Abstract
Microsatellites are abundant genomic elements that contribute to genetic diversity and disease-associated regulatory variation. Although long-read sequencing enables accurate resolution of repetitive regions, computational methods for fully resolved microsatellite genotyping remain limited. Here, we introduce variant motif where (vmwhere), a computational framework for identifying, genotyping, decomposing, and visualizing complex tetrameric microsatellites from long-read sequencing data. Using simulated error-free reads, vmwhere accurately measures several genotyping metrics, including allele length, repeat length, maximum consecutive repeat length, and motif density. Applied to long-read whole-genome sequencing data, vmwhere identified sequence interruptions, motif-specific differences in repeat architecture, and ancestry-associated allele variation, including long repeat alleles that exceed short-read sequencing limitations. We applied vmwhere to GGAA microsatellites in Ewing sarcoma, an aggressive pediatric cancer driven by EWS-FLI1 fusion oncoprotein, which binds to microsatellites and remodels chromatin. Genome-wide integration of long-read-defined microsatellite architecture with chromatin accessibility and EWS-FLI1 binding revealed that GGAA repeat structure was associated with chromatin state, with longer consecutive repeat microsatellites exhibiting increased EWS-FLI1 binding and chromatin accessibility. Cell line-specific expansions and contractions of GGAA microsatellite repeat length were associated with gains and losses of chromatin accessibility. Further, we identified haplotype-specific chromatin states, with preferential binding and accessibility at longer alleles. Together, these results establish vmwhere as a scalable framework for resolving population-level microsatellite variation and linking repeat architecture to chromatin state. Repeat structure and length characteristics provides insights into genotype-function relationships at microsatellite repeats in cancer.