Similarity regression predicts evolution of transcription factor sequence specificity

• Published in: Nature genetics Vol. 51; no. 6; pp. 981 - 989
• Main Authors: Lambert, Samuel A., Yang, Ally W. H., Sasse, Alexander, Cowley, Gwendolyn, Albu, Mihai, Caddick, Mark X., Morris, Quaid D., Weirauch, Matthew T., Hughes, Timothy R.
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.06.2019; Nature Publishing Group
• Subjects: 45; 631/114; 631/1647/48; 631/208/191; 631/208/200; 64; 82; 82/80; Agriculture; Amino acids; Analysis; Animal Genetics and Genomics; Binding; Binding sites; Biomedical and Life Sciences; Biomedicine; Cancer Research; Deoxyribonucleic acid; Divergence; Diversification; DNA; DNA methylation; DNA-ligand interactions; Eukaryotes; Evolution; Fruit flies; Gene expression; Gene Function; Gene regulation; Generalized linear models; Genetic regulation; Genomes; Human Genetics; Insects; Predictions; Proteins; Regression analysis; Similarity; Transcription factors; Zinc finger proteins
• ISSN: 1061-4036; 1546-1718
• DOI: 10.1038/s41588-019-0411-1

Transcription factor (TF) binding specificities (motifs) are essential for the analysis of gene regulation. Accurate prediction of TF motifs is critical, because it is infeasible to assay all TFs in all sequenced eukaryotic genomes. There is ongoing controversy regarding the degree of motif diversification among related species that is, in part, because of uncertainty in motif prediction methods. Here we describe similarity regression, a significantly improved method for predicting motifs, which we use to update and expand the Cis-BP database. Similarity regression inherently quantifies TF motif evolution, and shows that previous claims of near-complete conservation of motifs between human and Drosophila are inflated, with nearly half of the motifs in each species absent from the other, largely due to extensive divergence in C2H2 zinc finger proteins. 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. Similarity regression is an improved method for predicting transcription factor motifs, enabling analysis of DNA-binding motifs across eukaryotes and an expansion of the Cis-BP database of measured and predicted transcription factor motifs.