Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

ASM-Clust: classifying functionally diverse protein families using alignment score matrices

View ORCID ProfileDaan R. Speth, View ORCID ProfileVictoria J. Orphan
doi: https://doi.org/10.1101/792739
Daan R. Speth
1Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Daan R. Speth
  • For correspondence: dspeth@caltech.edu vorphan@gps.caltech.edu
Victoria J. Orphan
1Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Victoria J. Orphan
  • For correspondence: dspeth@caltech.edu vorphan@gps.caltech.edu
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

Abstract

Rapid advances in sequencing technology have resulted in the availability of genomes from organisms across the tree of life. Accurately interpreting the function of proteins in these genomes is a major challenge, as annotation transfer based on homology frequently results in misannotation and error propagation. This challenge is especially pressing for organisms whose genomes are directly obtained from environmental samples, as interpretation of their physiology and ecology is often based solely on the genome sequence. For complex protein (super)families containing a large number of sequences, classification can be used to determine whether annotation transfer is appropriate, or whether experimental evidence for function is lacking. Here we present a novel computational approach for de novo classification of large protein (super)families, based on clustering an alignment score matrix obtained by aligning all sequences in the family to a small subset of the data. We evaluate our approach on the enolase family in the Structure Function Linkage Database.

Availability and implementation ASM-Clust is implemented in bash with helper scripts in perl. Scripts comprising ASM-Clust are available for download from https://github.com/dspeth/bioinfo_scripts/tree/master/ASM_clust/

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
Back to top
PreviousNext
Posted October 03, 2019.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
ASM-Clust: classifying functionally diverse protein families using alignment score matrices
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
ASM-Clust: classifying functionally diverse protein families using alignment score matrices
Daan R. Speth, Victoria J. Orphan
bioRxiv 792739; doi: https://doi.org/10.1101/792739
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
ASM-Clust: classifying functionally diverse protein families using alignment score matrices
Daan R. Speth, Victoria J. Orphan
bioRxiv 792739; doi: https://doi.org/10.1101/792739

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (4675)
  • Biochemistry (10347)
  • Bioengineering (7659)
  • Bioinformatics (26307)
  • Biophysics (13505)
  • Cancer Biology (10672)
  • Cell Biology (15424)
  • Clinical Trials (138)
  • Developmental Biology (8490)
  • Ecology (12808)
  • Epidemiology (2067)
  • Evolutionary Biology (16835)
  • Genetics (11383)
  • Genomics (15471)
  • Immunology (10603)
  • Microbiology (25186)
  • Molecular Biology (10211)
  • Neuroscience (54399)
  • Paleontology (400)
  • Pathology (1667)
  • Pharmacology and Toxicology (2889)
  • Physiology (4334)
  • Plant Biology (9237)
  • Scientific Communication and Education (1586)
  • Synthetic Biology (2556)
  • Systems Biology (6774)
  • Zoology (1461)