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

2026-01-25

AI Summary

This work reframes motif discovery as a quality-diversity optimization problem, addressing the limitation of traditional methods that typically return only a single dominant motif and thus fail to capture the biologically plausible heterogeneity inherent in regulatory sequences. By adapting the MAP-Elites evolutionary algorithm, the approach explicitly maintains motif diversity along multiple behavioral dimensions—including specificity, compositional structure, coverage, and robustness—while preserving high likelihood fit. Leveraging position weight matrix modeling and multidimensional behavioral descriptors, the method not only recovers high-quality motifs comparable to those identified by MEME in human liver CTCF ChIP-seq data but also uncovers multiple biologically meaningful motif variants, thereby overcoming the structural diversity obscured by conventional single-solution approaches.

Technology Category

Search and Optimization: Evolutionary ComputationConstraint Satisfaction and Optimization: Constraint Learning and AcquisitionMachine Learning: Learning Preferences or Rankings

Application Category

Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphsWeb Mining and Content Analysis: Robustness and generalizability of Web mining methodsEconomics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

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.

Problem

• motif discovery
• biological heterogeneity
• ChIP-seq
• quality-diversity
• computational biology

Innovation

• motif discovery
• quality-diversity optimization
• MAP-Elites
• ChIP-seq
• position weight matrix