Motif Diversity in Human Liver ChIP-seq Data Using MAP-Elites

• URL: http://arxiv.org/abs/2601.17808v1
• Date: Sun, 25 Jan 2026 11:57:54 GMT
• Title: Motif Diversity in Human Liver ChIP-seq Data Using MAP-Elites
• Authors: Alejandro Medina, Mary Lauren Benton,
• Abstract summary: We apply the MAP-Elites algorithm to evolve position weight matrix motifs under a likelihood-based fitness objective.<n>Results show that MAP-Elites recovers multiple high-quality motif variants with fitness comparable to MEME's strongest solutions.
• Score: 45.88028371034407
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Motif discovery is a core problem in computational biology, traditionally formulated as a likelihood optimization task that returns a single dominant motif from a DNA sequence dataset. However, regulatory sequence data admit multiple plausible motif explanations, reflecting underlying biological heterogeneity. In this work, we frame motif discovery as a quality-diversity problem and apply the MAP-Elites algorithm to evolve position weight matrix motifs under a likelihood-based fitness objective while explicitly preserving diversity across biologically meaningful dimensions. We evaluate MAP-Elites using three complementary behavioral characterizations that capture trade-offs between motif specificity, compositional structure, coverage, and robustness. Experiments on human CTCF liver ChIP-seq data aligned to the human reference genome compare MAP-Elites against a standard motif discovery tool, MEME, under matched evaluation criteria across stratified dataset subsets. Results show that MAP-Elites recovers multiple high-quality motif variants with fitness comparable to MEME's strongest solutions while revealing structured diversity obscured by single-solution approaches.

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