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Detection theory in identification of RNA-DNA sequence differences using RNA-sequencing.

Toung JM, Lahens N, Hogenesch JB, Grant G - PLoS ONE (2014)

Bottom 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.

View Article: PubMed Central - PubMed

Affiliation: Genomics and Computational Biology Graduate Program, University of Pennsylvania School of Medicine, Philadelphia, PA, United States of America.

ABSTRACT
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.

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Sensitivity of RDD detection versus the simulated RDD level.Here we depict the true positive rate of RDD detection versus the simulated RDD level, or the percentage of reads at the site bearing the sequence difference allele. A minimum of 1 read bearing the RNA-DNA sequence difference is sufficient for a site to be deemed correctly identified. Sites with coverage less than 10x per the simulated RNA-Seq dataset are removed from consideration.
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pone-0112040-g002: Sensitivity of RDD detection versus the simulated RDD level.Here we depict the true positive rate of RDD detection versus the simulated RDD level, or the percentage of reads at the site bearing the sequence difference allele. A minimum of 1 read bearing the RNA-DNA sequence difference is sufficient for a site to be deemed correctly identified. Sites with coverage less than 10x per the simulated RNA-Seq dataset are removed from consideration.

Mentions: Next, we analyzed the effect of RDD level on the sensitivity of RDD detection. We binned the simulated sequence differences into 10 groups by RDD levels and evaluated the true positive rate for each group. For both datasets, we found the sensitivity of RDD detection to increase with higher RDD levels (Figure 2; Table S4 in File S1). Furthermore, GSNAP had the highest sensitivity values across all levels among the four aligners. Given the lower recall rates for sequence differences with low levels, we restrict our downstream analyses to sites with a minimum level of 10%.


Detection theory in identification of RNA-DNA sequence differences using RNA-sequencing.

Toung JM, Lahens N, Hogenesch JB, Grant G - PLoS ONE (2014)

Sensitivity of RDD detection versus the simulated RDD level.Here we depict the true positive rate of RDD detection versus the simulated RDD level, or the percentage of reads at the site bearing the sequence difference allele. A minimum of 1 read bearing the RNA-DNA sequence difference is sufficient for a site to be deemed correctly identified. Sites with coverage less than 10x per the simulated RNA-Seq dataset are removed from consideration.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4232354&req=5

pone-0112040-g002: Sensitivity of RDD detection versus the simulated RDD level.Here we depict the true positive rate of RDD detection versus the simulated RDD level, or the percentage of reads at the site bearing the sequence difference allele. A minimum of 1 read bearing the RNA-DNA sequence difference is sufficient for a site to be deemed correctly identified. Sites with coverage less than 10x per the simulated RNA-Seq dataset are removed from consideration.
Mentions: Next, we analyzed the effect of RDD level on the sensitivity of RDD detection. We binned the simulated sequence differences into 10 groups by RDD levels and evaluated the true positive rate for each group. For both datasets, we found the sensitivity of RDD detection to increase with higher RDD levels (Figure 2; Table S4 in File S1). Furthermore, GSNAP had the highest sensitivity values across all levels among the four aligners. Given the lower recall rates for sequence differences with low levels, we restrict our downstream analyses to sites with a minimum level of 10%.

Bottom 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.

View Article: PubMed Central - PubMed

Affiliation: Genomics and Computational Biology Graduate Program, University of Pennsylvania School of Medicine, Philadelphia, PA, United States of America.

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus