Published online 3 January 2007 Nucleic Acids Research, 2007, Vol. 35, No. 3 e20 doi:10.1093/nar/gkl1062 Positional clustering improves computational binding site detection and identifies novel cis-regulatory sites in mammalian GABAA receptor subunit genes Timothy E. Reddy 1 , Boris E. Shakhnovich 1 , Daniel S. Roberts2,3, Shelley J. Russek2 and Charles DeLisi1,2,4,* 1Bioinformatics Program, Boston University, 24 Cummington Street, Boston, MA 02215, USA, 2Laboratory of Molecular Neurobiology, Department of Pharmacology and Experimental Therapeutics, Boston University School of Medicine, 715 Albany St., Boston, MA 02118, USA, 3Program in BioMedical Neuroscience and 4Biomedical Engineering, Boston University, 44 Cummington Street, Boston, MA 02215, USA Received June 13, 2006; Revised October 18, 2006; Accepted November 20, 2006 ABSTRACT Understanding transcription factor (TF) mediated control of gene expression remains a major chal- lenge at the interface of computational and experi- mental biology. Computational techniques predicting TF-binding site specificity are frequently unreliable. On the other hand, comprehensive experimental validation is difficult and time consuming. We intro- duce a simple strategy that dramatically improves robustness and accuracy of computational binding site prediction. First, we evaluate the rate of recur- rence of computational TFBS predictions by com- monly used sampling procedures. We find that the vast majority of results are biologically meaningless. However clustering results based on nucleotide position improves predictive power. Additionally, we find that positional clustering increases robustness to long or imperfectly selected input sequences. Positional clustering can also be used as a mecha- nism to integrate results from multiple sampling approaches for improvements in accuracy over each one alone. Finally, we predict and validate regulatory sequences partially responsible for transcriptional control of the mammalian type A g-aminobutyric acid receptor (GABAAR) subunit genes. Positional cluster- ing is useful for improving computational binding site predictions, with potential application to improving our understanding of mammalian gene expression. In particular, predicted regulatory mechanisms in the mammalian GABAAR subunit gene family may open new avenues of research towards understanding this pharmacologically important neurotransmitter receptor system. INTRODUCTION Co-regulation is a basic mechanism to coordinately control expression of genes in modules, biochemical pathways and protein complexes (1–3). In eukaryotes, expression is most often mediated by transcription factors (TFs) that bind upstream of the transcription start site (TSS) and recruit the polymerase assembly (4). TFs bind, with varying afﬁnity, to a set of similar, short ( 6–20 nt) sequences (5). Computational binding site discovery focuses on ﬁnding signiﬁcantly over- represented sequences in upstream regions of co-regulated genes (6–8). Thus, computational TFBS prediction algorithms must begin with an input set of promoters from genes hypo- thetically co-regulated by a shared TF. The algorithms aim to predict the binding positions and consequently the nucleot- ide speciﬁcity of the TF (9–11). The ﬁrst part of transcription factor binding site (TFBS) discovery, the input set, can be identiﬁed using either compu- tational or experimental methods. Experimental techniques, such as chromatin immunoprecipitation (ChIP) (12), have been successfully used to generate a genome scale mapping of approximate TF-binding positions (10,13,14). Computa- tional techniques, such as phylogenetic proﬁling (15,16) and artiﬁcial neural networks, can also be used to identify sets of co-regulated genes. Both experimental and computa- tional approaches, however, suffer from a signiﬁcant false positive (FP) prediction rate. Inclusion of extraneous promot- ers in the input sets dilutes the enrichment of the shared TFBS sequences making computational TFBS discovery signiﬁcantly more challenging (17). We term such erro- neously included promoters decoy sequences (DSs). *To whom correspondence should be addressed. Tel: +1 617 353 1122; Fax: +1 617 353 3333; Email: delisi@bu.edu The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors 2006 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.