  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 method that combines 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 the DNA and RNA, we are able to rescue back
calls that would be missed by traditional mutation calling algorithms that only
examine the DNA.
  RADIA was developed for the identification of somatic mutations using both
DNA and RNA from the same individual. We demonstrate high sensitivity (84%) and
very high specificity (98% and 99%) in real 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 real data, rather
than simulating sequencing data from a reference genome. We evaluate
sensitivity on the simulation data and demonstrate our ability to rescue back
calls at low DNA allelic frequencies by including the RNA. Finally, we
highlight mutations in important cancer genes that were rescued back due to the
incorporation of the RNA.
  Software available at https://github.com/aradenbaugh/radia/
