Prediction of m6A and m5C at single-molecule resolution reveals a cooccurrence of RNA modifications across the transcriptome

Mateos, Pablo Acera; Sethi, Aditya J; Guarnacci, Marco; Ravindran, Agin; Srivastava, Akanksha; Xu, Jiajia; Woodward, Katrina; Hamilton, William B.; Gao, Jing; Starrs, Lora; Hayashi, Rippei; Burgio, Gaétan; Wickramasinghe, Vihandha O.; Dehorter, Nathalie; Preiß, Thomas; Shirokikh, Nikolay E.; Eyras, Eduardo

doi:10.1101/2022.03.14.484124

Cited by 23 publications

(27 citation statements)

References 87 publications

(235 reference statements)

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“…We should note that all artificial mixes representing distinct RNA modification stoichiometries contained the same number of reads (n=1000), to ensure that coverage would not be a confounder in the analysis. Our results revealed that stoichiometry had a major effect in the detection of RNA modifications (Figure 2D), in agreement with previous works 57,59 . Notably, we observed that the effect of stoichiometry was strongly dependent on the RNA modification type.…”

Section: Performance Of Nanopore-based Rna Modification Detection Alg...supporting

confidence: 93%

“…RNA modifications can then be identified using two main approaches: (i) in the form of systematic base-calling 'errors' [47][48][49][50][51] , or (ii) in the form of alterations in the current signal (i.e., altered current intensities, dwell times and/or trace) 43,[52][53][54][55][56][57] . In recent years, a plethora of algorithms to detect RNA modifications in DRS datasets have been developed 47,53,[56][57][58][59] ; however, the overlap between predicted RNA modified sites by different algorithms is poor 49,57 , limiting our ability to extract meaningful biological conclusions from these datasets. Moreover, it is currently unclear how the performance of each algorithm varies depending on the RNA modification type, modification stoichiometry and sequencing depth, thus limiting the applicability of DRS for the detection of dynamically regulated RNA modifications in biological contexts 45 .…”

Section: Introductionmentioning

confidence: 99%

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Native RNA nanopore sequencing reveals antibiotic-induced loss of rRNA modifications in the A- and P-sites

Delgado-Tejedor

Medina

Begik

et al. 2023

Preprint

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The biological relevance and dynamics of mRNA modifications have been extensively studied in the past few years, revealing their key roles in major cellular processes, such as cellular differentiation or sex determination. However, whether rRNA modifications are dynamically regulated, and under which conditions, remains largely unclear. Here, we performed a systematic characterization of bacterial rRNA modification dynamics upon exposure to diverse antibiotics using native RNA nanopore sequencing. To identify significant rRNA modification changes, we developed NanoConsensus, a novel pipeline that integrates the estimates from multiple RNA modification detection algorithms, predicting differentially modified rRNA sites with very low false positive rates and high replicability. We showed that NanoConsensus is robust across RNA modification types, stoichiometries and coverage, and outperforms all individual algorithms tested. Using this approach, we identified multiple rRNA modifications that are lost upon the presence of antibiotics, showing that rRNA modification profiles are altered in an antibiotic-specific manner. We found that significantly altered rRNA modified sites upon antibiotic exposure are located in the vicinity of the A and P-sites of the ribosome, possibly contributing to antibiotic resistance. We then systematically examined whether loss of "antibiotic-sensitive" rRNA modifications may be sufficient to confer antibiotic resistance, finding that depletion of some rRNA modification enzymes guiding dysregulated rRNA modifications confers increased antibiotic resistance. Altogether, our work reveals that rRNA modification profiles can be rapidly altered in response to environmental exposures, and that nanopore sequencing can accurately identify dysregulated rRNA modifications, contributing to the mechanistic dissection of antibiotic resistance. Moreover, we provide a novel, robust workflow to study rRNA modification dynamics in any species using nanopore sequencing in a scalable and reproducible manner.

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Section: Performance Of Nanopore-based Rna Modification Detection Alg...supporting

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Native RNA nanopore sequencing reveals antibiotic-induced loss of rRNA modifications in the A- and P-sites

Delgado-Tejedor

Medina

Begik

et al. 2023

Preprint

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“…Information obtained from full-length native RNA nanopore sequencing has been used to detect isoform-specific modifications (Aw et al 2021), as well as to predict RNA modifications in individual reads (Begik et al 2021;Acera Mateos et al 2022), providing a resolution of the epitranscriptome at an unprecedented level, which had not been obtained using short-read sequencing data (Leger et al 2021). With the help of computational tools, unmodified and modified reads can be distinctly classified based on their unique current signal signatures, making it possible to detect even slight changes in the proportion of modified molecules, referred to as modification stoichiometry, across different conditions and cell types (Begik et al 2021;Pratanwanich et al 2021).…”

Section: Main Textmentioning

confidence: 99%

Exploring the epitranscriptome by native RNA sequencing

Begik

Mattick

Novoa

2022

RNA

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Chemical RNA modifications, collectively referred to as the ‘epitranscriptome’, are essential players in fine-tuning gene expression. Our ability to analyze RNA modifications has improved rapidly in recent years, largely due to the advent of high throughput sequencing methodologies, which typically consist of coupling modification-specific reagents, such as antibodies or enzymes, to next-generation sequencing. Recently, it also became possible to map RNA modifications directly by sequencing native RNAs using nanopore technologies, which has been applied for the detection of a number of RNA modifications, such as N6-methyladenosine (m6A), pseudouridine (Ψ) and inosine (I). However, the signal modulations caused by most RNA modifications have yet to be determined. A global effort is needed to determine the signatures of the full range of RNA modifications to avoid the technical biases that have so far limited our understanding of the epitranscriptome.

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“…RATTLE modularity, with the ability to parameterize each step, means it can be easily adapted to any sample type. Additionally, RATTLE rich output, including information about the predicted transcripts and genes, as well as the reads used to build each transcript, will prove valuable in downstream analyses, including the study of differential transcript usage [25], the analysis of single-cell long-read sequencing [26], and the identification of RNA modifications in non-model species [27]. RATTLE lays the foundation of exciting developments in long-read transcriptomics.…”

Section: Discussionmentioning

confidence: 99%

RATTLE: Reference-free reconstruction and quantification of transcriptomes from Nanopore sequencing

Rubia

Indi

Carbonell-Sala

et al. 2020

Preprint

Self Cite

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Single-molecule long-read sequencing provides an unprecedented opportunity to measure the transcriptome from any sample 1-3 . However, current methods for the analysis of transcriptomes from long reads rely on the comparison with a genome or transcriptome reference 2,4,5 , or use multiple sequencing technologies 6,7 .These approaches preclude the cost-effective study of species with no reference available, and the discovery of new genes and transcripts in individuals underrepresented in the reference. Methods for the assembly of DNA long-reads 8-10 cannot be directly transferred to transcriptomes since their consensus sequences lack the interpretability as genes with multiple transcript isoforms. To address these challenges, we have developed RATTLE, the first method for the reference-free reconstruction and quantification of transcripts from long reads. Using simulated data, transcript isoform spike-ins, and sequencing data from human and mouse tissues, we demonstrate that RATTLE accurately performs read clustering and error-correction.Furthermore, RATTLE predicts transcript sequences and their abundances with accuracy comparable to reference-based methods. RATTLE enables rapid and cost-effective long-read transcriptomics in any sample and any species, without the need of a genome or annotation reference and without using additional technologies.

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Prediction of m6A and m5C at single-molecule resolution reveals a cooccurrence of RNA modifications across the transcriptome

Cited by 23 publications

References 87 publications

Native RNA nanopore sequencing reveals antibiotic-induced loss of rRNA modifications in the A- and P-sites

Native RNA nanopore sequencing reveals antibiotic-induced loss of rRNA modifications in the A- and P-sites

Exploring the epitranscriptome by native RNA sequencing

RATTLE: Reference-free reconstruction and quantification of transcriptomes from Nanopore sequencing

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