A predictive model for identifying mini-regulatory modules in the mouse genome.
BACKGROUND
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.
Yaragatti
Mahesh
M
Biotechnology Program, CIS, University of Pennsylvania, 3330 Walnut Street, Philadelphia, PA 19104, USA. myar@seas.upenn.edu
Sandler
Ted
T
Ungar
Lyle
L
D000465:Algorithms; D000818:Animals; D019295:Computational Biology / Q000379:methods*; D030561:Databases, Nucleic Acid; D020224:Expressed Sequence Tags; D016678:Genome; D023281:Genomics / Q000379:methods; D051379:Mice; D012333:RNA, Messenger / Q000235:genetics; D012045:Regulatory Sequences, Nucleic Acid / Q000235:genetics*
D012333:RNA, Messenger