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Title | LocTree2 predicts localization for all domains of life. |
Publication Type | Journal Article |
Year of Publication | 2012 |
Authors | Goldberg, T, Hamp, T, Rost, B |
Journal | Bioinformatics |
Volume | 28 |
Issue | 18 |
Pagination | i458-i465 |
Date Published | 2012 Sep 15 |
ISSN | 1367-4811 |
Keywords | Animals, Archaeal Proteins, Bacterial Proteins, Membrane Proteins, Molecular Sequence Annotation, Proteins, Sequence Analysis, Protein, Support Vector Machines |
Abstract | MOTIVATION: Subcellular localization is one aspect of protein function. Despite advances in high-throughput imaging, localization maps remain incomplete. Several methods accurately predict localization, but many challenges remain to be tackled.RESULTS: In this study, we introduced a framework to predict localization in life's three domains, including globular and membrane proteins (3 classes for archaea; 6 for bacteria and 18 for eukaryota). The resulting method, LocTree2, works well even for protein fragments. It uses a hierarchical system of support vector machines that imitates the cascading mechanism of cellular sorting. The method reaches high levels of sustained performance (eukaryota: Q18=65%, bacteria: Q6=84%). LocTree2 also accurately distinguishes membrane and non-membrane proteins. In our hands, it compared favorably with top methods when tested on new data.AVAILABILITY: Online through PredictProtein (predictprotein.org); as standalone version at http://www.rostlab.org/services/loctree2.CONTACT: localization@rostlab.orgSUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |
DOI | 10.1093/bioinformatics/bts390 |
Alternate Journal | Bioinformatics |
PubMed ID | 22962467 |
PubMed Central ID | PMC3436817 |