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The results published here are in whole or part based upon data generated by the TCGA Research Network:, a project of the National Cancer Institute. įunding: This work was supported by the National Cancer Institute the National Human Genome Research Institute a gift from Edward Schulak and the Howard Hughes Medical Institute. TCGA barcodes and Universally Unique Identifiers (UUIDs) for the TCGA samples used in this study can be found in Table S2. Open-access somatic MAFs can be visualized and downloaded via the UCSC Cancer Browser ( ). Variant Call Format (VCF) and Mutation Annotation Format (MAF) files are available from the TCGA Data Access Portal at. BAM files are available from The Cancer Genome Atlas via the UCSC Cancer Genomics Hub. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: The authors confirm that, for approved reasons, some access restrictions apply to the data underlying the findings. Received: JAccepted: SeptemPublished: November 18, 2014Ĭopyright: © 2014 Radenbaugh et al. Lee Moffitt Cancer Center & Research Institute, United States of America (2014) RADIA: RNA and DNA Integrated Analysis for Somatic Mutation Detection. Finally, we highlight mutations in important cancer genes that were rescued due to the incorporation of the RNA.Ĭitation: Radenbaugh AJ, Ma S, Ewing A, Stuart JM, Collisson EA, Zhu J, et al. We evaluate sensitivity on the simulation data and demonstrate our ability to rescue back mutations at low DNA allelic frequencies by including the RNA. We also introduce a simulation package that spikes in artificial mutations to patient data, rather than simulating sequencing data from a reference genome.
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Mutations with both high DNA and RNA read support have the highest validation rate of over 99%.
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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. 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. The inclusion of the RNA increases the power to detect somatic mutations, especially at low DNA allelic frequencies. 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. In recent years, it has become routine for projects like The Cancer Genome Atlas (TCGA) to also sequence the tumor RNA. Mutation calling algorithms thus far have focused on comparing the normal and tumor genomes from the same individual. Further, the direct DNA A-to-I deamination at Transcription Bubbles is expected to contribute to the T-to-C component of the strand-biased Ig SHM spectrum.The detection of somatic single nucleotide variants is a crucial component to the characterization of the cancer genome.
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It is concluded that the strand-biased somatic mutation patterns at both A:T and G:C base pairs in vivo are best interpreted by the sequential steps of the RNA/RT-based mechanism. Further, if the A-to-I DNA editing at RNA:DNA hybrids were the sole cause of A-to-I (read as A-to-G) mutation events for Ig SHM in vivo then the exact opposite strand biases at A:T base pairs (T > A) of what is actually observed (A > T) would be predicted. The RNA moiety of RNA:DNA hybrids is also edited at similar lower frequencies relative to the editing rate at dsRNA substrates. The A-to-I DNA editing component at RNA:DNA hybrids that is likely to occur in Transcription Bubbles, while important, is of far lower A-to-I editing efficiency than in dsRNA substrates. Both these strand biases are inconsistent with alternative “DNA Deamination” mechanisms, yet are expected consequences of the RNA/RT-based “Reverse Transcriptase” mechanism of immunoglobulin (Ig) somatic hypermutation (SHM). Those studies have established that there are two significant strand biases at A:T and G:C base pairs, A-site mutations exceed T-site mutations (A > T) by 2.9 fold and G-site mutations exceed C-site mutations (G > C) by 1.7 fold. The significance of these data are related to previous work on strand-biased and codon-context mutation signatures in B lymphocytes and cancer genomes. The implications are discussed of recently published biochemical studies on ADAR-mediated A-to-I DNA and RNA deamination at RNA:DNA hybrids.