Identification and analysis of mouse non-coding RNA using transcriptome data

• DOI: 10.1007/s11427-015-4929-x
• Publication Date: 2016-03-05T03:44:47Z
• Authors: Yuhui Zhao; Wanfei Liu; Jingyao Zeng; Shoucheng Liu; Xinyu Tan; Hasanawad Aljohi; Songnian Hu
• Keywords: H3K4me3

Transcripts are expressed spatially and temporally they very complicated, precise specific; however, most studies focused on protein-coding related genes. Recently, massively parallel cDNA sequencing (RNA-seq) has emerged to be a new promising tool for transcriptome research, numbers of non-coding RNAs, especially lincRNAs, have been widely identified well characterized as important regulators diverse biological processes. In this study, we used ultra-deep RNA-seq data from 15 mouse tissues study the diversity dynamic RNAs in mouse. Using our own criteria, totally 16,249 genes (21,569 RNAs) We annotated these by properties found generally shorter, fewer exons, express lower level more strikingly tissue-specific compared with Moreover, show significant enrichment transcriptional initiation elongation signals including histone modifications (H3K4me3, H3K27me3 H3K36me3), RNAPII binding sites CAGE tags. The gene set analysis (GSEA) result revealed several sets lincRNAs associated processes such immune effector process, muscle development sexual reproduction. Taken together, provides comprehensive annotation gives an opportunity future functional evolutionary RNAs.