Genetics in MedicineGeneticsPeer-reviewed journal article
Standard operating procedure for somatic variant refinement of tumor sequencing data
Erica Kay BarnelliD, Peter RonningiD, Katie Marie CampbelliD, Kilannin KrysiakiD, Benjamin J. AinscoughiD, Lana M. ShetaiD, Shahil P. PemaiD, Alina D. SchmidtiD, Megan M. RichtersiD, Kelsy C. CottoiD, Arpad M. DanosiD, Cody A. RamireziD, Zachary L. SkidmoreiD, Nicholas C. SpiesiD, Jasreet HundaliD, Malik S. Sediqzad, Jason KunisakiiD, Felicia GomeziD, Lee TraniiD, Matthew K. MatlockiD, Alex Handler WagneriD, S. Joshua SwamidassiD, Malachi GriffithiD, Obi L. GriffithiD
Abstract
PurposeManual review of aligned sequencing reads is required to develop a high-quality list of somatic variants from massively parallel sequencing data (MPS). Despite widespread use in analyzing MPS data, there has been little attempt to describe methods for manual review, resulting in high inter- and intra-lab variability in somatic variant detection and characterization of tumors.\n\nMethodsOpen source software was used to develop an optimal method for manual review setup. We also developed a systemic approach to visually inspect each variant during manual review.\n\nResultsWe present a standard operating procedures for somatic variant refinement for use by manual reviewers. The approach is enhanced through representative examples of 4 different manual review categories that indicate a reviewers confidence in the somatic variant call and 19 annotation tags that contextualize commonly observed sequencing patterns during manual review. Representative examples provide detailed instructions on how to classify variants during manual review to rectify lack of confidence in automated somatic variant detection.\n\nConclusionStandardization of somatic variant refinement through systematization of manual review will improve the consistency and reproducibility of identifying true somatic variants after automated variant calling.
- assay: whole-exome sequencing
- assay: whole-genome sequencing
- research focus: variant calling
- research focus: variant effect prediction
- research focus: benchmarking
- approach: experimental assay
- Citations
- 94
- FWCI
- 3.62
- Field percentile
- top 5%
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Using Machine Learning to Identify True Somatic Variants from Next-Generation Sequencing
Chao Wu, Xiaonan Zhao, … Mahdi Sarmady·Clinical ChemistryPeer-reviewed·Oct 31, 2019
- A multi-platform reference for somatic structural variation detection
Jose Espejo Valle-Inclán, Nicolle J.M. Besselink, … Edwin P. J. G. Cuppen·Cell GenomicsPeer-reviewed·Jun 1, 2022
- Evaluation of somatic variant calling methods on high coverage tumour-only amplicon sequencing data in a clinical environment
Dhammapal Bharne, Daniel Gaston·bioRxiv·Apr 11, 2026
- An optimised computational approach for the identification of somatic structural variants in cancer
Sara Waise, Nana Mensah, … Peter Van Loo·bioRxiv·Jul 1, 2025
- GLASS: assisted and standardized assessment of gene variations from Sanger sequence trace data
Karol Pál, Vojtěch Bystrý, … Nikos Darzentas·BioinformaticsPeer-reviewed·Jul 12, 2017
- Validation of genetic variants from NGS data using Deep Convolutional Neural Networks
Marc Vaisband, Maria Schubert, … Jan Hasenauer·BMC BioinformaticsPeer-reviewed·Apr 20, 2023