RT Journal
A1 Shivakumar, Vikram S.
A1 Langmead, Ben
T1 Partitioned multi-MUM finding for scalable pangenomics with MumemtoM
JF Genome Research 
JO Genome Research 
YR 2026 
FD February 01 
VO 36 
IS 2 
SP 397 
OP 404 
DO 10.1101/gr.280940.125 
UL http://genome.cshlp.org/content/36/2/397.abstract 
AB 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.