A-G.23: Mapping and improving the limited sensitivity of SigProfilerAssignment to flat mutational signatures
Maria Katsantoni
University of Bern
Matus Medo
University of Bern
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SigProfilerAssignment is a popular and well-performing tool for estimating the activity of mutational signatures in sequenced samples. Although signature analysis generally becomes more accurate with increasing mutation burden in the analyzed samples, we report that this is not always the case for SigProfilerAssignment. In particular, the tool's sensitivity deteriorates in the range of mutation burden typical for whole-genome sequencing when multiple signatures with flat mutational profiles are simultaneously active. We propose a modified algorithm, FSPA, that achieves a higher sensitivity without sacrificing the high precision of SigProfilerAssignment. This modification enhances the reliability of mutational signature analysis in cancer genome studies, thereby ensuring more accurate downstream biological interpretations.
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