Evidence map›Paper›PMID 41279200›Full record

ArticlebioRxiv : the preprint server for biology2025

Comprehensive benchmarking of somatic mutation detection by the SMaHT Network.

Somatic Mosaicism across Human Tissues Network, Alexej Abyzov

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Somatic Mosaicism across Human Tissues Network

Funding

WashU-VAI Somatic Mosaicism across Human Tissues (SMaHT) Program Genome Characterization CenterUM1DA058219 · NIDA · WASHINGTON UNIVERSITY · PI Robert Scott Fulton, Hui Shen · 2023 to 2026
$29.1M
Somatic Mosaicism across Human Tissues Program: Genome Characterization Centers (GCC SMaHT)UM1DA058220 · NIDA · SEATTLE CHILDREN'S HOSPITAL · PI JAMES T BENNETT, Evan Eichler · 2023 to 2026
$15.2M
Comprehensive Somatic Variant Characterization at the HGSCUM1DA058229 · NIDA · BAYLOR COLLEGE OF MEDICINE · PI Harsha Vardhan Doddapaneni, RICHARD A GIBBS · 2023 to 2026
$15.0M
Whole Individual Comprehensive KnowlEDge: Somatic Mosaicism across Human Tissues (WICKed SMaHT)UM1DA058235 · NIDA · BROAD INSTITUTE, INC. · PI KRISTIN ARDLIE, Niall John Lennon · 2023 to 2026
$13.8M
New York Genome Characterization Center: Somatic Mosaicism across Human TissuesUM1DA058236 · NIDA · NEW YORK GENOME CENTER · PI Samuel Aparicio, Nicolas Robine · 2023 to 2026
$11.8M
Data Analysis Center for Somatic Mosaicism Across Human Tissues NetworkUM1DA058230 · NIDA · HARVARD MEDICAL SCHOOL · PI Peter J Park · 2023 to 2026
$6.3M
WashU Somatic Mosaicism across Human Tissues (SMaHT) Program Organizational CenterU24NS132103 · NINDS · WASHINGTON UNIVERSITY · PI FULTON, LUCINDA, LAWSON, HEATHER A. · 2023 to 2025
$4.5M
Tissue Procurement Center (TPC) Supporting the Somatic Mosaicism across Human Tissues (SMaHT) NetworkU24MH133204 · NIMH · NATIONAL DISEASE RESEARCH INTERCHANGE · PI BELL, THOMAS J · 2023 to 2023
$3.0M
Establishing and benchmarking advanced methods to comprehensively characterize somatic genome variation in single human cellsUH3NS132146 · NINDS · STANFORD UNIVERSITY · PI Alexander Eckehart Urban · 2025 to 2026
$1.2M
Establishing and benchmarking advanced methods to comprehensively characterize somatic genome variation in single human cellsUG3NS132146 · NINDS · STANFORD UNIVERSITY · PI URBAN, ALEXANDER ECKEHART, VACCARINO, FLORA M · 2023 to 2024
$922k
Single Molecule Detection of L1 Insertions and IntermediatesUG3NS132127 · NINDS · DANA-FARBER CANCER INST · PI BERNSTEIN, BRADLEY EVAN, BURNS, KATHLEEN H · 2023 to 2024
$879k
Detection and Characterization of Somatic Mutations in Human Tissue Utilizing Duplex-Consensus SequencingUG3NS132144 · NINDS · BOSTON CHILDREN'S HOSPITAL · PI CHOUDHURY, SANGITA, LEE, EUNJUNG ALICE · 2023 to 2024
$877k
NIDA NIH HHS UM1 DA058219NIDA NIH HHS UM1 DA058220NIDA NIH HHS UM1 DA058229NIDA NIH HHS UM1 DA058230NIDA NIH HHS UM1 DA058235NIDA NIH HHS UM1 DA058236NIMH NIH HHS U24 MH133204NINDS NIH HHS U24 NS132103NINDS NIH HHS UG3 NS132024NINDS NIH HHS UG3 NS132061NINDS NIH HHS UG3 NS132084NINDS NIH HHS UG3 NS132105NINDS NIH HHS UG3 NS132127NINDS NIH HHS UG3 NS132128NINDS NIH HHS UG3 NS132132NINDS NIH HHS UG3 NS132134NINDS NIH HHS UG3 NS132135NINDS NIH HHS UG3 NS132136NINDS NIH HHS UG3 NS132138NINDS NIH HHS UG3 NS132139NINDS NIH HHS UG3 NS132144NINDS NIH HHS UG3 NS132146NINDS NIH HHS UH3 NS132146
6 · The paper itself

Abstract

Somatic mosaicism is increasingly recognized as a fundamental feature of human biology, yet the detection of somatic mutations remains challenging. The SMaHT Network conducted four large-scale benchmarking experiments to evaluate sequencing technologies, experimental approaches, and computational methods for detecting diverse somatic mutations. Cumulative sequencing coverage exceeded 1,000× with short reads and 100-400× with long reads for each of nine analyzed samples. We defined optimal strategies for integrating bulk short- and long-read sequencing for mutation detection and demonstrated that using donor-specific assemblies and human pangenome improved variant calling and extended mutation catalogs to challenging genomic regions. We benchmarked six duplex-seq 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 leveraging all three provides comprehensive characterization of mosaicism within a tissue. Together, these findings provide a roadmap for accurate, genome-wide somatic mutation discovery and analysis.

Identifiers

PMID41279200
PMCPMC12632347

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.