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Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Minireview cung cấp cho các bạn kiến thức về ngành y đề tài: Sequence-based feature prediction and annotation of proteins. | Review Sequence-based feature prediction and annotation of proteins Agnieszka S Juncker Lars J Jensen Andrea Pierleoni Andreas Bernsel Michael L Tress Peer Bork Gunnar von Heijne Alfonso Valencia Christos A Ouzounis Rita Casadio and S0ren Brunak Addresses Center for Biological Sequence Analysis Department of Systems Biology Technical University of Denmark DK-2800 Lyngby Denmark. European Molecular Biology Laboratory D-69117 Heidelberg Germany. University of Bologna Biocomputing Group Via San Giacomo 9 2 40126 Bologna Italy. Center for Biomembrane Research and Stockholm Bioinformatics Center Department of Biochemistry and Biophysics Stockholm University SE-106 91 Stockholm Sweden. Structural Biology and Biocomputing Programme Spanish National Cancer Research Centre CNIO Melchor Fernandez Almagro 3 E-28029 Madrid Spain. KCL Centre for Bioinformatics School of Physical Sciences and Engineering King s College London London WC2R 2LS UK. Correspondence S0ren Brunak. Email brunak@cbs.dtu.dk Published 2 February 2009 Genome Biology 2009 10 206 doi l0.ll86 gb-2009-l0-2-206 The electronic version of this article is the complete one and can be found online at http genomebiology.com 2009 10 2 206 2009 BioMed Central Ltd Abstract A recent trend in computational methods for annotation of protein function is that many prediction tools are combined in complex workflows and pipelines to facilitate the analysis of feature combinations for example the entire repertoire of kinase-binding motifs in the human proteome. As more sequenced genomes become available computational methods for predicting protein function from sequence data continue to be of high importance. In fact such methods represent the only viable strategy for keeping up with the growth of genomic information. In the current era of pan- and metagenomics it is obvious that computational annotation is essential for turning sequence data into functional knowledge that can be used to understand biological mechanisms and .
