In silico discovery of human natural antisense transcripts

• Published in: BMC bioinformatics Vol. 7; no. 1; p. 18
• Main Authors: Li, Yuan-Yuan, Qin, Lei, Guo, Zong-Ming, Liu, Lei, Xu, Hao, Hao, Pei, Su, Jiong, Shi, Yixiang, He, Wei-Zhong, Li, Yi-Xue
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 13.01.2006; BioMed Central; BMC
• Subjects: Algorithms; Base Sequence; Chromosome Mapping - methods; Databases, Protein; Humans; Molecular Sequence Data; Proteome - genetics; RNA, Antisense - genetics; Sequence Alignment - methods; Sequence Analysis, RNA - methods; Transcription Factors - genetics
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/1471-2105-7-18

Several high-throughput searches for potential natural antisense transcripts (NATs) have been performed recently, but most of the reports were focused on cis type. A thorough in silico analysis of human transcripts will help expand our knowledge of NATs. We have identified 568 NATs from human RefSeq RNA sequences. Among them, 403 NATs are reported for the first time, and at least 157 novel NATs are trans type. According to the pairing region of a sense and antisense RNA pair, hNATs are divided into 6 classes, of which about 87% involve 5' or 3' UTR sequences, supporting the regulatory role of UTRs. Among a total of 535 NAT pairs related with splice variants, 77.4% (414/535) have their pairing regions affected or completely eliminated by alternative splicing, suggesting significant relationship of alternative splicing and antisense-directed regulation. The extensive occurrence of splice variants in hNATs and other multiple pairing patterns results in a one-to-many relationship, allowing the formation of complex regulation networks. Based on microarray data from Stanford Microarray Database, two hNAT pairs were found to display significant inverse expression patterns before and after insulin injection. NATs might carry out more extensive and complex functions than previously thought. Combined with endogenous micro RNAs, hNATs could be regarded as a special group of transcripts contributing to the complex regulation networks.