WoLF PSORT predicts the subcellular localization sites of proteins based on their amino acid sequences. The method, which is a major extension to the venerable PSORTII program, makes predictions based on both known sorting signal motifs and some correlative sequence features such as amino acid content. Like PSORT and PSORTII, WoLF PSORT displays some information about detected sorting signals which is useful in helping users determine the reliability of the prediction in specific cases. Our experiments (paper in preparation) show that the overall prediction accuracy of WoLF PSORT is over 80%. For common localization sites (e.g. cytosol, nucleus, mitochondria, etc) WoLF PSORT makes better than majority classifier predictions even for queries that do not have strong sequence similarity to any sequence in the dataset. Thus WoLF PSORT is a useful complement to tools such as BLAST. The current dataset used to train WoLF PSORT contains over 12,000 animal sequences and more than 2,000 plant and fungi sequences respectively. It was gathered mainly from Uniprot but several hundred Arabidopsis thaliana sequences from the Gene Ontology database were also included.


WoLF PSORT was being developed by

What's in a name

"WoLF" does not necessarily stand for anything. A rather dramatic mnemonic would be "Where Life Functions". Originally it was going to be "Learned Weight Features" but I wanted the acronym to be a pronouncable English word. Waldo Lives Forever.


Functional Analysis in silico | NAKAI Lab
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