Somatic variant detection is an integral part of cancer genomics analysis. While most methods have focused on short-read sequencing, long-read technologies offer potential advantages in repeat mapping and variant phasing.
Somatic variant detection is an integral part of cancer genomics analysis. While most methods have focused on short-read sequencing, long-read technologies offer potential advantages in repeat mapping and variant phasing.
We present DeepSomatic, a deep-learning method for detecting somatic small nucleotide variations and insertions and deletions from both short-read and long-read data. The method has modes for whole-genome and whole-exome sequencing and can run on tumor–normal, tumor-only and formalin-fixed paraffin-embedded samples. To train DeepSomatic and help address the dearth of publicly available training and benchmarking data for somatic variant detection, we generated and make openly available the Cancer Standards Long-read Evaluation dataset of six matched tumor–normal cell line pairs whole-genome sequenced with Illumina, PacBio HiFi and Oxford Nanopore Technologies, along with benchmark variant sets. Across samples, both cell line and patient-derived, and across short-read and long-read sequencing technologies, DeepSomatic consistently outperforms existing callers.Fig. 2: Six tumor–normal cell lines sequenced with multiple technologies.Fig. 4: Somatic variant calling on glioblastoma and pediatric blood cancer tumor samples.). CASTLE benchmarking sets derived from DeepSomatic and aligned reads used for training and evaluation are available in the Google Cloud at https://console.cloud.google.com/storage/browser/brain-genomics-public/publications/park2024_deepsomatichttps://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/seqc/Somatic_Mutation_WG/data/WGS/Google ScholarGarcia-Prieto, C. A., Martínez-Jiménez, F., Valencia, A. & Porta-Pardo, E. Detection of oncogenic and clinically actionable mutations in cancer genomes critically depends on variant calling tools.Google ScholarMusunuri, R. L. et al. Lancet2: improved and accelerated somatic variant calling with joint multi-sample local assembly graphs. Preprint at Fang, L. T. et al. Establishing community reference samples, data and call sets for benchmarking cancer mutation detection using whole-genome sequencing.Damaraju, N., Miller, A. L. & Miller, D. E. Long-read DNA and RNA sequencing to streamline clinical genetic testing and reduce barriers to comprehensive genetic testing.Google ScholarLansdon, L. A. et al. Successful classification of clinical pediatric leukemia genetic subtypes via structural variant detection using HiFi long-read sequencing. Preprint atHCC1395-HCC1395BL ONT sequencing was supported by the National Cancer Institute of the NIH under grant award number U01CA253405. M.S.F. reports grants from Braden’s Hope for Childhood Cancer, Big Slick, Black & Veatch Foundation, Masonic Cancer Alliance, Noah’s Bandage Project, Elizabeth and Monte McDowell, Cancer Center Auxiliary, and the Department of Defense . B.P. and J.P. were supported by NIH grant nos. R01HG010485, U41HG010972, U24HG011853 and OT2OD033761. M.K., A.G.K., A.B. and T.A. were supported by the Intramural Research Program of the NIH. The contributions of the NIH authors were made as part of their official duties as NIH federal employees, are in compliance with agency policy requirements and are considered Works of the United States Government. However, the findings and conclusions presented in this paper are those of the authors and do not necessarily reflect the views of the NIH or the US Department of Health and Human Services. This work was supported by grant award number HT9425-23-1-0844 from the Congressionally Directed Medical Research Programs . This research includes work performed in TGen’s Collaborative Sequencing Center, a City of Hope Comprehensive Cancer Center supported shared resource .UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA, USADaniel E. Cook, Pi-Chuan Chang, Alexey Kolesnikov, Lucas Brambrink, Juan Carlos Mier, Andrew Carroll & Kishwar ShafinAyse G. Keskus, Asher Bryant, Tanveer Ahmad & Mikhail KolmogorovGiuseppe Narzisi, Adrienne Helland & Nicolas Robine Children’s Mercy Hospital, University of Missouri-Kansas City School of Medicine, Kansas City, MO, USA Byunggil Yoo, Irina Pushel, Lisa A. Lansdon, Chengpeng Bi, Adam Walter, Margaret Gibson, Tomi Pastinen & Midhat S. FarooqiRebecca Reiman, Sharvari Mankame, T. Rhyker Ranallo-Benavidez & Floris P. Barthel B.P., K.S. and M.K. helped conceive and direct the study. J.P. performed data analysis. D.E.C., P.-C.C., A. Kolesnikov, L.B., J.C.M., A.C. and K.S. contributed to DeepSomatic development. J.P., A.B. and A. Keskus performed cell line data processing. J.G., B.M. and K.H.M. contributed to ONT data sequencing. S.S. performed Hi-C data sequencing. M.K., A. Keskus, A.B. and T.A. contributed to Severus development. J.S., Y.Z. and B.T. performed Illumina data sequencing. G.N., A.H. and N.R. performed ONT sequencing of the HCC1395 cell line. B.Y., I.P., L.A.L., C. Bi, A.W., M.G., T.P. and M.S.F. performed PacBio data sequencing. F.P.B., R.R., S.M., T.R.R.-B. and C. Brown performed glioblastoma sample coordination and sequencing. J.P., B.P., D.E.C., K.S. and M.K. drafted the paper.K.S., D.E.C., P.-C.C., A. Kolesnikov, L.B., J.C.M. and A.C. are employees of Google LLC and own Alphabet stock as part of the standard compensation package. M.S.F. is a part of the speakers bureau for Bayer and PacBio. The remaining authors declare no competing interests.thanks Qian Liu, Kai Ye and the other, anonymous, reviewer for their contribution to the peer review of this work.Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author or other rightsholder; author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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