ゲノム情報科学研究教育機構  アブストラクト
Date May 29, 2006
Speaker Dr. Paul Horton, CBRC (Computational Biology Research Center), National Institute of Advanced Industrial Science and Technology
Title Protein Subcellular Localization Prediction with WoLF PSORT
Abstract   I will present a new program for predicting protein subcellular localization from amino acid sequence. WoLF PSORT is a major update to the PSORTII program, based on new sequence data and incorporating new features with a feature selection procedure. Following SWISS-PROT, we divided eukaryotes into three groups: fungi, plant, and animal. For the 2113 fungi proteins divided into 14 categories; we found that, combined with BLAST, WoLF PSORT yields a cross-validated accuracy of 83%, eliminating about 1/3 of the errors made when using BLAST alone. For 12771 animal proteins a combined accuracy of 95.6% is obtained, eliminating 1/4 of BLAST alone errors, and for 2333 plant proteins the accuracy can be improved to 86% from 84%.
  In the talk I will use the disease protein treacle as a running example to illustrate the type of information one may obtain with the WoLF PSORT server. Time permitting, I may also briefly discuss some practical issues of maintaining a popular server (which has been accessed by approximately 20,000 unique URLs) on a shoe string budget.


References;

PSORT
Kenta Nakai, Minoru Kanehisa.
A Knowledge Base for Predicting Protein Localization Sites in Eukaryotic Cells.
Genomics, 14, 897-911(1992).

PSORTII
Paul Horton, Kenta Nakai.
Better Prediction of Protein Cellular Localization Sites with the k Nearest Neighbors Classifier.
Proceeding of the Fifth International Conference on Intelligent Systems for Molecular Biology, 147-152 (1997).

WoLF PSORT (http://wolfpsort.org)
Paul Horton, Keun-Joon Park, Takeshi Obayashi, Kenta Nakai.
Protein Subcellular Localization Prediction with WoLF PSORT.
Proceedings of the 4th Annual Asia Pacific Bioinformatics Conference APBC06 , 39-48 (2006).
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