tRNAfeature: An algorithm for tRNA features to identify tRNA genes in DNA sequences.
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.
Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, 415 Chien-Kung Road, Kaohsiung 80778, Taiwan. Electronic address: chyang@cc.kuas.edu.tw.
Cheng-Hong
CH
Yang
Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, 415 Chien-Kung Road, Kaohsiung 80778, Taiwan. Electronic address: e0955767257@yahoo.com.tw.
Yu-Da
YD
Lin
Department of Chemical Engineering & Institute of Biotechnology and Chemical Engineering, I-Shou University, No. 1, Sec. 1, Syuecheng Rd., Dashu District, Kaohsiung 84001, Taiwan. Electronic address: chuang@isu.edu.tw.
Li-Yeh
LY
Chuang
D000465:Algorithms; D020029:Base Pairing / Q000235:genetics; D001483:Base Sequence; D004247:DNA / Q000235:genetics*; D030561:Databases, Nucleic Acid; D020745:Genome, Archaeal; D007438:Introns / Q000235:genetics; D009690:Nucleic Acid Conformation; D012343:RNA, Transfer / Q000737:chemistry / Q000235:genetics*; D017279:Selenocysteine / Q000235:genetics
D017279:Selenocysteine; D004247:DNA; D012343:RNA, Transfer
tRNA feature; tRNA identification; tRNA secondary structure prediction