Yang CH, Lin YD, Chuang LY. tRNAfeature: An algorithm for tRNA features to identify tRNA genes in DNA sequences.
J Theor Biol 2016;
404:251-261. [PMID:
27291467 DOI:
10.1016/j.jtbi.2016.06.008]
[Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2016] [Revised: 05/30/2016] [Accepted: 06/07/2016] [Indexed: 11/28/2022]
Abstract
The identification of transfer RNAs (tRNAs) is critical for a detailed understanding of the evolution of biological organisms and viruses. However, some tRNAs are difficult to recognize due to their unusual sub-structures and may result in the detection of the wrong anticodon. Therefore, the detection of unusual sub-structures of tRNA genes remains an important challenge. In this study, we propose a method to identify tRNA genes based on tRNA features. tRNAfeature attempts to refold the sequence with single-stranded regions longer than those found in the canonical and conventional structural models for tRNA. We predicted a set of 53926 archaeal, eubacterial and eukaryotic tRNA genes annotated in tRNADB-CE and scanned the tRNA genes in whole genome sequencing. The results indicate that tRNAfeature is more powerful than other existing methods for identifying tRNAs.
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