Positional weight matrices have sufficient prediction power for analysis of noncoding variants

F1000Res. 2022 Jan 12:11:33. doi: 10.12688/f1000research.75471.3. eCollection 2022.


The position weight matrix, also called the position-specific scoring matrix, is the commonly accepted model to quantify the specificity of transcription factor binding to DNA. Position weight matrices are used in thousands of projects and software tools in regulatory genomics, including computational prediction of the regulatory impact of single-nucleotide variants. Yet, recently Yan et al. reported that "the position weight matrices of most transcription factors lack sufficient predictive power" if applied to the analysis of regulatory variants studied with a newly developed experimental method, SNP-SELEX. Here, we re-analyze the rich experimental dataset obtained by Yan et al. and show that appropriately selected position weight matrices in fact can adequately quantify transcription factor binding to alternative alleles.

Keywords: PSSM; PWM; SNP-SELEX; TF-DNA binding; Transcriptional regulation; rSNP.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Binding Sites / genetics
  • Position-Specific Scoring Matrices
  • Protein Binding
  • Software*
  • Transcription Factors* / genetics
  • Transcription Factors* / metabolism


  • Transcription Factors

Associated data

  • figshare/10.6084/m9.figshare.16906789.v1

Grants and funding

This study was supported by the Russian Science Foundation (RSF) grant 20-74-10075 to IVK.