Detection theory in identification of RNA-DNA sequence differences using RNA-sequencing.
Advances in sequencing technology have allowed for detailed analyses of the transcriptome at single-nucleotide resolution, facilitating the study of RNA editing or sequence differences between RNA and DNA genome-wide. In humans, two types of post-transcriptional RNA editing processes are known to occur: A-to-I deamination by ADAR and C-to-U deamination by APOBEC1. In addition to these sequence differences, researchers have reported the existence of all 12 types of RNA-DNA sequence differences (RDDs); however, the validity of these claims is debated, as many studies claim that technical artifacts account for the majority of these non-canonical sequence differences. In this study, we used a detection theory approach to evaluate the performance of RNA-Sequencing (RNA-Seq) and associated aligners in accurately identifying RNA-DNA sequence differences. By generating simulated RNA-Seq datasets containing RDDs, we assessed the effect of alignment artifacts and sequencing error on the sensitivity and false discovery rate of RDD detection. Overall, we found that even in the presence of sequencing errors, false negative and false discovery rates of RDD detection can be contained below 10% with relatively lenient thresholds. We also assessed the ability of various filters to target false positive RDDs and found them to be effective in discriminating between true and false positives. Lastly, we used the optimal thresholds we identified from our simulated analyses to identify RDDs in a human lymphoblastoid cell line. We found approximately 6,000 RDDs, the majority of which are A-to-G edits and likely to be mediated by ADAR. Moreover, we found the majority of non A-to-G RDDs to be associated with poorer alignments and conclude from these results that the evidence for widespread non-canonical RDDs in humans is weak. Overall, we found RNA-Seq to be a powerful technique for surveying RDDs genome-wide when coupled with the appropriate thresholds and filters.
Genomics and Computational Biology Graduate Program, University of Pennsylvania School of Medicine, Philadelphia, PA, United States of America.
Jonathan M
JM
Toung
Genomics and Computational Biology Graduate Program, University of Pennsylvania School of Medicine, Philadelphia, PA, United States of America.
Nicholas
N
Lahens
Institute for Biomedical Informatics, University of Pennsylvania School of Medicine, Philadelphia, PA, United States of America; Institute for Translational Medicine and Therapeutics, University of Pennsylvania School of Medicine, Philadelphia, PA, United States of America; Department of Pharmacology, University of Pennsylvania School of Medicine, Philadelphia, PA, United States of America.
John B
JB
Hogenesch
Institute for Biomedical Informatics, University of Pennsylvania School of Medicine, Philadelphia, PA, United States of America; Institute for Translational Medicine and Therapeutics, University of Pennsylvania School of Medicine, Philadelphia, PA, United States of America; Department of Genetics, University of Pennsylvania School of Medicine, Philadelphia, PA, United States of America.
Gregory
G
Grant
D001483:Base Sequence; D002460:Cell Line; D019295:Computational Biology; D003198:Computer Simulation; D004247:DNA / Q000235:genetics*; D030561:Databases, Nucleic Acid; D006801:Humans; D008214:Lymphocytes / Q000378:metabolism; D012313:RNA / Q000235:genetics*; D012372:ROC Curve; D017423:Sequence Analysis, RNA / Q000379:methods*
D012313:RNA; D004247:DNA