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Computational Methods for Annotation Transfers from Sequence

Cozzetto, D; Jones, DT; (2016) Computational Methods for Annotation Transfers from Sequence. In: Dessimoz, C and Škunca, N, (eds.) The Gene Ontology Handbook: Part II. (pp. 55-67). Springer New York: New York, USA. Green open access

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Abstract

Surveys of public sequence resources show that experimentally supported functional information is still completely missing for a considerable fraction of known proteins and is clearly incomplete for an even larger portion. Bioinformatics methods have long made use of very diverse data sources alone or in combination to predict protein function, with the understanding that different data types help elucidate complementary biological roles. This chapter focuses on methods accepting amino acid sequences as input and producing GO term assignments directly as outputs; the relevant biological and computational concepts are presented along with the advantages and limitations of individual approaches.

Type: Book chapter
Title: Computational Methods for Annotation Transfers from Sequence
Location: United States
ISBN-13: 9781493937417
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-1-4939-3743-1_5
Publisher version: http://dx.doi.org/10.1007/978-1-4939-3743-1_5
Language: English
Additional information: Copyright © 2017 The Author(s). Open Access: This chapter is distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, a link is provided to the Creative Commons license and any changes made are indicated. The images or other third party material in this chapter are included in the work’s Creative Commons license, unless indicated otherwise in the credit line; if such material is not included in the work’s Creative Commons license and the respective action is not permitted by statutory regulation, users will need to obtain permission from the license holder to duplicate, adapt or reproduce the material.
Keywords: De novo function prediction, Homology-based annotation transfers, Multi-domain architecture, Phylogenomics, Protein function prediction
UCL classification: UCL > Provost and Vice Provost Offices
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/1529158
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