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.

翻译：基序发现是计算生物学中的一个核心问题，传统上被表述为一种似然优化任务，旨在从DNA序列数据集中返回单个主导基序。然而，调控序列数据允许多种合理的基序解释，这反映了潜在的生物学异质性。在本工作中，我们将基序发现构建为一个质量-多样性优化问题，并应用MAP-Elites算法，在基于似然的适应度目标下进化位置权重矩阵基序，同时明确地在具有生物学意义的维度上保持多样性。我们使用三种互补的行为特征来评估MAP-Elites，这些特征捕捉了基序特异性、组成结构、覆盖度和鲁棒性之间的权衡。在人类CTCF肝脏ChIP-seq数据（比对至人类参考基因组）上的实验，将MAP-Elites与标准基序发现工具MEME进行了比较，评估基于分层数据集子集在匹配的评估标准下进行。结果表明，MAP-Elites能够恢复多种高质量基序变体，其适应度与MEME的最优解相当，同时揭示了被单解方法所掩盖的结构化多样性。