stored at participating Arizona hospitals and biospecimen banks, which are available for acquisition and use by researchers. Researchers may use this site to browse, search and request biospecimens to use in qualified studies. The development of the ABL was guided by the Arizona Biospecimen Consortium (ABC), a consortium of hospitals and medical centers in the Phoenix area, and is now being piloted by this Consortium under the direction of ABRC. You may browse by type (cells, fluid, molecular, tissue) or disease. Common data elements decided by the ABC Standards Committee, based on data elements on the National Cancer Institute''s (NCI''s) Common Biorepository Model (CBM), are displayed. These describe the minimum set of data elements that the NCI determined were most important for a researcher to see about a biospecimen. The ABL currently does not display information on whether or not clinical data is available to accompany the biospecimens. However, a requester has the ability to solicit clinical data in the request. Once a request is approved, the biospecimen provider will contact the requester to discuss the request (and the requester''s questions) before finalizing the invoice and shipment. The ABL is available to the public to browse. In order to request biospecimens from the ABL, the researcher will be required to submit the requested required information. Upon submission of the information, shipment of the requested biospecimen(s) will be dependent on the scientific and institutional review approval. Account required. Registration is open to everyone., documented September 2, 2016. Database for defining official rat gene symbols. It includes rat gene symbols from three major sources: the Rat Genome Database (RGD), Ensembl, and NCBI-Gene. All rat symbols are compared with official symbols from orthologous human genes as specified by the Human Gene Nomenclature Committee (HGNC). Based on the outcome of the comparisons, a rat gene symbol may be selected. Rat symbols that do not match a human ortholog undergo a strict procedure of comparisons between the different rat gene sources as well as with the Mouse Genome Database (MGD). For each rat gene this procedure results in an unambiguous gene designation. The designation is presented as a status level that accompanies every rat gene symbol suggested in the database. The status level describes both how a rat symbol was selected, and its validity. Rat Gene Symbol Tracker approves rat gene symbols by an automatic procedure. The rat genes are presented with links to RGD, Ensembl, NCBI Gene, MGI and HGNC. RGST ensures that each acclaimed rat gene symbol is unique and follows the guidelines given by the RGNC. To each symbol a status level associated, describing the gene naming process.

Proper citation: Rat Gene Symbol Tracker (RRID:SCR_003261) Copy   

•   Source: SciCrunch Registry

http://bioinfo.mbi.ucla.edu/ASAP/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on 8/12/13. Database to access and mine alternative splicing information coming from genomics and proteomics based on genome-wide analyses of alternative splicing in human (30 793 alternative splice relationships found) from detailed alignment of expressed sequences onto the genomic sequence. ASAP provides precise gene exon-intron structure, alternative splicing, tissue specificity of alternative splice forms, and protein isoform sequences resulting from alternative splicing. They developed an automated method for discovering human tissue-specific regulation of alternative splicing through a genome-wide analysis of expressed sequence tags (ESTs), which involves classifying human EST libraries according to tissue categories and Bayesian statistical analysis. They use the UniGene clusters of human Expressed Sequence Tags (ESTs) to identify splices. The UniGene EST's are clustered so that a single cluster roughly corresponds to a gene (or at least a part of a gene). A single EST represents a portion of a processed (already spliced) mRNA. A given cluster contains many ESTs, each representing an outcome of a series of splicing events. The ESTs in UniGene contain the different mRNA isoforms transcribed from an alternatively spliced gene. They are not predicting alternative splicing, but locating it based on EST analysis. The discovered splices are further analyzed to determine alternative splicing events. They have identified 6201 alternative splice relationships in human genes, through a genome-wide analysis of expressed sequence tags (ESTs). Starting with 2.1 million human mRNA and EST sequences, they mapped expressed sequences onto the draft human genome sequence and only accepted splices that obeyed the standard splice site consensus. After constructing a tissue list of 46 human tissues with 2 million human ESTs, they generated a database of novel human alternative splices that is four times larger than our previous report, and used Bayesian statistics to compare the relative abundance of every pair of alternative splices in these tissues. Using several statistical criteria for tissue specificity, they have identified 667 tissue-specific alternative splicing relationships and analyzed their distribution in human tissues. They have validated our results by comparison with independent studies. This genome-wide analysis of tissue specificity of alternative splicing will provide a useful resource to study the tissue-specific functions of transcripts and the association of tissue-specific variants with human diseases.

Proper citation: ASAP: the Alternative Splicing Annotation Project (RRID:SCR_003415) Copy   

•   Source: SciCrunch Registry