Open AccessIdentification of lncRNAs-gene interactions in transcription regulation based on co-expression analysis of RNA-seq data
Author(s) -
Si Jie Lu,
Juan Xie,
Li Yang,
Bin Yu,
Qin Ma,
Bing Qiang Liu
Publication year - 2019
Publication title -
mathematical biosciences and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.451
H-Index - 45
eISSN - 1551-0018
pISSN - 1547-1063
DOI - 10.3934/mbe.2019357
Subject(s) - computational biology , gene , biology , identification (biology) , rna seq , gene expression , regulation of gene expression , genetics , long non coding rna , rna , transcriptome , botany
Long noncoding RNAs (lncRNA) play important roles in gene expression regulation in diverse biological contexts. Numerous studies have indicated that lncRNA-gene interactions are closely related to the occurrence and development of cancers. Thus, it is important to develop an effective method for the identification of target genes of lncRNA. Meanwhile, the high throughput sequencing data provide tremendous information about regulation correlation, by which the new target genes could be detected from known lncRNA regulated genes. In this study, we developed a method for elucidating lncRNA-gene interactions by using a biclustering approach, which allows for the identification of particular expression patterns across multiple datasets, indicating networks of lncRNA and gene interactions. A p-value strategy is followed to link co-expression patterns to certain lncRNAs. The method was applied on the breast cancer RNA-seq datasets along with a set of known lncRNA regulated genes. The evaluation indicated that the method can detect some new targets but fail to obtain higher coverage. We believe that this developed method will provide useful information for future studies on lncRNAs.