UBER anatomy ONtology
How to cite this record: FAIRsharing.org: UBERON; UBER anatomy ONtology; DOI: https://doi.org/10.25504/FAIRsharing.4c0b6b; Last edited: Feb. 22, 2018, 2:03 p.m.; Last accessed: Mar 19 2018 5:02 a.m.
|online documentation||https://github.com/obophenotype/uberon/w ...|
No XSD schemas defined
Conditions of UseApplies to: Data use
Uberon, an integrative multi-species anatomy ontology.
Mungall CJ,Torniai C,Gkoutos GV,Lewis SE,Haendel MA
Genome Biol 2012
Unification of multi-species vertebrate anatomy ontologies for comparative biology in Uberon.
Haendel MA,Balhoff JP,Bastian FB,Blackburn DC,Blake JA,Bradford Y,Comte A,Dahdul WM,Dececchi TA,Druzinsky RE,Hayamizu TF,Ibrahim N,Lewis SE,Mabee PM,Niknejad A,Robinson-Rechavi M,Sereno PC,Mungall CJ
J Biomed Semantics 2014
Models and Formats
No syntax standards defined
Bgee is a database to retrieve and compare gene expression patterns in multiple animal species, produced from multiple data types (RNA-Seq, Affymetrix, in situ hybridization, and EST data). Bgee is based exclusively on curated "normal", healthy, expression data (e.g., no gene knock-out, no treatment, no disease), to provide a comparable reference of normal gene expression. Bgee produces calls of presence/absence of expression, and of differential over-/under-expression, integrated along with information of gene orthology, and of homology between organs. This allows comparisons of expression patterns between species.
The ENCODE (Encyclopedia of DNA Elements) Consortium is an international collaboration of research groups funded by the National Human Genome Research Institute (NHGRI). The goal of ENCODE is to build a comprehensive parts list of functional elements in the human genome, including elements that act at the protein and RNA levels, and regulatory elements that control cells and circumstances in which a gene is active.
Microenvironment Perturbagen LINCS Center image server
The MEP LINCS project contributes to the development of the NIH Library of Integrated Network-based Cellular Signatures (LINCS) program by developing a dataset and computational strategy to elucidate how microenvironment (ME) signals affect cell intrinsic intracellular transcriptional- and protein-defined molecular networks to generate experimentally observable cellular phenotypes measured by high-content imaging.
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