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HFSP: high speed homology-driven function annotation of proteins

Cited 14 time in Web of Science Cited 13 time in Scopus
Authors

Mahlich, Yannick; Steinegger, Martin; Rost, Burkhard; Bromberg, Yana

Issue Date
2018-07
Publisher
Oxford University Press
Citation
Bioinformatics, Vol.34 No.13, pp.304-312
Abstract
Motivation: The rapid drop in sequencing costs has produced many more (predicted) protein sequences than can feasibly be functionally annotated with wet-lab experiments. Thus, many computational methods have been developed for this purpose. Most of these methods employ homology-based inference, approximated via sequence alignments, to transfer functional annotations between proteins. The increase in the number of available sequences, however, has drastically increased the search space, thus significantly slowing down alignment methods. Results: Here we describe homology-derived functional similarity of proteins (HFSP), a novel computational method that uses results of a high-speed alignment algorithm, MMseqs2, to infer functional similarity of proteins on the basis of their alignment length and sequence identity. We show that our method is accurate (85% precision) and fast (more than 40-fold speed increase over stateof-the-art). HFSP can help correct at least a 16% error in legacy curations, even for a resource of as high quality as Swiss-Prot. These findings suggest HFSP as an ideal resource for large-scale functional annotation efforts.
ISSN
1367-4803
URI
https://hdl.handle.net/10371/202577
DOI
https://doi.org/10.1093/bioinformatics/bty262
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  • College of Natural Sciences
  • School of Biological Sciences
Research Area Development of algorithms to search, cluster and assemble sequence data, Metagenomic analysis, Pathogen detection in sequencing data

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