Back to Search Start Over

lncRNAKB, a knowledgebase of tissue-specific functional annotation and trait association of long noncoding RNA

Authors :
Komudi Singh
Vijender Chaitankar
Fernando S. Goes
Xiangbo Ruan
Abhilash Suresh
Ping Li
Peter P. Zandi
Richard S. Lee
Yi Chen
Ilker Tunc
Jennifer T Judy
Yun-Ching Chen
Mehdi Pirooznia
Haiming Cao
M. Saleet Jafri
Fayaz Seifuddin
Source :
Scientific Data, Vol 7, Iss 1, Pp 1-16 (2020), Scientific Data
Publication Year :
2020
Publisher :
Springer Science and Business Media LLC, 2020.

Abstract

Long non-coding RNA Knowledgebase (lncRNAKB) is an integrated resource for exploring lncRNA biology in the context of tissue-specificity and disease association. A systematic integration of annotations from six independent databases resulted in 77,199 human lncRNA (224,286 transcripts). The user-friendly knowledgebase covers a comprehensive breadth and depth of lncRNA annotation. lncRNAKB is a compendium of expression patterns, derived from analysis of RNA-seq data in thousands of samples across 31 solid human normal tissues (GTEx). Thousands of co-expression modules identified via network analysis and pathway enrichment to delineate lncRNA function are also accessible. Millions of expression quantitative trait loci (cis-eQTL) computed using whole genome sequence genotype data (GTEx) can be downloaded at lncRNAKB that also includes tissue-specificity, phylogenetic conservation and coding potential scores. Tissue-specific lncRNA-trait associations encompassing 323 GWAS (UK Biobank) are also provided. LncRNAKB is accessible at http://www.lncrnakb.org/, and the data are freely available through Open Science Framework (10.17605/OSF.IO/RU4D2).<br />Measurement(s) regulation of gene expression • sequence feature annotation • lnc_RNA • tissue-specific expression of lncRNA • Expression Quantitative Trait Locus Technology Type(s) digital curation • computational modeling technique Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12827597

Details

Language :
English
ISSN :
20524463
Volume :
7
Database :
OpenAIRE
Journal :
Scientific Data
Accession number :
edsair.doi.dedup.....2c6a92660f14c005bdad89be95be2484
Full Text :
https://doi.org/10.1038/s41597-020-00659-z