MIMOSA: A model-independent framework for comparing transcription factor binding site motifs
Year: 2026
Type: preprint
Abstract: Motivation Transcription factors (TFs) regulate gene expression by binding specific DNA sequences, which are commonly represented by motif models. Although position weight matrices (PWMs) remain the dominant motif representation, alternative models, such as Markov models, can capture interpositional dependencies and may provide higher predictive performance. Existing motif comparison tools, however, are designed mainly for PWMs or require reducing motifs to position weight matrices or position frequency matrices. This limits cross-model motif comparison, complicates interpretation of de novo motif discovery results, and hinders integration of diverse motif models into genomic analyses. Results We present MIMOSA (Model-Independent Motif Similarity Assessment), a model-independent framework for direct comparison of TF binding site (TFBS) motifs regardless of their mathematical representation. MIMOSA assesses motif similarity by comparing calibrated recognition profiles produced by different motif models on the same DNA sequence set, rather than by comparing motif parameters. In a cross-database benchmark on HOCOMOCO motifs, MIMOSA achieved retrieval performance comparable to established PWM-oriented tools, including Tomtom and MACRO-APE, with mean reciprocal rank (MRR) and Recall@ k close to the best-performing methods. Pairwise ranking comparisons showed that MIMOSA captures a similarity signal consistent with existing approaches while providing a model-independent comparison strategy. Applying MIMOSA to de novo ATF3 ChIP-seq motifs showed that recognition-profile comparison distinguished alternative spacer variants represented as separate PWMs from their integration within more flexible models such as BaMM and Slim. Thus, MIMOSA enables quantitative cross-model motif comparison and supports interpretation of motif heterogeneity in TFBS analyses. Availability and implementation MIMOSA is implemented in Python and is freely available at https://github.com/ubercomrade/mimosa .
Authors: Anton V. Tsukanov, Victor G. Levitsky
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
Funder: Russian Science Foundation
OpenAlex ID: W7161551775