WoLF PSORT

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


References in zbMATH (referenced in 11 articles )

Showing results 1 to 11 of 11.
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  1. Cheng, Xiang; Xiao, Xuan; Chou, Kuo-Chen: pLoc_bal-mGneg: predict subcellular localization of Gram-negative bacterial proteins by quasi-balancing training dataset and general PseAAC (2018)
  2. Massahi, Aslan; Çalık, Pınar: In-silico determination of \textitPichiapastoris signal peptides for extracellular recombinant protein production (2015)
  3. Wan, Shibiao; Mak, Man-Wai; Kung, Sun-Yuan: GOASVM: a subcellular location predictor by incorporating term-frequency gene ontology into the general form of Chou’s pseudo-amino acid composition (2013)
  4. Bakheet, Tala; Doig, Andrew J.: Properties and identification of antibiotic drug targets (2010) ioport
  5. Blum, Torsten; Briesemeister, Sebastian; Kohlbacher, Oliver: Multiloc2: integrating phylogeny and gene ontology terms improves subcellular protein localization prediction (2009) ioport
  6. Du, Pufeng; Cao, Shengjiao; Li, Yanda: SubChlo: predicting protein subchloroplast locations with pseudo-amino acid composition and the evidence-theoretic (K)-nearest neighbor (ET-KNN) algorithm (2009)
  7. Tung, Thai Quang; Lee, Doheon: A method to improve protein subcellular localization prediction by integrating various biological data sources (2009) ioport
  8. Xu, Qian; Hu, Derek Hao; Xue, Hong; Yu, Weichuan; Yang, Qiang: Semi-supervised protein subcellular localization (2009) ioport
  9. Liu, Junfeng; Zhao, Hongyu; Tan, Jun; Luo, Dajie; Yu, Weichuan; Harner, E. James; Shih, Weichung Joe: Is subcellular localization informative for modeling protein-protein interaction signal? (2008) ioport
  10. Horton, Paul; Park, Keun-Joon; Obayashi, Takeshi; Fujita, Naoya; Harada, Hajime; Adams-Collier, C. J.; Nakai, Kenta: Wolf PSORT: Protein localization predictor. (2007) ioport
  11. Tamura, Takeyuki; Akutsu, Tatsuya: Subcellular location prediction of proteins using support vector machines with alignment of block sequences utilizing amino acid composition (2007) ioport