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Bioinformatics Advance Access originally published online on April 28, 2009
Bioinformatics 2009 25(13):1617-1624; doi:10.1093/bioinformatics/btp282
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© 2009 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Affinity Density: a novel genomic approach to the identification of transcription factor regulatory targets

Dennis J. Hazelett 1,*, Daniel L. Lakeland 2 and Joseph B. Weiss 3

1Integrative Biosciences, Oregon Health and Science University (OHSU), 611 SW Campus Dr SOD705, Portland, OR 97239, 2Independent Consultant, Los Angeles, CA and 3Department of Cardiology, Oregon Health and Science University (OHSU), 3303 SW Bond Ave CHH14W, Portland, OR 97239, USA

*To whom correspondence should be addressed.


  Abstract

Methods: A new method was developed for identifying novel transcription factor regulatory targets based on calculating Local Affinity Density. Techniques from the signal-processing field were used, in particular the Hann digital filter, to calculate the relative binding affinity of different regions based on previously published in vitro binding data. To illustrate this approach, the complete genomes of Drosophila melanogaster and D.pseudoobscura were analyzed for binding sites of the homeodomain proteinc Tinman, an essential heart development gene in both Drosophila and Mouse. The significant binding regions were identified relative to genomic background and assigned to putative target genes. Valid candidates common to both species of Drosophila were selected as a test of conservation.

Results: The new method was more sensitive than cluster searches for conserved binding motifs with respect to positive identification of known Tinman targets. Our Local Affinity Density method also identified a significantly greater proportion of Tinman-coexpressed genes than equivalent, optimized cluster searching. In addition, this new method predicted a significantly greater than expected number of genes with previously published RNAi phenotypes in the heart.

Availability: Algorithms were implemented in Python, LISP, R and maxima, using MySQL to access locally mirrored sequence data from Ensembl (D.melanogaster release 4.3) and flybase (D.pseudoobscura). All code is licensed under GPL and freely available at http://www.ohsu.edu/cellbio/dev_biol_prog/affinitydensity/.

Contact: hazelett{at}ohsu.edu

Associate Editor: Alex Bateman

Received on February 6, 2009; revised on April 21, 2009; accepted on April 22, 2009

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