Hazelett DJ, Lakeland DL, and Weiss, JB. "Affinity Density: A Novel Approach to Identification of Transcription Factor Regulatory Targets."
gzipped tar archive (v1.0-RC4)
This website provides code and information in support of the above mentioned paper which has been accepted at Bioinformatics. A pre-print version of the manuscript is available here. Please see the journal for the final version.
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 protein 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.
A visual map of Tinman binding site density across the entire fly genome, with associated loci.
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 from this web page.
post-print version available for advance access online at Bioinformatics (Oxford Press).
Dennis Hazelett
Last modified: Wed Apr 29 12:09:58 PDT 2009