LocTree3 prediction of localization

By Tatyana Goldberg, Maximilian Hecht, Tobias Hamp, Tim Karl, Guy Yachdav, Nadeem Ahmed, Uwe Altermann, Philipp Angerer, Sonja Ansorge, Kinga Balasz and 24 more. Nucleic Acids Res., 2014

300 citations recorded by the source corpus.

The prediction of protein sub-cellular localization is an important step toward elucidating protein function. For each query protein sequence, LocTree2 applies machine learning (profile kernel SVM) to predict the native sub-cellular localization in 18 classes for eukaryotes, in six for bacteria and in three for archaea. The method outputs a score that reflects the reliability of each prediction. LocTree2 has performed on par with or better than any other state-of-the-art method. Here, we report the availability of LocTree3 as a public web server. The server includes the machine learning-based LocTree2 and improves over it through the addition of homology-based inference. Assessed on sequence-unique data, LocTree3 reached an 18-state accuracy Q18 = 80 ± 3% for eukaryotes and a six-state accuracy Q6 = 89 ± 4% for bacteria. The server accepts submissions ranging from single protein sequences to entire proteomes. Response time of the unloaded server is about 90 s for a 300-residue eukaryotic protein and a few hours for an entire eukaryotic proteome not considering the generation of the alignments. For over 1000 entirely sequenced organisms, the predictions are directly available as downloads. The web server is available at http://www.rostlab.org/services/loctree3.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/d8ee28f382611b44750a85f7771d8f832b65198e

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