Cozzetto, D;
Minneci, F;
Currant, H;
Jones, DT;
(2016)
FFPred 3: feature-based function prediction for all Gene Ontology domains.
Scientific Reports
, 6
, Article 31865. 10.1038/srep31865.
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Abstract
Predicting protein function has been a major goal of bioinformatics for several decades, and it has gained fresh momentum thanks to recent community-wide blind tests aimed at benchmarking available tools on a genomic scale. Sequence-based predictors, especially those performing homology-based transfers, remain the most popular but increasing understanding of their limitations has stimulated the development of complementary approaches, which mostly exploit machine learning. Here we present FFPred 3, which is intended for assigning Gene Ontology terms to human protein chains, when homology with characterized proteins can provide little aid. Predictions are made by scanning the input sequences against an array of Support Vector Machines (SVMs), each examining the relationship between protein function and biophysical attributes describing secondary structure, transmembrane helices, intrinsically disordered regions, signal peptides and other motifs. This update features a larger SVM library that extends its coverage to the cellular component sub-ontology for the first time, prompted by the establishment of a dedicated evaluation category within the Critical Assessment of Functional Annotation. The effectiveness of this approach is demonstrated through benchmarking experiments, and its usefulness is illustrated by analysing the potential functional consequences of alternative splicing in human and their relationship to patterns of biological features.
| Type: | Article |
|---|---|
| Title: | FFPred 3: feature-based function prediction for all Gene Ontology domains |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1038/srep31865 |
| Publisher version: | http://doi.org/10.1038/srep31865 |
| Language: | English |
| Additional information: | © The Author(s) 2016. This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
| Keywords: | Science & Technology, Multidisciplinary Sciences, Science & Technology - Other Topics, PROTEIN FUNCTION PREDICTION, CLASSIFICATION, ANNOTATIONS, SEQUENCE, DATABASE, TOOL |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1514421 |
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