Similarity Regression predicts evolution of transcription factor sequence specificity

Lambert, Samuel A, Yang, Ally, Sasse, Alexander, Cowley, Gwendolyn ORCID: 0000-0002-8505-1354, Albu, Mihai, Caddick, Mark X, Morris, Quaid D, Weirauch, Matthew T and Hughes, Timothy R
(2019) Similarity Regression predicts evolution of transcription factor sequence specificity. Nature Genetics.

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Transcription factor (TF) binding specificities (motifs) are essential to the analysis of noncoding DNA and gene regulation. Accurate prediction of the sequence specificities of TFs 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.

Item Type: Article
Depositing User: Symplectic Admin
Date Deposited: 12 Mar 2019 10:36
Last Modified: 14 Jan 2020 08:50

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