[Gene prediction and function research of SARS-CoV(BJ01)].
Identifieur interne : 000516 ( Ncbi/Curation ); précédent : 000515; suivant : 000517[Gene prediction and function research of SARS-CoV(BJ01)].
Auteurs : Ting-Gui Chen [République populaire de Chine] ; Song-Feng Wu ; Ping Wan ; Chun-Juan Du ; Jian-Qi Li ; Dong Li ; Guang-Zhi Wei ; Bin Li ; Zhong-Sheng Wang ; Xiao-Fang Xue ; Yun-Ping Zhu ; Fu-Chu HeSource :
- Yi chuan xue bao = Acta genetica Sinica [ 0379-4172 ] ; 2003.
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- MESH :
- genetics : Genes, Viral, Open Reading Frames, SARS Virus.
- methods : Computational Biology.
- Reproducibility of Results.
Abstract
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.
PubMed: 14682248
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last="Li">Jian-Qi Li</name></author><author><name sortKey="Li, Dong" sort="Li, Dong" uniqKey="Li D" first="Dong" last="Li">Dong Li</name></author><author><name sortKey="Wei, Guang Zhi" sort="Wei, Guang Zhi" uniqKey="Wei G" first="Guang-Zhi" last="Wei">Guang-Zhi Wei</name></author><author><name sortKey="Li, Bin" sort="Li, Bin" uniqKey="Li B" first="Bin" last="Li">Bin Li</name></author><author><name sortKey="Wang, Zhong Sheng" sort="Wang, Zhong Sheng" uniqKey="Wang Z" first="Zhong-Sheng" last="Wang">Zhong-Sheng Wang</name></author><author><name sortKey="Xue, Xiao Fang" sort="Xue, Xiao Fang" uniqKey="Xue X" first="Xiao-Fang" last="Xue">Xiao-Fang Xue</name></author><author><name sortKey="Zhu, Yun Ping" sort="Zhu, Yun Ping" uniqKey="Zhu Y" first="Yun-Ping" last="Zhu">Yun-Ping Zhu</name></author><author><name sortKey="He, Fu Chu" sort="He, Fu Chu" uniqKey="He F" first="Fu-Chu" last="He">Fu-Chu He</name></author></analytic><series><title level="j">Yi chuan xue bao = Acta genetica 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SRAS</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Computational Biology</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Reproducibility of Results</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Biologie informatique</term><term>Reproductibilité des résultats</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">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.</div></front></TEI></record>Pour manipuler ce document sous Unix (Dilib)
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