[Gene prediction and function research of SARS-CoV(BJ01)].
Through reading the articles, this study points out the shortage of gene prediction and function research about SARS-CoV, and predict it again for developing effective drugs and future vaccines. Using twelve gene prediction methods to predict coronavirus known genes, we select four better methods including Heuristic models, Gene Identification, ZCURVE_CoV and ORF FINDER to predict SARS-CoV(BJ01), and use ATGpr for analyzing probability of initiation codon and Kozak rule, search transcription regulating sequence(TRS) in order to improve the accuracy of predicted genes. Twenty-one probable new genes with more than 50 amino acids have been obtained excluding 13 ORFs which are similar to the genes of NCBI and relative articles. For predicted proteins, we use ProtParam to analyse physical and chemical features; SignalP to analyse signal peptide; BLAST, FASTA to search similar sequences; TMPred, TMHMM, PFAM and HMMTOP to analyse domain and motif in order to improve reliability of gene function prediction. At the same time, we separate the 21 ORFs into four classes using codition of four gene prediction methods, match score, match expection and match length between predicted gene and Coronavirus known gene. In the end, we discuss the results and analyse the reasons.
Chen
Ting-Gui
TG
Laboratory of Systems Biology, Beijing Institute of Radiation Medicine, Beijing 100850, China. chentg@hupo.org.cn
Wu
Song-Feng
SF
Wan
Ping
P
Du
Chun-Juan
CJ
Li
Jian-Qi
JQ
Li
Dong
D
Wei
Guang-Zhi
GZ
Li
Bin
B
Wang
Zhong-Sheng
ZS
Xue
Xiao-Fang
XF
Zhu
Yun-Ping
YP
He
Fu-Chu
FC
D019295:Computational Biology / Q000379:methods*; D005814:Genes, Viral / Q000235:genetics*; D016366:Open Reading Frames / Q000235:genetics; D015203:Reproducibility of Results; D045473:Severe acute respiratory syndrome-related coronavirus / Q000235:genetics*