MRT-ModSeq - Rapid detection of RNA modifications with MarathonRT
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ABSTRACT:
ORGANISM(S): Escherichia coli Saccharomyces cerevisiae Homo sapiens
PROVIDER: GSE202160 | GEO | 2023/08/07
REPOSITORIES: GEO
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Project description:MRT-ModSeq - Rapid detection of RNA modifications with MarathonRT
| PRJNA834813 | ENA
Project description:Thermostable reverse transcriptases are workhorse enzymes underlying nearly all modern techniques for RNA structure mapping and for transcriptome-wide discovery of RNA chemical modifications. Despite their wide use, these enzymes’ behaviors at chemical modified nucleotides remain poorly understood. Wellington-Oguri et al. recently reported an apparent loss of chemical modification within putatively unstructured polyadenosine stretches modified by dimethyl sulfate or 2’ hydroxyl acylation, as probed by reverse transcription. Here, re-analysis of these and other publicly available data, capillary electrophoresis experiments on chemically modified RNAs, and nuclear magnetic resonance spectroscopy on A 12 and variants show that this effect is unlikely to arise from an unusual structure of polyadenosine. Instead, tests of different reverse transcriptases on chemically modified RNAs and molecules synthesized with single 1-methyladenosines implicate a previously uncharacterized reverse transcriptase behavior: near-quantitative bypass through chemical modifications within polyadenosine stretches. All tested natural and engineered reverse transcriptases (MMLV; SuperScript II, III, and IV; TGIRT-III; and MarathonRT) exhibit this anomalous bypass behavior. Accurate DMS-guided structure modeling of the polyadenylated HIV-1 3´ untranslated region RNA requires taking into account this anomaly. Our results suggest that poly(rA-dT) hybrid duplexes can trigger unexpectedly effective reverse transcriptase bypass and that chemical modifications in poly(A) mRNA tails may be generally undercounted.
2020-04-22 | GSE149061 | GEO
Project description:We report RBS-Seq, a new RNA bisulfite sequencing method enabling the sensitive and simultaneous detection of m5C, pseudouridine, and m1A at single-base resolution transcriptome-wide. For each, we detect ‘signature’ base mismatches (for m5C and m1A), or 1-2 base deletions (for pseudouridine) structurally explained by ribose ring-opened pseudouridine-mono-bisulfite adducts. Our profiles for pseudouridine reveal clear signatures at known sites in tRNAs and rRNAs, and provide hundreds of new targets in non-coding RNAs and mRNAs. However, our results diverge greatly from prior work, suggesting ~10-fold fewer m5C sites, and the absence of substantial m1A in mRNAs.
2019-03-08 | GSE90963 | GEO
Project description:Chemical RNA modifications, collectively referred to as the ‘epitranscriptome’, have been intensively studied during the last years, largely facilitated by the use of next-generation sequencing technologies. Recent efforts have turned towards the nanopore direct RNA sequencing (DRS) platform, as it allows simultaneous detection of diverse RNA modification types in full-length native RNA molecules. While RNA modifications can be identified in the form of systematic basecalling ‘errors’ in DRS datasets, m6A modifications produce very modest ‘errors’, limiting the applicability of this approach to sites that are modified at high stoichiometries. Here, we demonstrate that the use of alternative RNA basecalling models, trained with fully-unmodified in vitro synthetic sequences, increase the ‘error’ signal of m6A modifications, leading to enhanced detection of RNA modifications even at lower stoichiometries. We then show that the use of these models enhances the detection of RNA modifications on previously published in vivo human samples, using third-party softwares for the detection of RNA modifications. Moreover, our work provides a novel RNA basecalling model that shows a median accuracy of 97%, compared to previously available RNA basecalling models that show 91% accuracy. Notably, this increase in accuracy does not only lead to improved detection of RNA modifications, but also enhanced mappability of RNA reads, which becomes more evident in the case of short RNA reads (50% increase). Altogether, our work stresses the importance of using fully unmodified RNA sequences for training RNA basecalling models, and how the use of different basecalling models can significantly affect the detection of RNA modifications and read mappability.
2024-08-26 | GSE246151 | GEO
Project description:We combined electrophoresis and nanopore sequencing to analyze MarathonRT velocity over the heterogeneous sequences and structures of HOTAIR RNA template and reveal that the local sequences and structures of the template have negligible effect to MarathonRT, and MarathonRT can copy the long RNA in a single turnover, which leads to unusually synchronized primer extension at a constant speed of 25 nt/sec. We further demonstrate that ultra-stable RNA structure insertions do not obstruct MarathonRT, suggesting that MarathonRT can immediately disrupt any RNA structures within the template.
2022-06-12 | GSE195979 | GEO
Project description: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.
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Project description:An increasing amount of evidence indicates that fragments derived from longer small RNAs such as snoRNAs, snRNAs, and rRNAs may hold functions relevant to biology and disease. However, they remain poorly understood. Few pipelines exist to analyze the fragmentation of small RNAs, and even fewer take users from raw reads to processed results. Every decision that a user makes when processing small RNA-seq data can greatly impact downstream results. Thus, we sought to develop sRNAfrag, a standardized workflow that can accurately quantify and describe the presence of sRNA fragments, with three inputs: run data, a reference genome, and an annotation file.
2023-08-20 | GSE239311 | GEO
Project description:A broad diversity of modifications decorate RNA molecules. Originally conceived as static components, evidence is accumulating that some RNA modifications may be dynamic, contributing to cellular responses to external signals and environmental circumstances. A major difficulty in studying these modifications, however, is the need of tailored protocols to map each modification type individually. Here, we present a new approach that uses direct RNA nanopore sequencing to identify diverse RNA modification types present in native RNA molecules. First, we show that each RNA modification type results in a distinct and characteristic base-calling ‘error’ signature, which we validate using a battery of genetic strains lacking either pseudouridine (Ѱ) or 2’-O-methylation (Nm) modifications at known sites. We then demonstrate the value of these signatures for de novo transcriptome-wide prediction of Ѱ modifications, confirming known Ѱ-modified sites in rRNAs, snRNAs and mRNAs, as well as uncovering novel Ѱ sites including a previously unreported Pus4-dependent Ѱ modification in yeast mitochondrial rRNA, which we validate using orthogonal methods. To explore the dynamics of pseudouridylation across environmental stresses, we treat the cells with oxidative, cold and heat stresses, finding that yeast ribosomal rRNA modifications do not change upon environmental exposures. By contrast, our method reveals over a dozen novel heat-sensitive Ѱ-modified sites in snRNAs and snoRNAs, in addition to recovering previously reported sites. Finally, we develop a novel software, nanoRMS, which we show can estimate per-site modification stoichiometries from individual RNA molecules by identifying the reads with altered current intensity profiles, and quantify the RNA modification stoichiometry changes between two conditions. Our work demonstrates that Ѱ RNA modifications can be predicted de novo and in a quantitative manner using native RNA nanopore sequencing.
2021-04-23 | GSE148603 | GEO
Project description:Small nucleolar RNA (snoRNA) are non-coding RNAs, which participate in the chemical modification of ribosomal RNAs (rRNAs) and small nuclear RNAs. However, the roles of snoRNA in homeostasis of hematopoietic stem cells(HSCs) have not been studied. We isolated four hematopoietic stem and progenitor cells (HSPCs) (LT-HSCs, IT-HSCs, ST-HSCs, and MPPs) from the bone marrow (BM) of C57BL/6 mice and performed small RNA-seq. We found SnoRNAs belonging to SNORD113-114 cluster were specifically enriched in LT-HSCs, and their expression decreased dramatically with HSC differentiation. Here, we used a modified small RNA-seq method to examine snoRNA profiles of hematopoietic progenitor and mature cells in WT mice.
2024-10-30 | GSE269874 | GEO
Project description:We previously reported a microarray-based diagnostic system for heart transplant endomyocardial biopsies (EMBs), using either 3-archetype (3AA) or 4-archetype (4AA) unsupervised algorithms to estimate rejection. The present study aimed to examine the stability of machine-learning algorithms in new biopsies, compare 3AA vs. 4AA algorithms, assess supervised binary classifiers trained on histologic or molecular diagnoses, create a report combining many scores into an ensemble of estimates, and examine possible automated sign-outs. Algorithms derived in cohort 331 performed similarly in new biopsies despite differences in case mix. In the combined cohort, the 4AA model, including a parenchymal injury score, retained correlations with histologic rejection and DSA similar to the 3AA model. Supervised molecular classifiers predicted molecular rejection (AUCs>0.87) better than histologic rejection (AUCs<0.78), even when trained on histology diagnoses. A report incorporating many AA and binary classifier scores interpreted by one expert showed highly significant agreement with histology (p<0.001), but with many discrepancies as expected from the known noise in histology. An automated random forest score closely predicted expert diagnoses, confirming potential for automated sign-outs.Molecular algorithms are stable in new populations and can be assembled into an ensemble that combines many supervised and unsupervised estimates of the molecular disease states. (ClinicalTrials.gov NCT02670408).
2019-08-16 | GSE124897 | GEO