DeepSomatic: Accurate somatic small variant discovery for multiple sequencing technologies
Park, J.; Cook, D. E.; Chang, P.-C.; Kolesnikov, A.; Brambrink, L.; Mier, J. C.; Gardner, J.; McNulty, B.; Sacco, S.; Keskus, A.; Bryant, A.; Ahmad, T.; Shetty, J.; Zhao, Y.; Tran, B.; Narzisi, G.; Helland, A.; Yoo, B.; Pushel, I.; Lansdon, L. A.; Bi, C.; Walter, A.; Gibson, M.; Pastinen, T.; Farooqi, M. S.; Robine, N.; Miga, K. H.; Carroll, A.; Kolmogorov, M.; Paten, B.; Shafin, K.
Show abstract
Somatic variant detection is an integral part of cancer genomics analysis. While most methods have focused on short-read sequencing, long-read technologies now offer potential advantages in terms of repeat mapping and variant phasing. We present DeepSomatic, a deep learning method for detecting somatic SNVs and insertions and deletions (indels) from both short-read and long-read data, with modes for whole-genome and exome sequencing, and able to run on tumor-normal, tumor-only, and with FFPE-prepared samples. To help address the dearth of publicly available training and benchmarking data for somatic variant detection, we generated and make openly available a dataset of five matched tumor-normal cell line pairs sequenced with Illumina, PacBio HiFi, and Oxford Nanopore Technologies, along with benchmark variant sets. Across samples and technologies (short-read and long-read), DeepSomatic consistently outperforms existing callers, particularly for indels.
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