Hunting for microsatellite instability in long-read data with Owl

Background: Microsatellite instability (MSI) refers to the accumulation of somatic mutations in simple repeat regions and is a hallmark of mismatch repair deficiency as well as a predictive biomarker for immunotherapy response. Most existing MSI callers are built for short-read sequencing and typically require paired tumor-normal data or high sequencing depth. These methods quantify repeat-length variability and classify genomes as MSI high when 10-30 percent of markers are unstable. Long-read sequencing (LRS) enables haplotype-resolved repeat profiling, allowing accurate MSI detection from tumor-only genomes at standard (~30×) coverage. Methods: We developed Owl, a MSI detection software tool for PacBio HiFi data. Owl interrogates more than 160,000 simple repeats using a wrap-around dynamic programming algorithm and calculates the coefficient of variation (CV) in repeat length across phased haplotypes. Genome-wide MSI scores are then reported as the fraction of markers exceeding a parametrically derived CV threshold (CV > 5). Results: We first applied Owl to 131 healthy controls from the Human Pangenome Reference Consortium and observed that MSI-stable genomes typically fall between 2-6%. We then profiled 26 additional cancer genomes, all of which had Owl scores between 2-3%, consistent with microsatellite-stable profiles. Finally, we identified five MSI-high samples that exceeded our 10% threshold, with scores ranging from 15-18%. These included two gastric cancers, an astrocytoma sample, and two Ewing sarcoma cell lines. Only one sample had both HiFi and Illumina data available for comparison; in that astrocytoma sample, Owl (17.1%) and Illumina DRAGEN (20.0%) produced concordant MSI-high classifications.By measuring MSI at >160,000 repeats, we can also detect motif-specific instability in tumor samples. For example, the two Ewing sarcoma cell lines (TC32 and CHLA10) showed a two fold increase of GGAA motif instability (23-26% MSI) compared to other motifs, an interesting and potentially disease-relevant pattern. The EWS::FLI1 fusion is known to bind GGAA-rich regulatory elements, and the observed instability at these motifs suggests that repeat variation itself may play an important role in Ewing sarcoma biology. Conclusions: Owl enables robust, quantitative detection of MSI from long-read whole-genome data, requiring only tumor samples. Integrated into the PacBio HiFi Somatic workflow, Owl extends MSI profiling to long-read sequencing, revealing motif-specific instability patterns not captured by short-read approaches.