MIMOSA: A model-independent framework for transcription factor binding site motif similarity assessment

Abstract

Transcription factors (TFs) regulate gene expression by binding specific DNA sequences, called transcription factor binding sites (TFBSs), and motifs summarize the sequence specificity of these interactions. Although the position weight matrix (PWM) remains the most widely used motif model, alternative models can capture dependencies between nucleotide positions. Available tools for motif comparison are designed only for PWM motifs, and converting a motif from an alternative model into a PWM often leads to a loss of information. We propose MIMoSA (Model-Independent Motif Similarity Assessment), a tool that compares motif models independently of their representation. MIMoSA compares recognition profiles produced by different motifs on the same DNA sequence set rather than their internal parameters. Comparison of MIMoSA with PWM-based tools TomTom and MACRO-APE with the HOCOMOCO motif collection ensured comparable performance of all tools. A case study of a ChIPseq dataset for ATF3 TF further supported the reliability of MIMoSA application. The tool is available at \url{https://github.com/ubercomrade/mimosa}.