Skip Navigation



Bioinformatics Advance Access published online on December 3, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn622
This Article
Right arrow Advance Access manuscript (PDF)
Right arrow Supplementary Data
Right arrow All Versions of this Article:
25/3/353    most recent
btn622v1
Right arrow Comments: Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when Comments are posted
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Yaragatti, M.
Right arrow Articles by Ungar, L.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Yaragatti, M.
Right arrow Articles by Ungar, L.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author (2008). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

A predictive model for identifying mini-regulatory modules in the mouse genome

Mahesh Yaragatti *, Ted Sandler and Lyle Ungar

Biotechnology Program, CIS, University of Pennsylvania, 3330 Walnut Street, Philadelphia, PA, 19104, USA

*To whom correspondence should be addressed. Mahesh Yaragatti, E-mail: myar{at}seas.upenn.edu


  Abstract

Motivation: Rapidly advancing genome technology has allowed access to a large number of diverse genomes and annotation data. We have defined a systems model that integrates assembly data, comparative genomics, gene predictions, mRNA and EST align-ments, and physiological tissue expression. Using these as predictive parameters, we engineered a machine learning ap-proach to decipher putative active regions in the genome

Results: Analysis of genomic sequences showed nucleosome-free region (NFR) modules contain a higher percentage of conserved regions, RNA encoding sequences, CpG islands, splice sites, and GC rich areas. In contrast, random in silico fragments revealed higher percentages of DNA repeats and a lower conservation. The larger conserved sequences from the Vista enhancer browser (VEB) showed a greater percentage of short DNA sequence matches and RNA coding regions in multiple species.

Our model can predict small regulatory regions in the genome with >95% prediction accuracy using NFR modules and >85% prediction accuracy with VEB elements. Ultimately, this systems model can be applied to any organism to identify candidate tran-scriptional modules on a genome scale.

Availability:The sequences used in this paper are available in Supplementary Table 2.

Contact:myar{at}seas.upenn.edu

Associate Editor: Dr. Trey Ideker

Received on January 26, 2008; revised on October 17, 2008; accepted on November 29, 2008

Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 Am. J. Roentgenol.Home page
D. Y. Lee and K. C. P. Li
Systems Diagnostics: The Systems Approach to Molecular Imaging
Am. J. Roentgenol., August 1, 2009; 193(2): 287 - 294.
[Abstract] [Full Text] [PDF]


Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.