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

Abstract

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.

A statistical approach to identify regulatory DNA variations

“…Therefore, we filtered the SNPs for those which have an oligonucleotide binding score p-value < 0.05 and a preferential binding score p-value < 0.01, as proposed by the authors, resulting in 9.840 SNPs for 129 TFs. We gathered the TF motifs from Boytsov et al [9], which provide optimized PWM motifs for the SNP-SELEX data set. As for the ASB data set we excluded for each TF the SNPs without a TFBS for at least one of the two alleles.…”