[Gene prediction and function research of SARS-CoV(BJ01)].
Identifieur interne : 002F40 ( PubMed/Checkpoint ); précédent : 002F39; suivant : 002F41[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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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 Sinica</title><idno type="ISSN">0379-4172</idno><imprint><date when="2003" type="published">2003</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Computational Biology (methods)</term><term>Genes, Viral (genetics)</term><term>Open Reading Frames (genetics)</term><term>Reproducibility of Results</term><term>SARS Virus (genetics)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Biologie informatique ()</term><term>Cadres ouverts de lecture (génétique)</term><term>Gènes viraux (génétique)</term><term>Reproductibilité des résultats</term><term>Virus du SRAS (génétique)</term></keywords><keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Genes, Viral</term><term>Open Reading Frames</term><term>SARS Virus</term></keywords><keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>Cadres ouverts de lecture</term><term>Gènes viraux</term><term>Virus du 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><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">14682248</PMID><DateCompleted><Year>2004</Year><Month>01</Month><Day>28</Day></DateCompleted><DateRevised><Year>2006</Year><Month>11</Month><Day>15</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Print">0379-4172</ISSN><JournalIssue CitedMedium="Print"><Volume>30</Volume><Issue>8</Issue><PubDate><Year>2003</Year><Month>Aug</Month></PubDate></JournalIssue><Title>Yi chuan xue bao = Acta genetica Sinica</Title><ISOAbbreviation>Yi Chuan Xue Bao</ISOAbbreviation></Journal><ArticleTitle>[Gene prediction and function research of SARS-CoV(BJ01)].</ArticleTitle><Pagination><MedlinePgn>773-80</MedlinePgn></Pagination><Abstract><AbstractText>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.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Chen</LastName><ForeName>Ting-Gui</ForeName><Initials>TG</Initials><AffiliationInfo><Affiliation>Laboratory of Systems Biology, Beijing Institute of Radiation Medicine, Beijing 100850, China. chentg@hupo.org.cn</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Wu</LastName><ForeName>Song-Feng</ForeName><Initials>SF</Initials></Author><Author ValidYN="Y"><LastName>Wan</LastName><ForeName>Ping</ForeName><Initials>P</Initials></Author><Author ValidYN="Y"><LastName>Du</LastName><ForeName>Chun-Juan</ForeName><Initials>CJ</Initials></Author><Author ValidYN="Y"><LastName>Li</LastName><ForeName>Jian-Qi</ForeName><Initials>JQ</Initials></Author><Author ValidYN="Y"><LastName>Li</LastName><ForeName>Dong</ForeName><Initials>D</Initials></Author><Author ValidYN="Y"><LastName>Wei</LastName><ForeName>Guang-Zhi</ForeName><Initials>GZ</Initials></Author><Author ValidYN="Y"><LastName>Li</LastName><ForeName>Bin</ForeName><Initials>B</Initials></Author><Author ValidYN="Y"><LastName>Wang</LastName><ForeName>Zhong-Sheng</ForeName><Initials>ZS</Initials></Author><Author ValidYN="Y"><LastName>Xue</LastName><ForeName>Xiao-Fang</ForeName><Initials>XF</Initials></Author><Author ValidYN="Y"><LastName>Zhu</LastName><ForeName>Yun-Ping</ForeName><Initials>YP</Initials></Author><Author ValidYN="Y"><LastName>He</LastName><ForeName>Fu-Chu</ForeName><Initials>FC</Initials></Author></AuthorList><Language>chi</Language><PublicationTypeList><PublicationType UI="D004740">English Abstract</PublicationType><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList></Article><MedlineJournalInfo><Country>China</Country><MedlineTA>Yi Chuan Xue 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MajorTopicYN="Y">genetics</QualifierName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="pubmed"><Year>2003</Year><Month>12</Month><Day>20</Day><Hour>5</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2004</Year><Month>1</Month><Day>30</Day><Hour>5</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2003</Year><Month>12</Month><Day>20</Day><Hour>5</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">14682248</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>République populaire de Chine</li></country><settlement><li>Pékin</li></settlement></list><tree><noCountry><name sortKey="Du, Chun Juan" sort="Du, Chun Juan" uniqKey="Du C" first="Chun-Juan" last="Du">Chun-Juan Du</name><name sortKey="He, Fu Chu" sort="He, Fu Chu" uniqKey="He F" first="Fu-Chu" last="He">Fu-Chu He</name><name sortKey="Li, Bin" sort="Li, Bin" uniqKey="Li B" first="Bin" last="Li">Bin Li</name><name sortKey="Li, Dong" sort="Li, Dong" uniqKey="Li D" first="Dong" last="Li">Dong Li</name><name sortKey="Li, Jian Qi" sort="Li, Jian Qi" uniqKey="Li J" first="Jian-Qi" last="Li">Jian-Qi Li</name><name sortKey="Wan, Ping" sort="Wan, Ping" uniqKey="Wan P" first="Ping" last="Wan">Ping Wan</name><name sortKey="Wang, Zhong Sheng" sort="Wang, Zhong Sheng" uniqKey="Wang Z" first="Zhong-Sheng" last="Wang">Zhong-Sheng Wang</name><name sortKey="Wei, Guang Zhi" sort="Wei, Guang Zhi" uniqKey="Wei G" first="Guang-Zhi" last="Wei">Guang-Zhi Wei</name><name sortKey="Wu, Song Feng" sort="Wu, Song Feng" uniqKey="Wu S" first="Song-Feng" last="Wu">Song-Feng Wu</name><name sortKey="Xue, Xiao Fang" sort="Xue, Xiao Fang" uniqKey="Xue X" first="Xiao-Fang" last="Xue">Xiao-Fang Xue</name><name sortKey="Zhu, Yun Ping" sort="Zhu, Yun Ping" uniqKey="Zhu Y" first="Yun-Ping" last="Zhu">Yun-Ping Zhu</name></noCountry><country name="République populaire de Chine"><noRegion><name sortKey="Chen, Ting Gui" sort="Chen, Ting Gui" uniqKey="Chen T" first="Ting-Gui" last="Chen">Ting-Gui Chen</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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