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Functional characterization of transcription factor motifs using cross-species comparison across large evolutionary distances

Kim, Jaebum
Cunningham, Ryan
James, Brian
Gibson, J. D.
Niehuis, Oliver
Robertson, H. M.
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Published in PLoS computational biology. 2010, vol. 6, no. 1, e1000652
Abstract We address the problem of finding statistically significant associations between cis-regulatory motifs and functional gene sets, in order to understand the biological roles of transcription factors. We develop a computational framework for this task, whose features include a new statistical score for motif scanning, the use of different scores for predicting targets of different motifs, and new ways to deal with redundancies among significant motif-function associations. This framework is applied to the recently sequenced genome of the jewel wasp, Nasonia vitripennis, making use of the existing knowledge of motifs and gene annotations in another insect genome, that of the fruitfly. The framework uses cross-species comparison to improve the specificity of its predictions, and does so without relying upon non-coding sequence alignment. It is therefore well suited for comparative genomics across large evolutionary divergences, where existing alignment-based methods are not applicable. We also apply the framework to find motifs associated with socially regulated gene sets in the honeybee, Apis mellifera, using comparisons with Nasonia, a solitary species, to identify honeybee-specific associations.
Keywords Amino Acid MotifsAmino Acid SequenceAnimalsBees/geneticsBehavior, AnimalComputational Biology/ methodsConserved Sequence/genetics/physiologyDrosophila melanogaster/geneticsGenome, InsectInsect Proteins/geneticsModels, GeneticMolecular Sequence DataSpecies SpecificityTranscription Factors/genetics/physiologyWasps/genetics
PMID: 20126523
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Research group Génomique Evolutionnaire Computationnelle (830)
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KIM, Jaebum et al. Functional characterization of transcription factor motifs using cross-species comparison across large evolutionary distances. In: PLoS computational biology, 2010, vol. 6, n° 1, p. e1000652. doi: 10.1371/journal.pcbi.1000652 https://archive-ouverte.unige.ch/unige:9210

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Deposited on : 2010-07-12

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