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Computational identification of epigenetically regulated lncRNAs and their associated genes based on integrating genomic data
Author(s) -
Zhao Tingting,
Xu Jinyuan,
Liu Ling,
Bai Jing,
Wang Lihua,
Xiao Yun,
Li Xia,
Zhang Liming
Publication year - 2015
Publication title -
febs letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.593
H-Index - 257
eISSN - 1873-3468
pISSN - 0014-5793
DOI - 10.1016/j.febslet.2015.01.013
Subject(s) - h3k4me3 , prc2 , epigenetics , computational biology , biology , gene , histone , histone h3 , identification (biology) , genetics , gene expression , promoter , botany
Long non‐coding RNAs (lncRNAs) are new players in various biological processes. However, understanding of lncRNAs is still in its infancy. Here, we proposed an integrative method to identify epigenetically regulated lncRNAs and their associated genes. By combining RNA‐seq data and ChIP‐seq data for histone H3 trimethylated at lysine 4 (H3K4me3) and H3K27me3, we identified 699 H3K4me3‐regulated and 235 H3K27me3‐regulated lncRNAs, each with an average of 238 and 307 associated genes, respectively. By analyzing Polycomb repressive complex 2 (PRC2) binding maps, we observed that the negatively related genes of most epigenetically regulated lncRNAs were enriched for PRC2‐binding genes. In addition, through enrichment analysis, we inferred some lncRNAs with aberrant epigenetic modifications in glioblastoma and Alzheimer's disease. Together, we describe a method for the analysis of lncRNAs and demonstrate how integration of multi‐omics data can improve understanding of lncRNAs.