Evidence map›Paper›PMID 29950013›Full record

ArticleBioinformatics (Oxford, England)2018

HFSP: high speed homology-driven function annotation of proteins.

Yannick Mahlich, Martin Steinegger, Burkhard Rost, Yana Bromberg

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.

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0cells of the map it votes in
31citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

31 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

4 authors.

Yannick MahlichDepartment of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ, USA.
Martin SteineggerComputational Biology & Bioinformatics - i12 Informatics, Technical University of Munich (TUM), Munich, Germany.
Burkhard RostComputational Biology & Bioinformatics - i12 Informatics, Technical University of Munich (TUM), Munich, Germany.
Yana BrombergDepartment of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ, USA.

Funding

AVA,Dx: Analysis of Variation for Association with DiseaseU01GM115486 · NIGMS · RUTGERS, THE STATE UNIV OF N.J. · PI BROMBERG, YANA · 2015 to 2018
$1.2M
NIGMS NIH HHS U01 GM115486
6 · The paper itself

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 state-of-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. Supplementary information: Supplementary data are available at Bioinformatics online.

Indexed as

AlgorithmsComputational BiologyMolecular Sequence AnnotationProteinsAmino Acid SequenceDatabases, ProteinSequence AlignmentProteins

Identifiers

PMID29950013
PMCPMC6022561

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.