Computational identification of epigenetically regulated lncRNAs and their associated genes based on integrating genomic data

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Abstract

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.

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APA

Zhao, T., Xu, J., Liu, L., Bai, J., Wang, L., Xiao, Y., … Zhang, L. (2015). Computational identification of epigenetically regulated lncRNAs and their associated genes based on integrating genomic data. FEBS Letters, 589(4), 521–531. https://doi.org/10.1016/j.febslet.2015.01.013

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