Partitioned multi-MUM finding for scalable pangenomics with MumemtoM
Abstract
Pangenome collections are growing to hundreds of high-quality genomes. This necessitates scalable methods for constructing pangenome alignments that can incorporate newly sequenced assemblies. We previously developed Mumemto, which computes maximal unique matches (multi-MUMs) across pangenomes using compressed indexing. In this work, we introduce MumemtoM (Mumemto Merge), comprising two new partitioning and merging strategies. Both strategies enable highly parallel, memory-efficient, and updateable computation of multi-MUMs. One of the strategies, called string-based merging, is also capable of conducting the merges in a way that follows the shape of a phylogenetic tree, naturally yielding the multi-MUM for the tree’s internal nodes as well as the root. With these strategies, Mumemto now scales to 474 human haplotypes, the only multi-MUM method able to do so. It also introduces a time–memory tradeoff that allows Mumemto to be tailored to more scenarios, including in resource-limited settings.
Footnotes
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[Supplemental material is available for this article.]
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Article published online before print. Article, supplemental material, and publication date are at https://www.genome.org/cgi/doi/10.1101/gr.280940.125.
- Received May 16, 2025.
- Accepted November 5, 2025.
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