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      WoLF PSORT: protein localization predictor

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          Abstract

          WoLF PSORT is an extension of the PSORT II program for protein subcellular location prediction. WoLF PSORT converts protein amino acid sequences into numerical localization features; based on sorting signals, amino acid composition and functional motifs such as DNA-binding motifs. After conversion, a simple k-nearest neighbor classifier is used for prediction. Using html, the evidence for each prediction is shown in two ways: (i) a list of proteins of known localization with the most similar localization features to the query, and (ii) tables with detailed information about individual localization features. For convenience, sequence alignments of the query to similar proteins and links to UniProt and Gene Ontology are provided. Taken together, this information allows a user to understand the evidence (or lack thereof) behind the predictions made for particular proteins. WoLF PSORT is available at wolfpsort.org

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          Gene Ontology: tool for the unification of biology

          Michael Ashburner, Catherine A. Ball, Judith Blake … (2002)
          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            PSORT: a program for detecting sorting signals in proteins and predicting their subcellular localization.

            K Nakai, P Horton (1999)
            Article 0 comments Cited 272 times – based on 0 reviews     Review now
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              Extensive feature detection of N-terminal protein sorting signals.

              Hideo Bannai, Yoshinori Tamada, Osamu Maruyama … (2002)
              The prediction of localization sites of various proteins is an important and challenging problem in the field of molecular biology. TargetP, by Emanuelsson et al. (J. Mol. Biol., 300, 1005-1016, 2000) is a neural network based system which is currently the best predictor in the literature for N-terminal sorting signals. One drawback of neural networks, however, is that it is generally difficult to understand and interpret how and why they make such predictions. In this paper, we aim to generate simple and interpretable rules as predictors, and still achieve a practical prediction accuracy. We adopt an approach which consists of an extensive search for simple rules and various attributes which is partially guided by human intuition. We have succeeded in finding rules whose prediction accuracies come close to that of TargetP, while still retaining a very simple and interpretable form. We also discuss and interpret the discovered rules.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Nucleic Acids Res
                Journal ID (iso-abbrev): Nucleic Acids Res
                Journal ID (publisher-id): nar
                Journal ID (hwp): nar
                Title: Nucleic Acids Research
                Publisher: Oxford University Press
                ISSN (Print): 0305-1048
                ISSN (Electronic): 1362-4962
                Publication date (Print): July 2007
                Publication date (Electronic): 21 May 2007
                Publication date PMC-release: 21 May 2007
                Volume: 35
                Issue: Web Server issue
                Pages: W585-W587
                Affiliations
                1Computational Biology Research Center, AIST, Tokyo, Japan, 2Center for Genome Science, National Institute of Health, Korea Center for Disease Control & Prevention, 5 Nokbeon-Dong, Eunpyung-Gu, Seoul 122-701 Korea, 3Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan and 4Collier Technologies, Everett, WA, USA
                Author notes
                *To whom correspondence should be addressed. + 81-3-5449-5131 + 81-3-5449-5133 knakai@ 123456ims.u-tokyo.ac.jp
                Article
                DOI: 10.1093/nar/gkm259
                PMC ID: 1933216
                PubMed ID: 17517783
                SO-VID: 961b2bb5-7af3-4a6c-83c7-f3f6c77e2f8f
                Copyright © © 2007 The Author(s)
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 30 January 2007
                Date revision received : 26 March 2007
                Date accepted : 8 April 2007
                Categories
                Subject: Articles

                ScienceOpen disciplines: Genetics
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                ScienceOpen disciplines: Genetics

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