SLPred: a multi-view subcellular localization prediction tool for multi-location human proteins.

Autor:	Özsarı G; Department of Computer Engineering, Middle East Technical University, Ankara 06800, Turkey.; Department of Computer Engineering, Niğde Ömer Halisdemir University, Niğde 51240, Turkey., Rifaioglu AS; Department of Computer Engineering, İskenderun Technical University, Hatay 31200, Turkey.; Faculty of Medicine, Institute for Computational Biomedicine, Heidelberg University and Heidelberg University Hospital, Heidelberg 69120, Germany., Atakan A; Department of Computer Engineering, Middle East Technical University, Ankara 06800, Turkey.; Department of Computer Engineering, Erzincan Binali Yıldırım University, Erzincan 24002, Turkey., Doğan T; Department of Computer Engineering, Hacettepe University, Ankara 06800, Turkey., Martin MJ; European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, Hinxton CB10 1SD, UK., Çetin Atalay R; Graduate School of Informatics Middle East Technical University, Ankara 06800, Turkey.; Section of Pulmonary and Critical Care Medicine, the University of Chicago, Chicago, IL 60637, USA., Atalay V; Department of Computer Engineering, Middle East Technical University, Ankara 06800, Turkey.
Jazyk:	angličtina
Zdroj:	Bioinformatics (Oxford, England) [Bioinformatics] 2022 Sep 02; Vol. 38 (17), pp. 4226-4229.
DOI:	10.1093/bioinformatics/btac458
Abstrakt:	Summary: Accurate prediction of the subcellular locations (SLs) of proteins is a critical topic in protein science. In this study, we present SLPred, an ensemble-based multi-view and multi-label protein subcellular localization prediction tool. For a query protein sequence, SLPred provides predictions for nine main SLs using independent machine-learning models trained for each location. We used UniProtKB/Swiss-Prot human protein entries and their curated SL annotations as our source data. We connected all disjoint terms in the UniProt SL hierarchy based on the corresponding term relationships in the cellular component category of Gene Ontology and constructed a training dataset that is both reliable and large scale using the re-organized hierarchy. We tested SLPred on multiple benchmarking datasets including our-in house sets and compared its performance against six state-of-the-art methods. Results indicated that SLPred outperforms other tools in the majority of cases. Availability and Implementation: SLPred is available both as an open-access and user-friendly web-server (https://slpred.kansil.org) and a stand-alone tool (https://github.com/kansil/SLPred). All datasets used in this study are also available at https://slpred.kansil.org. Supplementary Information: Supplementary data are available at Bioinformatics online. (© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)
Databáze:	MEDLINE
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