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MultiEditR: The first tool for detection and quantification of multiple RNA editing sites from Sanger sequencing demonstrates comparable fidelity to RNA-seq

ABSTRACT: We present MultiEditR, the first algorithm specifically designed to detect and quantify RNA editing from Sanger sequencing (z.umn.edu/multieditr). Although RNA editing is routinely evaluated by measuring the heights of peaks from in Sanger sequencing traces, the accuracy and the precision of this approach has yet to be evaluated against gold-standards next-generation sequencing methods. Through a comprehensive comparison to RNA-seq and amplicon based deep sequencing, we show that MultiEditR is accurate, precise, and reliable for detecting endogenous and programmable RNA editing.

ORGANISM(S): Mus musculus

PROVIDER: GSE164211 | GEO | 2023/01/04

REPOSITORIES: GEO

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Project description:While the importance of random sequencing errors decreases at higher DNA or RNA sequencing depths, systematic sequencing errors (SSEs) dominate at high sequencing depths and can be difficult to distinguish from biological variants. These SSEs can cause base quality scores to underestimate the probability of error at certain genomic positions, resulting in false positive variant calls, particularly in mixtures such as samples with RNA editing, tumors, circulating tumor cells, bacteria, mitochondrial heteroplasmy, or pooled DNA. Most algorithms proposed for correction of SSEs require a training data set, which is typically either from a part of the data set being M-bM-^@M-^\recalibratedM-bM-^@M-^] (Genome Analysis ToolKit, or GATK) or from a separate data set with special characteristics (SysCall). Here, we combine the advantages of these approaches by adding synthetic RNA spike-in standards to human RNA, and use GATK to recalibrate base quality scores with reads mapped to the spike-in standards. Compared to conventional GATK recalibration that uses reads mapped to the genome, spike-ins improve the accuracy of Illumina base quality scores by a mean of 5 units, and by as much as 13 units M-BM- at CpG sites. In addition, since reads mapping to the genome are not used for recalibration, our method allows run-specific recalibration even for the many species without a comprehensive and accurate SNP database. We also use GATK with the spike-in standards to demonstrate that the Illumina RNA sequencing runs overestimate quality scores for AC, CC, GC, GG, and TC dinucleotides, while SOLiD has less dinucleotide SSEs but more SSEs for certain cycles. We conclude that using these DNA and RNA spike-in standards with GATK improves base quality score recalibration. Four human RNA samples with equimolar ERCC spike-in standards were sequenced on Illumina. Two human brain/liver/muscle RNA mixtures with dynamic range of ERCC spike-in standards were sequenced on SOLiD.

Project description:Purpose: RNA editing by ADAR1 is essential for hematopoietic development. The goals of this study were firstly, to identify ADAR1-specific RNA-editing sites by indentifying A-to-I (G) mismatches in RNA-seq data compared to mm9 reference genome in wild type mice that were not edited or reduced in editing frequency in ADAR1E861A editing deficient mice. Secondly, to determine the transcriptional consequence of an absence of ADAR1-mediated A-to-I editing. Methods: Fetal liver mRNA profiles of embryonic day 12.5 wild-type (WT) and ADAR1 editing-deficient (ADAR1E861A) mice were generated by RNA sequencing, in triplicate (biological replicates), using Illumina HiSeq2000. The sequence reads that passed quality filters were analyzed at the transcript level with TopHat followed by Cufflinks. qRT–PCR validation was performed using SYBR Green assays. A-to-I (G) RNA editing sites were identified as previously described by Ramaswami G. et al., Nature Methods, 2012 using Burrows–Wheeler Aligner (BWA) followed by ANOVA (ANOVA). RNA editing sites were confirmed by Sanger sequencing. Results: Using an optimized data analysis workflow, we mapped about 30 million sequence reads per sample to the mouse genome (build mm9) and identified 14,484 transcripts in the fetal livers of WT and ADAR1E861A mice with BWA. RNA-seq data had a goodness of fit (R2) of >0.94 between biological triplicates per genotype. Approximately 4.4% of the transcripts showed differential expression between the WT and ADAR1E861A fetal liver, with a LogFC≥1.5 and p value <0.05. A profound upregulation of interferon stimulated genes were found to be massively upregulated (up to 11 logFC) in ADAR1E861A fetal liver compared to WT. 6,012 A-to-I RNA editing sites were identified when assessing mismatches in RNA-seq data of WT and ADAR1E861A fetal liver. Conclusions: Our study represents the first detailed analysis of fetal liver transcriptomes and A-to-I RNA editing sites, with biologic replicates, generated by RNA-seq technology. A-to-I RNA editing is the essential function of ADAR1 and is required to suppress interferon signaling to endogenous RNA. Fetal liver mRNA profiles of E12.5 wild type (WT) and ADAR E861A mutant mice were generated by deep sequencing, in triplicate, using Illumina HiSeq 200.

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MultiEditR: The first tool for detection and quantification of multiple RNA editing sites from Sanger sequencing demonstrates comparable fidelity to RNA-seq

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