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

A critical assessment of Mus musculus gene function prediction using integrated genomic evidence

Tools
- Tools
+ Tools

Peña-Castillo, Lourdes, Tasan, Murat, Myers, Chad L, Lee, Hyunju, Joshi, Trupti, Zhang, Chao, Guan, Yuanfang, Leone, Michele, Pagnani, Andrea, Kim, Wan et al.
(2008) A critical assessment of Mus musculus gene function prediction using integrated genomic evidence. Genome Biology, 9 (Suppl 1). S2. doi:10.1186/gb-2008-9-s1-s2

Research output not available from this repository, contact author.
Official URL: http://dx.doi.org/10.1186/gb-2008-9-s1-s2

Request Changes to record.

Abstract

Background: Several years after sequencing the human genome and the mouse genome, much remains to be discovered about the functions of most human and mouse genes. Computational prediction of gene function promises to help focus limited experimental resources on the most likely hypotheses. Several algorithms using diverse genomic data have been applied to this task in model organisms; however, the performance of such approaches in mammals has not yet been evaluated.
Results: In this study, a standardized collection of mouse functional genomic data was assembled; nine bioinformatics teams used this data set to independently train classifiers and generate predictions of function, as defined by Gene Ontology (GO) terms, for 21,603 mouse genes; and the best performing submissions were combined in a single set of predictions. We identified strengths and weaknesses of current functional genomic data sets and compared the performance of function prediction algorithms. This analysis inferred functions for 76% of mouse genes, including 5,000 currently uncharacterized genes. At a recall rate of 20%, a unified set of predictions averaged 41% precision, with 26% of GO terms achieving a precision better than 90%.
Conclusion: We performed a systematic evaluation of diverse, independently developed computational approaches for predicting gene function from heterogeneous data sources in mammals. The results show that currently available data for mammals allows predictions with both breadth and accuracy. Importantly, many highly novel predictions emerge for the 38% of mouse genes that remain uncharacterized.

Item Type: Journal Article
Divisions: Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School > Biomedical Sciences > Translational & Experimental Medicine > Metabolic and Vascular Health (- until July 2016)
Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School
Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School > Biomedical Sciences > Translational & Experimental Medicine
Journal or Publication Title: Genome Biology
Publisher: BioMed Central Ltd.
ISSN: 1474-7596
Official Date: 28 June 2008
Dates:
DateEvent
28 June 2008Published
Volume: 9
Number: Suppl 1
Page Range: S2
DOI: 10.1186/gb-2008-9-s1-s2
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access

Request changes or add full text files to a record

Repository staff actions (login required)

View Item View Item
twitter

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