Similarity Regression predicts evolution of transcription factor sequence specificity

Transcription factor (TF) binding specificities (motifs) are essential to the analysis of noncoding DNA and gene regulation. Accurate prediction of TF sequence specificities is critical, because the hundreds of sequenced eukaryotic genomes encompass hundreds of thousands of TFs, and assaying each is currently infeasible. There is ongoing controversy regarding the efficacy of motif prediction methods, as well as the degree of motif diversification among related species. Here, we describe Similarity Regression (SR), a significantly improved method for predicting motifs. We have updated and expanded the Cis-BP database using SR, and validate its predictive capacity with new data from diverse eukaryotic TFs. SR inherently quantifies TF motif evolution, and we show that previous claims of near-complete conservation of motifs between human and Drosophila are grossly inflated, with nearly half the motifs in each species absent from the other. We conclude that diversification in DNA binding motifs is pervasive, and present a new tool and updated resource to study TF diversity and gene regulation across eukaryotes. Protein binding microarray (PBM) experiments were performed for a set of 341 diverse eukaryotic transcription factors. Briefly, the PBMs involved binding GST-tagged DNA-binding proteins to two double-stranded 44K Agilent microarrays, each containing a different DeBruijn sequence design, in order to determine their sequence preferences. Details of the PBM protocol are described in Berger et al., Nature Biotechnology 2006.