Dataset Information

Discovering transcriptional regulatory regions in Drosophila by a nonalignment method for phylogenetic footprinting.

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.

SUBMITTER: Sosinsky A

PROVIDER: S-EPMC1851073 | biostudies-literature | 2007 Apr

REPOSITORIES: biostudies-literature

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Publications

Discovering transcriptional regulatory regions in Drosophila by a nonalignment method for phylogenetic footprinting.

Sosinsky Alona A Honig Barry B Mann Richard S RS Califano Andrea A

Proceedings of the National Academy of Sciences of the United States of America 20070329 15

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 ...[more]

PMID: 17395715

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Project description:BACKGROUND: Phylogenetic footprinting is the identification of functional regions of DNA by their evolutionary conservation. This is achieved by comparing orthologous regions from multiple species and identifying the DNA regions that have diverged less than neutral DNA. Vestige is a phylogenetic footprinting package built on the PyEvolve toolkit that uses probabilistic molecular evolutionary modelling to represent aspects of sequence evolution, including the conventional divergence measure employed by other footprinting approaches. In addition to measuring the divergence, Vestige allows the expansion of the definition of a phylogenetic footprint to include variation in the distribution of any molecular evolutionary processes. This is achieved by displaying the distribution of model parameters that represent partitions of molecular evolutionary substitutions. Examination of the spatial incidence of these effects across regions of the genome can identify DNA segments that differ in the nature of the evolutionary process. RESULTS: Vestige was applied to a reference dataset of the SCL locus from four species and provided clear identification of the known conserved regions in this dataset. To demonstrate the flexibility to use diverse models of molecular evolution and dissect the nature of the evolutionary process Vestige was used to footprint the Ka/Ks ratio in primate BRCA1 with a codon model of evolution. Two regions of putative adaptive evolution were identified illustrating the ability of Vestige to represent the spatial distribution of distinct molecular evolutionary processes. CONCLUSION: Vestige provides a flexible, open platform for phylogenetic footprinting. Underpinned by the PyEvolve toolkit, Vestige provides a framework for visualising the signatures of evolutionary processes across the genome of numerous organisms simultaneously. By exploiting the maximum-likelihood statistical framework, the complex interplay between mutational processes, DNA repair and selection can be evaluated both spatially (along a sequence alignment) and temporally (for each branch of the tree) providing visual indicators to the attributes and functions of DNA sequences.

Dataset Information

Discovering transcriptional regulatory regions in Drosophila by a nonalignment method for phylogenetic footprinting.

Publications

Discovering transcriptional regulatory regions in Drosophila by a nonalignment method for phylogenetic footprinting.

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