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Nucleic Acids Res. 2018 Oct 17. doi: 10.1093/nar/gky960. [Epub ahead of print]

LncBook: a curated knowledgebase of human long non-coding RNAs.

Ma L1,2, Cao J1,2,3, Liu L1,2,3, Du Q4, Li Z1,2,3, Zou D1,2, Bajic VB5, Zhang Z1,2,3.

Author information

1
BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.
2
CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.
3
University of Chinese Academy of Sciences, Beijing 100049, China.
4
Anhui University of Technology, Maanshan 243032, China.
5
King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Thuwal 23955-6900, Kingdom of Saudi Arabia.

Abstract

Long non-coding RNAs (lncRNAs) have significant functions in a wide range of important biological processes. Although the number of known human lncRNAs has dramatically increased, they are poorly annotated, posing great challenges for better understanding their functional significance and elucidating their complex functioning molecular mechanisms. Here, we present LncBook (http://bigd.big.ac.cn/lncbook), a curated knowledgebase of human lncRNAs that features a comprehensive collection of human lncRNAs and systematic curation of lncRNAs by multi-omics data integration, functional annotation and disease association. In the present version, LncBook houses a large number of 270 044 lncRNAs and includes 1867 featured lncRNAs with 3762 lncRNA-function associations. It also integrates an abundance of multi-omics data from expression, methylation, genome variation and lncRNA-miRNA interaction. Also, LncBook incorporates 3772 experimentally validated lncRNA-disease associations and further identifies a total of 97 998 lncRNAs that are putatively disease-associated. Collectively, LncBook is dedicated to the integration and curation of human lncRNAs as well as their associated data and thus bears great promise to serve as a valuable knowledgebase for worldwide research communities.

PMID:
30329098
DOI:
10.1093/nar/gky960

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