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bioRxiv · 10.1101/2025.09.03.674039

Reference-based variant detection with varseek

Rich, J. M.·Luebbert, L.·Sullivan, D. K.·Rosa, R.·Pachter, L.
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Abstract

Variant detection from sequencing data is fundamental for genomics and is the first step in a wide range of applications, ranging from genome-wide association studies to disease diagnosis. Widely used tools for variant detection utilize a de novo approach that is based on a combination of read mapping algorithms and statistical methods for identifying genetic variation from error-prone sequencing data. This approach has been successful, although the detection of insertion and deletion variants, as well as the detection of variants from low-coverage data, remain challenging problems. We introduce varseek, a reference-based approach to variant detection that provides large improvements in performance in these challenging cases. The varseek approach utilizes a k-mer pseudoalignment approach, which provides the ability to identify variants at single-cell resolution in single-cell transcriptomics data. We showcase the versatility and performance of varseek for detecting tumor-specific COSMIC variants in glioblastoma single-cell sequencing.

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Rich, J. M., Luebbert, L., Sullivan, D. K., Rosa, R., Pachter, L.. 2025-09-05. Reference-based variant detection with varseek. https://doi.org/10.1101/2025.09.03.674039

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