Dataset Information

DRUMMER-Rapid detection of RNA modifications through comparative nanopore sequencing.

ABSTRACT: The chemical modification of ribonucleotides regulates the structure, stability, and interactions of RNAs. Profiling of these modifications using short-read (Illumina) sequencing techniques provides high sensitivity but low-to-medium resolution i.e., modifications cannot be assigned to specific transcript isoforms in regions of sequence overlap. An alternative strategy uses current fluctuations in nanopore-based long read direct RNA sequencing (DRS) to infer the location and identity of nucleotides that differ between two experimental conditions. While highly sensitive, these signal-level analyses require high quality transcriptome annotations and thus are best suited to the study of model organisms. By contrast, the detection of RNA modifications in microbial organisms which typically have no or low-quality annotations requires an alternative strategy. Here, we demonstrate that signal fluctuations directly influence error rates during base calling and thus provides an alternative approach for identifying modified nucleotides. DRUMMER (Detection of Ribonucleic acid Modifications Manifested in Error Rates (i) utilizes a range of statistical tests and background noise correction to identify modified nucleotides with high confidence, (ii) operates with similar sensitivity to signal-level analysis approaches, and (iii) correlates very well with orthogonal approaches. Using well-characterized DRS datasets supported by independent meRIP-Seq and miCLIP-Seq datasets we demonstrate that DRUMMER operates with high sensitivity and specificity. DRUMMER is written in Python 3 and is available as open source in the GitHub repository: https://github.com/DepledgeLab/DRUMMER. Supplementary data are available at Bioinformatics online.

SUBMITTER: Abebe JS

PROVIDER: S-EPMC9154255 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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DRUMMER-rapid detection of RNA modifications through comparative nanopore sequencing.

Abebe Jonathan S JS Price Alexander M AM Hayer Katharina E KE Mohr Ian I Weitzman Matthew D MD Wilson Angus C AC Depledge Daniel P DP

Bioinformatics (Oxford, England) 20220501 11

<h4>Motivation</h4>The chemical modification of ribonucleotides regulates the structure, stability and interactions of RNAs. Profiling of these modifications using short-read (Illumina) sequencing techniques provides high sensitivity but low-to-medium resolution i.e. modifications cannot be assigned to specific transcript isoforms in regions of sequence overlap. An alternative strategy uses current fluctuations in nanopore-based long read direct RNA sequencing (DRS) to infer the location and ide ...[more]

PMID: 35426900

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Project description:Modifications on viral RNAs (vRNAs), either genomic RNAs or RNA transcripts, have complex effects on the viral life cycle and cellular responses to viral infection. The advent of Oxford Nanopore Technologies Direct RNA Sequencing provides a new strategy for studying RNA modifications. To this end, multiple computational tools have been developed, but a systemic evaluation of their performance in mapping vRNA modifications is lacking. Here, 10 computational tools were tested using the Sindbis virus (SINV) RNAs isolated from infected mammalian (BHK-21) or mosquito (C6/36) cells, with in vitro-transcribed RNAs serving as modification-free control. Three single-mode approaches were shown to be inapplicable in the viral context, and three out of seven comparative methods required cutoff adjustments to reduce false-positive predictions. Utilizing optimized cutoffs, an integrated analysis of comparative tools suggested that the intersected predictions of Tombo_com and xPore were significantly enriched compared with the background. Consequently, a pipeline integrating Tombo_com and xPore was proposed for vRNA modification detection; the performance of which was supported by N6-methyladenosine prediction in severe acute respiratory syndrome coronavirus 2 RNAs using publicly available data. When applied to SINV RNAs, this pipeline revealed more intensive modifications in subgenomic RNAs than in genomic RNAs. Modified uridines were frequently identified, exhibiting substantive overlapping between vRNAs generated in different cell lines. On the other hand, the interpretation of other modifications remained unclear, underlining the limitations of the current computational tools despite their notable potential.IMPORTANCEComputational approaches utilizing Oxford Nanopore Technologies Direct RNA Sequencing data were almost exclusively designed to map eukaryotic epitranscriptomes. Therefore, extra caution must be exercised when using these tools to detect vRNA modifications, as in most cases, vRNA modification profiles should be regarded as unknown epitranscriptomes without prior knowledge. Here, we comprehensively evaluated the performance of 10 computational tools in detecting vRNA modification sites. All tested single-mode methods failed to differentiate native and in vitro-transcribed samples. Using optimized cutoff values, seven tested comparative tools generated very different predictions. An integrated analysis showed significant enrichment of Tombo_com and xPore predictions against the background. A pipeline for vRNA modification detection was proposed accordingly and applied to Sindbis virus RNAs. In conclusion, our study underscores the need for the careful application of computational tools to analyze viral epitranscriptomics. It also offers insights into alphaviral RNA modifications, although further validation is required.

Project description:Direct RNA sequencing from Oxford Nanopore Technologies (ONT) has become a valuable method for studying RNA modifications such as N6-methyladenosine (m6A), pseudouridine (ψ), and 5-methylcytosine (m5C). Recent advancements in the RNA004 chemistry substantially reduce sequencing errors compared to previous chemistries (e.g., RNA002), thereby promising enhanced accuracy for epitranscriptomic analysis. In this study, we benchmark the performance of two state-of-the-art RNA modification detection models capable of handling RNA004 data - ONT's Dorado and m6Anet - using two wild-type (WT) cell lines, HEK293T and HeLa, with respective ground truths from GLORI and eTAM-seq, and their paired in vitro transcribed (IVT) RNA as negative controls. We found that under default settings and considering sites with ≥10% modification ratio and ≥10X coverage, Dorado has higher recall (~0.92) than m6Anet (~0.51) for m6A detection. Among the overlapping methylated sites between ground truth and computational predictions, there are high correlations of site-specific m6A modification stoichiometry, with correlation coefficient of ~0.89 for Dorado-truth comparison and ~0.72 for m6Anet-truth comparison. However, combined assessment of WT and IVT datasets show that while the per-site false positive rate (FPR) can be lower (~8% for Dorado and ~33% for m6Anet), both computational tools can have high per-site false discovery rate (FDR) of m6A (~40% for Dorado and ~80% for m6Anet) due to the low prevalence of m6A in transcriptome, with a similar trend observed for pseudouridine (~95% FDR for Dorado). Additional motif analysis reveals that both Dorado and m6Anet exhibit high heterogeneity of false positive calls across sequence contexts, suggesting that sequence contexts help determine accuracy of specific modification calls. There is also a substantial overlap of false positive calls between the two IVT samples, suggesting a post-filtering strategy to improve modification calling by compiling a set of low-confidence sites with a probabilistic model from several IVT samples across diverse cells/tissues. Our analysis highlights key strengths and limitations of the current generation of m6A detection algorithms and offers insights into optimizing thresholds and interpretability. The IVT datasets generated by the RNA004 chemistry provides a publicly available benchmark resource for further development and refinement of computational methods.

Dataset Information

DRUMMER-Rapid detection of RNA modifications through comparative nanopore sequencing.

Publications

DRUMMER-rapid detection of RNA modifications through comparative nanopore sequencing.

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