Protein Subcellular Localization Prediction and Genomic Polymorphism Analysis of the SARS Coronavirus

被引：0
|
作者：
季星来
柳树群
李岭
孙之荣
机构：
[1] Department of Biological Sciences and Biotechnology
[2] Institute of Bioinformatics
[3] Tsinghua University
[4] Beijing 100084
[5] China Medical University
[6] China
[7] Shenyang 110001
[8] Department of Medical Genetics
来源：
TsinghuaScienceandTechnology | 2004年 / 04期
基金：
中国国家自然科学基金;
关键词：
severe acute respiratory syndrome (SARS); subcellular localization; viral life cycle; genomic polymorphism;
D O I：
暂无
中图分类号：
R373 [人体病毒学（致病病毒）];
学科分类号：
摘要：
The cause of severe acute respiratory syndrome (SARS) has been identified as a new coronavi-rus (CoV). Several sequences of the complete genome of SARS-CoV have been determined. The subcellu-lar localization (SubLocation) of annotated open-reading frames of the SARS-CoV genome was predicted using a support vector machine. Several gene products were predicted to locate in the Golgi body and cell nucleus. The SubLocation information was combined with predicted transmembrane information to develop a model of the viral life cycle. The results show that this information can be used to predict the functions of genes and even the virus pathogenesis. In addition, the entire SARS viral genome sequences currently available in GenBank were compared to identify the sequence variations among different isolates. Some variations in the Hong Kong strains may be related to the special clinical manifestations and provide clues for understanding the relationship between gene functions and evolution. These variations reflect the evolu-tion of the SARS virus in human populations and may help development of a vaccine.
引用
收藏
页码：384 / 390
页数：7
相关论文
共 50 条
  • [31] Prediction of protein subcellular localization based on primary sequence data
    Özarar, M
    Atalay, V
    Atalay, RÇ
    PROCEEDINGS OF THE IEEE 12TH SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE, 2004, : 118 - 120
  • [32] Structure exploration and function prediction of SARS coronavirus E protein
    Shao, C
    Hu, DH
    Sun, HZ
    Yan, LK
    Su, ZM
    Wang, RS
    Zhu, WS
    Guo, JH
    Shi, NZ
    Sun, H
    Li, ZS
    Sun, CC
    CHEMICAL JOURNAL OF CHINESE UNIVERSITIES-CHINESE, 2005, 26 (08): : 1512 - 1516
  • [33] Prediction of protein subcellular localization based on primary sequence data
    Özarar, M
    Atalay, V
    Atalay, RÇ
    COMPUTER AND INFORMATION SCIENCES - ISCIS 2003, 2003, 2869 : 611 - 618
  • [34] Robust prediction of protein subcellular localization combining PCA and WSVMs
    Tian, Jiang
    Gu, Hong
    Liu, Wenqi
    Gao, Chiyang
    COMPUTERS IN BIOLOGY AND MEDICINE, 2011, 41 (08) : 648 - 652
  • [35] Meta-prediction of protein subcellular localization with reduced voting
    Liu, Jie
    Kang, Shuli
    Tang, Chuanning
    Ellis, Lynda B. M.
    Li, Tongbin
    NUCLEIC ACIDS RESEARCH, 2007, 35 (15)
  • [36] PROlocalizer: integrated web service for protein subcellular localization prediction
    Laurila, Kirsti
    Vihinen, Mauno
    AMINO ACIDS, 2011, 40 (03) : 975 - 980
  • [37] A DEEP NEURAL NETWORK APPROACH FOR THE PREDICTION OF PROTEIN SUBCELLULAR LOCALIZATION
    Samson, A. B. P.
    Chandra, S. R. A.
    Manikant, M.
    NEURAL NETWORK WORLD, 2021, 31 (01) : 29 - 45
  • [38] Deep Forest-based Prediction of Protein Subcellular Localization
    Zhao, Lingling
    Wang, Junjie
    Nabil, Mahieddine Mohammed
    Zhang, Jun
    CURRENT GENE THERAPY, 2018, 18 (05) : 268 - 274
  • [39] PROlocalizer: integrated web service for protein subcellular localization prediction
    Kirsti Laurila
    Mauno Vihinen
    Amino Acids, 2011, 40 : 975 - 980
  • [40] TESTLoc: protein subcellular localization prediction from EST data
    Shen, Yao-Qing
    Burger, Gertraud
    BMC BIOINFORMATICS, 2010, 11
12345