Peer-reviewedCC BYCell
Comprehensive benchmarking of somatic mutation detection by the SMaHT Network
Abstract
Somatic mosaicism is increasingly recognized as a fundamental feature of human biology, yet the detection of somatic mutations remains challenging. The Somatic Mosaicism across Human Tissues (SMaHT) Network conducted large-scale benchmarking experiments involving cell lines and donor tissues to evaluate sequencing technologies, experimental approaches, and computational methods for detecting different types of somatic mutations, generating community resources with >1,000× short-read and 100-400× long-read data for nine analyzed samples. We determined effective strategies for utilizing short- and long-read sequencing for mutation detection and demonstrated that using donor-specific and pangenome-inferred diploid assemblies improved calling, extending mutation catalogs to challenging genomic regions. We benchmarked six duplex technologies and showed that single-cell sequencing resolves cell-type-specific mutational patterns and heterogeneity. Our results indicate that bulk, single-cell, and duplex analyses are complementary and that leveraging all three provides comprehensive characterization of mosaicism within tissues. Together, these findings provide the scientific and clinical communities with a roadmap for accurate genome-wide somatic mutation analysis.
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BibTeX
@article{abyzov2026comprehensive,
title = {{Comprehensive benchmarking of somatic mutation detection by the SMaHT Network}},
author = {Alexej Abyzov and Abhiram Natu and Adam Voshall and Adriana Estela Sedeño-Cortés and Aidan Hugh Burn and Alan P. Boyle and Alexander D. Veit and Alexander Eckehart Urban and Alexandre Pellan Cheng and Alexi Runnels and Alisa Mo and Amooyla Srinivasa and Amy Leonardson and Andrea J. Kriz and Andrew B. Stergachis and Andrew J. C. Russell and Andrew J. Schroeder and Andrew R. D'Avino and Andrew Ruttenberg and Ann Caplin and Anna Minkina and Areum Cho and Arijit Panda and Aristotelis Tsirigos and Arnaz Maryam Tariq and Ayush Semwal and Azeet Narayan and Azra Hasan and Benjamin Costa and Benjamin K. Johnson and Benno Orr and Benpeng Miao and Bianca Morris and Bo Zhou and Boxun Zhao and Brad Demarest and B Bernstein and Brandt A. Bessell and Brianne Carroll and Brianne E. Livermore and Caitlin N. Jacques and Camille Mumm and Carlos Mendez‐Dorantes and Carolin Purman and Carrie Cibulskis and Casey Andrews and Catherine Potenski and Cesar Ferreyra-Mansilla and Chad Tomlinson and Charlotte Zhang and Chenghang Zong and Cheuk-Ting Law and Chia‐Lin Wei and BS Chris Oliveira and Christian D. Frazar and Christina Missler and Christina Zakarian and Christopher A. Walsh and Christopher M. Grochowski and Christopher Walsh},
journal = {Cell},
year = {2026},
month = oct,
doi = {10.1016/j.cell.2026.09.037},
url = {https://doi.org/10.1016/j.cell.2026.09.037},
note = {CC BY},
}
RIS
TY - JOUR
TI - Comprehensive benchmarking of somatic mutation detection by the SMaHT Network
AU - Alexej Abyzov
AU - Abhiram Natu
AU - Adam Voshall
AU - Adriana Estela Sedeño-Cortés
AU - Aidan Hugh Burn
AU - Alan P. Boyle
AU - Alexander D. Veit
AU - Alexander Eckehart Urban
AU - Alexandre Pellan Cheng
AU - Alexi Runnels
AU - Alisa Mo
AU - Amooyla Srinivasa
AU - Amy Leonardson
AU - Andrea J. Kriz
AU - Andrew B. Stergachis
AU - Andrew J. C. Russell
AU - Andrew J. Schroeder
AU - Andrew R. D'Avino
AU - Andrew Ruttenberg
AU - Ann Caplin
AU - Anna Minkina
AU - Areum Cho
AU - Arijit Panda
AU - Aristotelis Tsirigos
AU - Arnaz Maryam Tariq
AU - Ayush Semwal
AU - Azeet Narayan
AU - Azra Hasan
AU - Benjamin Costa
AU - Benjamin K. Johnson
AU - Benno Orr
AU - Benpeng Miao
AU - Bianca Morris
AU - Bo Zhou
AU - Boxun Zhao
AU - Brad Demarest
AU - B Bernstein
AU - Brandt A. Bessell
AU - Brianne Carroll
AU - Brianne E. Livermore
AU - Caitlin N. Jacques
AU - Camille Mumm
AU - Carlos Mendez‐Dorantes
AU - Carolin Purman
AU - Carrie Cibulskis
AU - Casey Andrews
AU - Catherine Potenski
AU - Cesar Ferreyra-Mansilla
AU - Chad Tomlinson
AU - Charlotte Zhang
AU - Chenghang Zong
AU - Cheuk-Ting Law
AU - Chia‐Lin Wei
AU - BS Chris Oliveira
AU - Christian D. Frazar
AU - Christina Missler
AU - Christina Zakarian
AU - Christopher A. Walsh
AU - Christopher M. Grochowski
AU - Christopher Walsh
PY - 2026
DA - 2026/10/01
JO - Cell
DO - 10.1016/j.cell.2026.09.037
UR - https://doi.org/10.1016/j.cell.2026.09.037
AB - Somatic mosaicism is increasingly recognized as a fundamental feature of human biology, yet the detection of somatic mutations remains challenging. The Somatic Mosaicism across Human Tissues (SMaHT) Network conducted large-scale benchmarking experiments involving cell lines and donor tissues to evaluate sequencing technologies, experimental approaches, and computational methods for detecting different types of somatic mutations, generating community resources with >1,000× short-read and 100-400× long-read data for nine analyzed samples. We determined effective strategies for utilizing short- and long-read sequencing for mutation detection and demonstrated that using donor-specific and pangenome-inferred diploid assemblies improved calling, extending mutation catalogs to challenging genomic regions. We benchmarked six duplex technologies and showed that single-cell sequencing resolves cell-type-specific mutational patterns and heterogeneity. Our results indicate that bulk, single-cell, and duplex analyses are complementary and that leveraging all three provides comprehensive characterization of mosaicism within tissues. Together, these findings provide the scientific and clinical communities with a roadmap for accurate genome-wide somatic mutation analysis.
SN - 0092-8674
SN - 1097-4172
ER -