RADIA: RNA and DNA integrated analysis for somatic mutation detection

Radenbaugh, Amie J., Ma, Singer, Ewing, Adam, Stuart, Joshua M., Collisson, Eric A., Zhu, Jingchun and Haussler, David (2014) RADIA: RNA and DNA integrated analysis for somatic mutation detection. PLoS ONE, 9 11: e111516.1-e111516.11. doi:10.1371/journal.pone.0111516


Author Radenbaugh, Amie J.
Ma, Singer
Ewing, Adam
Stuart, Joshua M.
Collisson, Eric A.
Zhu, Jingchun
Haussler, David
Title RADIA: RNA and DNA integrated analysis for somatic mutation detection
Journal name PLoS ONE   Check publisher's open access policy
ISSN 1932-6203
Publication date 2014-11-18
Year available 2014
Sub-type Article (original research)
DOI 10.1371/journal.pone.0111516
Open Access Status DOI
Volume 9
Issue 11
Start page e111516.1
End page e111516.11
Total pages 11
Place of publication San Francisco, United States
Publisher Public Library of Science (PLoS)
Collection year 2015
Language eng
Formatted abstract
The detection of somatic single nucleotide variants is a crucial component to the characterization of the cancer genome. Mutation calling algorithms thus far have focused on comparing the normal and tumor genomes from the same individual. In recent years, it has become routine for projects like The Cancer Genome Atlas (TCGA) to also sequence the tumor RNA. Here we present RADIA (RNA and DNA Integrated Analysis), a novel computational method combining the patient-matched normal and tumor DNA with the tumor RNA to detect somatic mutations. The inclusion of the RNA increases the power to detect somatic mutations, especially at low DNA allelic frequencies. By integrating an individual’s DNA and RNA, we are able to detect mutations that would otherwise be missed by traditional algorithms that examine only the DNA. We demonstrate high sensitivity (84%) and very high precision (98% and 99%) for RADIA in patient data from endometrial carcinoma and lung adenocarcinoma from TCGA. Mutations with both high DNA and RNA read support have the highest validation rate of over 99%. We also introduce a simulation package that spikes in artificial mutations to patient data, rather than simulating sequencing data from a reference genome. We evaluate sensitivity on the simulation data and demonstrate our ability to rescue back mutations at low DNA allelic frequencies by including the RNA. Finally, we highlight mutations in important cancer genes that were rescued due to the incorporation of the RNA.
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Mater Research Institute-UQ (MRI-UQ)
Non HERDC
 
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