Abstract

BackgroundRNA editing is a co-transcriptional modification that increases the molecular diversity, alters secondary structure and protein coding sequences by changing the sequence of transcripts. The most common RNA editing modification is the single base substitution (A→I) that is catalyzed by the members of the Adenosine deaminases that act on RNA (ADAR) family. Typically, editing sites are identified as RNA-DNA-differences (RDDs) in a comparison of genome and transcriptome data from next-generation sequencing experiments. However, a method for robust detection of site-specific editing events from replicate RNA-seq data has not been published so far. Even more surprising, condition-specific editing events, which would show up as differences in RNA-RNA comparisons (RRDs) and depend on particular cellular states, are rarely discussed in the literature.ResultsWe present JACUSA, a versatile one-stop solution to detect single nucleotide variant positions from comparing RNA-DNA and/or RNA-RNA sequencing samples. The performance of JACUSA has been carefully evaluated and compared to other variant callers in an in silico benchmark. JACUSA outperforms other algorithms in terms of the F measure, which combines precision and recall, in all benchmark scenarios. This performance margin is highest for the RNA-RNA comparison scenario.We further validated JACUSA’s performance by testing its ability to detect A→I events using sequencing data from a human cell culture experiment and publicly available RNA-seq data from Drosophila melanogaster heads. To this end, we performed whole genome and RNA sequencing of HEK-293 cells on samples with lowered activity of candidate RNA editing enzymes. JACUSA has a higher recall and comparable precision for detecting true editing sites in RDD comparisons of HEK-293 data. Intriguingly, JACUSA captures most A→I events from RRD comparisons of RNA sequencing data derived from Drosophila and HEK-293 data sets.ConclusionOur software JACUSA detects single nucleotide variants by comparing data from next-generation sequencing experiments (RNA-DNA or RNA-RNA). In practice, JACUSA shows higher recall and comparable precision in detecting A→I sites from RNA-DNA comparisons, while showing higher precision and recall in RNA-RNA comparisons.

Highlights

  • RNA editing is a co-transcriptional modification that increases the molecular diversity, alters secondary structure and protein coding sequences by changing the sequence of transcripts

  • Two types of single base modifications, namely adenosine to inosine conversions (A → I) and cytidin to uridine (C → U) conversions, have been characterised in detail over decades of research [1]. Both conversions are executed by two specific classes of RNA binding proteins (RBPs) that interact with their respective RNA targets: Adenosine deaminase acting on RNA (ADAR) catalyses A → I conversions, whereas APOBEC1 family members catalyse C → U conversions

  • Detection of Single nucleotide variant (SNV) in RNA-DNA comparisons When no replicates are used, JAVA framework for accurate SNV assessment (JACUSA) shows a 6 − 10% higher true positive rate (TPR) as compared to the other tested methods while being competitive at the level of precision

Read more

Summary

Results

In silico benchmark We use two benchmark scenarios (Fig. 2) to compare JACUSA with other popular variant callers: REDItools, SAMtools/BCFtools, and MuTect. JACUSA identifies the highest number of RNA editing sites (6,466) out of which ≈ 98% show lower editing levels in siADAR samples than in samples from siAPOBEC3 treated cells This means that JACUSA reports 6375 true positive A → G sites out of a set of 6,466 predicted sites, the highest among all tested variant callers. Sites that are not homozygous in DNA represent putative SNPs and are typically removed from the candidate set when identifying RNA editing sites in RDD comparisons As this information is not visible to SAMtools/BCFtools and JACUSA, we reasoned that a lower fraction of SNP sites among identified RRDs would indicate a better performance on calling differential RNA editing events.

Conclusion
Background
Objective
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call