Limits...
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.

Show MeSH

Related in: MedlinePlus

False discovery rate of RNA-DNA sequence difference detection.Here we depict the false discovery rate of RNA-DNA sequence difference detection under various thresholds on the coverage, level of sequence difference, and number of reads bearing the sequence difference base per the aligner. Calculations are averaged across the three replicates and error bars represent standard deviation values.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4232354&req=5

pone-0112040-g004: False discovery rate of RNA-DNA sequence difference detection.Here we depict the false discovery rate of RNA-DNA sequence difference detection under various thresholds on the coverage, level of sequence difference, and number of reads bearing the sequence difference base per the aligner. Calculations are averaged across the three replicates and error bars represent standard deviation values.

Mentions: For a better understanding of how false positives affect the analysis of RDDs, we evaluated the false discovery rate (FDR), or the percentage of sites identified as having sequence differences that were not simulated to represent RDDs. For dataset 1, we found the FDR to range from 1.31±4.06E-2% in Tophat2 to 6.24±8.26E-2% in MapSplice when using a ‘minimum coverage of 10x, minimum level of 10%, and minimum of 1 one read bearing the RDD base’ threshold. These relatively low false discovery rates indicate that in the absence of sequencing error, misalignment issues do not contribute significantly to the incidence of false positives. With the introduction of sequencing error in dataset 2, we found that the false discovery rates are much higher, ranging from approximately 57% in GSNAP to 71% in Tophat2. These results are not surprising, as a threshold requiring only one read to bear the RDD base introduces false positives at sites with sequencing errors. With stricter thresholds on RDD detection, such as requiring a minimum coverage of 20x, a minimum RDD level of 20%, and a minimum of 4 RDD bases observed, we found that the false discovery rate decreases dramatically (Figure 4).


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

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

False discovery rate of RNA-DNA sequence difference detection.Here we depict the false discovery rate of RNA-DNA sequence difference detection under various thresholds on the coverage, level of sequence difference, and number of reads bearing the sequence difference base per the aligner. Calculations are averaged across the three replicates and error bars represent standard deviation values.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0112040-g004: False discovery rate of RNA-DNA sequence difference detection.Here we depict the false discovery rate of RNA-DNA sequence difference detection under various thresholds on the coverage, level of sequence difference, and number of reads bearing the sequence difference base per the aligner. Calculations are averaged across the three replicates and error bars represent standard deviation values.
Mentions: For a better understanding of how false positives affect the analysis of RDDs, we evaluated the false discovery rate (FDR), or the percentage of sites identified as having sequence differences that were not simulated to represent RDDs. For dataset 1, we found the FDR to range from 1.31±4.06E-2% in Tophat2 to 6.24±8.26E-2% in MapSplice when using a ‘minimum coverage of 10x, minimum level of 10%, and minimum of 1 one read bearing the RDD base’ threshold. These relatively low false discovery rates indicate that in the absence of sequencing error, misalignment issues do not contribute significantly to the incidence of false positives. With the introduction of sequencing error in dataset 2, we found that the false discovery rates are much higher, ranging from approximately 57% in GSNAP to 71% in Tophat2. These results are not surprising, as a threshold requiring only one read to bear the RDD base introduces false positives at sites with sequencing errors. With stricter thresholds on RDD detection, such as requiring a minimum coverage of 20x, a minimum RDD level of 20%, and a minimum of 4 RDD bases observed, we found that the false discovery rate decreases dramatically (Figure 4).

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