The Somatic Mutation Working Group
of the
SEQC2 Consortium
The objective of the Somatic Mutation Working Group of the Sequencing Quality Control Phase 2 (SEQC2) Consortium is to establish best practices, reference standards, and benchmark the results of somatic mutation detections under different bioinformatic and laboratory conditions.
If you have any question or suggestion with regard to this website, email to ltfang@gmail.com. For questions with regard to the Somatic Mutation Working Group, email to Wenming.Xiao@fda.hhs.gov.
Publication
Fang LT, Zhu B, Zhao Y, Chen W, Yang Z, Kerrigan L, Langenbach K, de Mars M, Lu C, Idler K, et al. Establishing community reference samples, data and call sets for benchmarking cancer mutation detection using whole-genome sequencing. Nature Biotechnology. 2021;39(9):1151-1160 / PMID:34504347 / SharedIt Link
Xiao W, Ren L, Chen Z, Fang LT, Zhao Y, Lack J, Guan M, Zhu B, Jaeger E, Kerrigan L, et al. Toward best practice in cancer mutation detection with whole-genome and whole-exome sequencing. Nature Biotechnology. 2021;39(9):1141-1150 / PMID:34504346 / SharedIt Link
Chen W, Zhao Y, Chen X, Yang Z, Xu X, Bi Y, Chen V, Li J, Choi H, Ernest B, et al. A multicenter study benchmarking single-cell RNA sequencing technologies using reference samples. Nature Biotechnology. 2021;39(9):1103-1114 / PMID:33349700 / SharedIt Link
Sahraeian SME, Fang LT, Karagiannis K, Moos M, Smith S, Santana-Quintero L, Xiao C, Colgan M, Hong H, Mohiyuddin M, et al. Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample. Genome Biology. 2022;23(1):12 / PMID:34996510
Zhao Y, Fang LT, Shen T, Choudhari S, Talsania K, Chen X, Shetty J, Kriga Y, Tran B, Zhu B, et al. Whole genome and exome sequencing reference datasets from a multi-center and cross-platform benchmark study. Scientific Data. 2021;8(1):296 / PMID:34753956
Chen X, Yang Z, Chen W, Zhao Y, Farmer A, Tran B, Furtak V, Moos M, Xiao W, Wang C. A multi-center cross-platform single-cell RNA sequencing reference dataset. Scientific Data. 2021;8(1):39 / PMID:33531477
Xiao C, Chen Z, Chen W, Padilla C, Colgan M, Wu W, Fang LT, Liu T, Yang Y, Schneider V, et al. Personalized genome assembly for accurate cancer somatic mutation discovery using tumor-normal paired reference samples. Genome Biology. 2022;23(1):237 / PMID:36352452
Talsania K, Shen T, Chen X, Jaeger E, Li Z, Chen Z, Chen W, Tran B, Kusko R, Wang L, et al. Structural variant analysis of a cancer reference cell line sample using multiple sequencing technologies. Genome Biology. 2022;23(1):255 / PMID:36514120
Additionally
Community use cases for SEQC2 somatic mutation reference call sets
Establishing community reference samples, data, and call sets for benchmarking cancer mutation detection using WGS
-- Li Tai Fang
Towards best practice in cancer mutation detection with WGS and WES
-- Wenming Xiao
Robust cancer mutation detection with deep learning models derived from tumor-normal sequencing data
-- Mohammad Sahraeian
A multicenter study benchmarking single-cell RNA-seq technologies using reference samples
-- Charles Wang
1) BAM Simulation pipeline in SomaticSeq to create pseudo-tumor and normal pairs for each data group
i) Use SomaticSeq to run Dockerized Mutation Callers on the pseudo-tumor and normal pairs to create SomaticSeq Classifiers
2) Somatic Mutation Results and Data on Seven Bridges Genomics for 63 pairs of tumor-normal BAM files
4) VAF of Super Set variant positions in WES sequencing data