Discovering Transcriptional Regulatory Regions in Drosophila by a Nonalignment Method for Phylogenetic Footprinting.
From: Howard Hughes Medical Institute, Columbia University, New York, NY 10032, USA.
Proceedings of the National Academy of Sciences of the United States of America
- Publish Date: Apr 2007
- ISSN: 0027-8424
- Volume: 104
- Issue: 15
- Pages: 6305-10
- Medium: Print
- Language: English
- Citation (JAMA): Sosinsky Alona, Honig Barry, Mann Richard S, et al. Discovering Transcriptional Regulatory Regions in Drosophila by a Nonalignment Method for Phylogenetic Footprinting.. Proc. Natl. Acad. Sci. U.S.A. Apr 2007;104:6305-10
Abstract
The functional annotation of the nonprotein-coding DNA of eukaryotic genomes is a problem of central importance. Phylogenetic footprinting methods, which attempt to identify functional regulatory regions by comparing orthologous genomic sequences of evolutionarily related species, have shown promising results. The main advantage of this class of approaches is that they do not require any knowledge of the regulating transcription factors. Here we describe a method called Enhancer Detection using only Genomic Information (EDGI), which integrates a traditional motif-discovery algorithm with a local permutation-clustering algorithm. Together, they can identify large regulatory elements (e.g., enhancers) as evolutionarily conserved order-independent clusters of short conserved motifs. We show that EDGI can distinguish between established sets of known enhancers and nonenhancers with 88% accuracy, rivaling predictions by methods that rely on the knowledge of the regulating transcription factors and their DNA-binding specificities. We tested EDGI’s performance on a set of Drosophila genomes. Our results demonstrate that comparative genomic analysis of multiple closely related species has substantial power to identify key functional elements without additional biological knowledge.
Mesh Headings (Keywords): Algorithms, Animals, DNA Footprinting, Drosophila, Enhancer Elements (Genetics), Evaluation Studies as Topic, Genomics, Phylogeny
Check for Full Text / PubMed Unique Identifier (PMID): 17395715
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