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Scalable Open Science Approach for Mutation Calling of Tumor Exomes Using Multiple Genomic Pipelines

Authors :
Ellrott, Kyle
Bailey, Matthew H.
Saksena, Gordon
Covington, Kyle R.
Kandoth, Cyriac
Stewart, Chip
Hess, Julian
Ma, Singer
Chiotti, Kami E.
McLellan, Michael
Sofia, Heidi J.
Hutter, Carolyn M.
Getz, Gad
Wheeler, David A.
Ding, Li
Caesar-Johnson, Samantha J.
Demchok, John A.
Felau, Ina
Kasapi, Melpomeni
Ferguson, Martin L.
Tarnuzzer, Roy
Wang, Zhining
Yang, Liming
Zenklusen, Jean C.
Zhang, Jiashan (Julia)
Chudamani, Sudha
Liu, Jia
Lolla, Laxmi
Naresh, Rashi
Pihl, Todd
Sun, Qiang
Wan, Yunhu
Wu, Ye
Cho, Juok
DeFreitas, Timothy
Frazer, Scott
Gehlenborg, Nils
Heiman, David I.
Kim, Jaegil
Lawrence, Michael S.
Lin, Pei
Meier, Sam
Noble, Michael S.
Voet, Doug
Zhang, Hailei
de Krijger, Ronald
MC3 Working Group
The Cancer Genome Atlas Research Network
Publication Year :
2018

Abstract

The Cancer Genome Atlas (TCGA) cancer genomics dataset includes over 10,000 tumor-normal exome pairs across 33 different cancer types, in total >400 TB of raw data files requiring analysis. Here we describe the Multi-Center Mutation Calling in Multiple Cancers project, our effort to generate a comprehensive encyclopedia of somatic mutation calls for the TCGA data to enable robust cross-tumor-type analyses. Our approach accounts for variance and batch effects introduced by the rapid advancement of DNA extraction, hybridization-capture, sequencing, and analysis methods over time. We present best practices for applying an ensemble of seven mutation-calling algorithms with scoring and artifact filtering. The dataset created by this analysis includes 3.5 million somatic variants and forms the basis for PanCan Atlas papers. The results have been made available to the research community along with the methods used to generate them. This project is the result of collaboration from a number of institutes and demonstrates how team science drives extremely large genomics projects. The MC3 is a variant calling project of over 10,000 cancer exome samples from 33 cancer types. Over three million somatic variants were detected using seven different methods developed from institutions across the United States. These variants formed the basis for the PanCan Atlas papers.

Details

Language :
English
Database :
OpenAIRE
Accession number :
edsair.od.....10691..89e4ff302fd2208bcd5c6d834546b26a