Prediction of translation initiation site for microbial genomes with TriTISA.
We report a new and simple method, TriTISA, for accurate prediction of translation initiation site (TIS) of microbial genomes. TriTISA classifies all candidate TISs into three categories based on evolutionary properties, and characterizes them in terms of Markov models. Then, it employs a Bayesian methodology for the selection of true TIS with a non-supervised, iterative procedure. Assessment on experimentally verified TIS data shows that TriTISA is overall better than all other methods of the state-of-the-art for microbial genome TIS prediction. In particular, TriTISA is shown to have a robust accuracy independent of the quality of initial annotation.


BACKGROUND
The C++ source code is freely available under the GNU GPL license via http://mech.ctb.pku.edu.cn/protisa/TriTISA.
Hu
Gang-Qing
GQ
State Key Lab for Turbulence and Complex Systems, Department of Biomedical Engineering, College of Engineering and Center for Theoretical Biology, Peking University, Beijing 100871, China.
Zheng
Xiaobin
X
Zhu
Huai-Qiu
HQ
She
Zhen-Su
ZS
D001483:Base Sequence; D019295:Computational Biology / Q000379:methods*; D016680:Genome, Bacterial / Q000235:genetics*; D014176:Protein Biosynthesis / Q000235:genetics*; D012984:Software