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.

doi:10.1101/2024.08.16.608331

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bioRxiv · 10.1101/2024.08.16.608331

DeepSomatic: Accurate somatic small variant discovery for multiple sequencing technologies

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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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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.. 2024-08-19. DeepSomatic: Accurate somatic small variant discovery for multiple sequencing technologies. https://doi.org/10.1101/2024.08.16.608331

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