Skip to content Skip to navigation
University of Warwick
  • Study
  • |
  • Research
  • |
  • Business
  • |
  • Alumni
  • |
  • News
  • |
  • About

University of Warwick
Publications service & WRAP

Highlight your research

  • WRAP
    • Home
    • Search WRAP
    • Browse by Warwick Author
    • Browse WRAP by Year
    • Browse WRAP by Subject
    • Browse WRAP by Department
    • Browse WRAP by Funder
    • Browse Theses by Department
  • Publications Service
    • Home
    • Search Publications Service
    • Browse by Warwick Author
    • Browse Publications service by Year
    • Browse Publications service by Subject
    • Browse Publications service by Department
    • Browse Publications service by Funder
  • Help & Advice
University of Warwick

The Library

  • Login
  • Admin

An ultra-fast metabolite prediction algorithm

Tools
- Tools
+ Tools

Yang, Zheng Rong and Grant, Murray (2012) An ultra-fast metabolite prediction algorithm. PLoS One, 7 (6). pp. 1-11. e39158. doi:10.1371/journal.pone.0039158

[img]
Preview
PDF
WRAP_journal.pone.0039158.PDF - Published Version - Requires a PDF viewer.
Available under License Creative Commons Attribution 4.0.

Download (1265Kb) | Preview
Official URL: http://dx.doi.org/10.1371/journal.pone.0039158

Request Changes to record.

Abstract

Small molecules are central to all biological processes and metabolomics becoming an increasingly important discovery tool. Robust, accurate and efficient experimental approaches are critical to supporting and validating predictions from post-genomic studies. To accurately predict metabolic changes and dynamics, experimental design requires multiple biological replicates and usually multiple treatments. Mass spectra from each run are processed and metabolite features are extracted. Because of machine resolution and variation in replicates, one metabolite may have different implementations (values) of retention time and mass in different spectra. A major impediment to effectively utilizing untargeted metabolomics data is ensuring accurate spectral alignment, enabling precise recognition of features (metabolites) across spectra. Existing alignment algorithms use either a global merge strategy or a local merge strategy. The former delivers an accurate alignment, but lacks efficiency. The latter is fast, but often inaccurate. Here we document a new algorithm employing a technique known as quicksort. The results on both simulated data and real data show that this algorithm provides a dramatic increase in alignment speed and also improves alignment accuracy.

Item Type: Journal Article
Subjects: Q Science > QP Physiology
Divisions: Faculty of Science > Life Sciences (2010- )
Library of Congress Subject Headings (LCSH): Metabolites
Journal or Publication Title: PLoS One
Publisher: Public Library of Science
ISSN: 1932-6203
Official Date: 2012
Dates:
DateEvent
2012Published
Volume: 7
Number: 6
Number of Pages: 11
Page Range: pp. 1-11
Article Number: e39158
DOI: 10.1371/journal.pone.0039158
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access
Funder: Biotechnology and Biological Sciences Research Council (Great Britain) (BBSRC)
Grant number: BB/C514115/1 (BBSRC), BB/E010334/1 (BBSRC), BB/F005903/1 (BBSRC)

Request changes or add full text files to a record

Repository staff actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics

twitter

Email us: wrap@warwick.ac.uk
Contact Details
About Us