We use statistical modeling and algorithms to develop methods to analyze large biological datasets. Particularly, we develop scalable algorithms for high-throughput genomic and transcriptomic sequencing data to study problems related to genome assembly, structural variation detection, and transcriptome analysis. While our lab has a strong theoretical component, we emphasize the applicability of methods and models to relevant biological and biomedical questions. The applicability of our algorithms has been demonstrated through integration in official bioinformatic pipelines offered by leading sequencing companies. We have both academic and industrial collaborations.
Kristoffer Sahlin, PI
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