Similarity regression predicts evolution of transcription factor sequence specificity

Nat Genet. 2019 Jun;51(6):981-989. doi: 10.1038/s41588-019-0411-1. Epub 2019 May 27.


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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Base Sequence*
  • Binding Sites*
  • Computational Biology / methods
  • Conserved Sequence
  • Databases, Genetic
  • Evolution, Molecular*
  • Gene Expression Regulation
  • Humans
  • Nucleotide Motifs
  • Protein Binding
  • Transcription Factors / metabolism*


  • Transcription Factors