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.

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

• SEQC2 Article Collection on Nature Biotechnology

• SEQC2 Article Collection on Genome Biology

Community use cases for SEQC2 somatic mutation reference call sets

• nf-core/sarek 3 used SEQC2 to benchmark somatic mutation calling

• Whole-genome somatic small variant calling: this how-to explores calling variants using Mutect2 in the SEQC2 Consortium Sample by NVIDIA Clara Parabricks

• Benchmarking NVIDIA Clara Parabricks' somatic variant calling pipeline on AWS

SEQC2 Webinar Series

Sequencing Data

Data Analysis

High-confidence Somatic SNV and INDEL v1.2 (step-by-step procedures)

• 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

    • a) Tumor-Normal somatic mutation run scripts

• 2) Somatic Mutation Results and Data on Seven Bridges Genomics for 63 pairs of tumor-normal BAM files

  • i) Somatic Mutation Tools on Cancer Genomics Cloud

  • ii) Building workflow on Seven Bridges Genomics

• 3) Orthogonal Validations

• 4) VAF of Super Set variant positions in WES sequencing data

• 5) High-confidence somatic VCF file annotations

• 6) Benchmarking Examples

Whole Exome Sequencing (WES)

Tumor-Normal Titration Analysis

FFPE (varying formalin fixation times)

Different Library Preps

Identify high-confidence Regions

Internal Data Sharing Solutions

• Globus Tutorial

• Seven Bridges Genomics