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Bioinformatics Advance Access originally published online on December 3, 2008
Bioinformatics 2009 25(3):353-357; doi:10.1093/bioinformatics/btn622
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© 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.


  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 alignments and physiological tissue expression. Using these as predictive parameters, we engineered a machine learning approach to decipher putative active regions in the genome.

Results: Analysis of genomic sequences showed nucleosome-free region (NFR) modules containing 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 transcriptional modules on a genome scale.

Contact: myar{at}seas.upenn.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Trey Ideker

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

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