Accurate detection of m6A RNA modifications in native RNA sequences [Curlcake constructs]
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ABSTRACT:
PROVIDER: PRJNA511582 | ENA |
REPOSITORIES: ENA
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Project description:The field of epitranscriptomics has experienced a major revolution in the last few years; however, a major limitation is the lack of generic methods to map RNA modifications transcriptome-wide. Here we show that using the platform offered by Oxford Nanopore Technologies, we can detect N6-methyladenosine (m6A) RNA modifications with high accuracy. Our results open new avenues to investigate the universe of RNA modifications with single nucleotide, single molecule resolution.
2019-07-10 | GSE124309 | GEO
Project description:Accurate detection of m6A RNA modifications in native RNA sequences [Yeast]
| PRJNA521324 | ENA
Project description:The field of epitranscriptomics has undergone an enormous expansion in the last few years; however, a major limitation is the lack of generic methods to map RNA modifications transcriptome-wide. Here we show that using Oxford Nanopore Technologies, N6-methyladenosine (m6A) RNA modifications can be detected with high accuracy, in the form of systematic errors and decreased base-calling qualities. Our results open new avenues to investigate the universe of RNA modifications with single nucleotide resolution, in individual RNA molecules.
2019-09-09 | GSE126213 | GEO
Project description:The epitranscriptomics field has undergone an enormous expansion in the last few years; however, a major limitation is the lack of generic methods to map RNA modifications transcriptome-wide. Here, we show that using direct RNA sequencing, N6-methyladenosine (m6A) RNA modifications can be detected with high accuracy, in the form of systematic errors and decreased base-calling qualities. Specifically, we find that our algorithm, trained with m6A-modified and unmodified synthetic sequences, can predict m6A RNA modifications with ~90% accuracy. We then extend our findings to yeast data sets, finding that our method can identify m6A RNA modifications in vivo with an accuracy of 87%. Moreover, we further validate our method by showing that these 'errors' are typically not observed in yeast ime4-knockout strains, which lack m6A modifications. Our results open avenues to investigate the biological roles of RNA modifications in their native RNA context.
| S-EPMC6734003 | biostudies-literature
Project description:Bacterial DNA is subject to various modifications involved in gene regulation and defense against bacteriophage attacks. Phosphorothioate (PT) modifications are protective modifications in which the non-bridging oxygen in the DNA phosphate backbone is replaced with a sulfur atom. Here, we expand third-generation sequencing techniques to allow for the sequence-specific mapping of DNA modifications by demonstrating the application of Oxford Nanopore Technologies (ONT) and the ELIGOS software package for site-specific detection and characterization of PT modifications. The ONT/ELIGOS platform accurately detected PT modifications in a plasmid carrying synthetic PT modifications. Subsequently, studies were extended to the genome-wide mapping of PT modifications in the Salmonella enterica genomes within the wild-type strain and strains lacking the PT regulatory gene dndB (ΔdndB) or the PT synthetic gene dndC (ΔdndC). PT site-specific signatures were observed in the established motifs of GAAC/GTTC. The PT site locations were in close agreement with PT sites previously identified using the Nick-seq technique. Compared to the wild-type strain, the number of PT modifications are 1.8-fold higher in ΔdndB and 25-fold lower in ΔdndC, again consistent with known regulation of the dnd operon. These results demonstrate the suitability of the ONT platform for accurate detection and identification of the unusual PT backbone modifications in native genome sequences.
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Project description:Mutational profiling (MaP) enables detection of sites of chemical modification in RNA as sequence changes during reverse transcription (RT), subsequently read out by massively parallel sequencing. We introduce ShapeMapper 2, which integrates careful handling of all classes of adduct-induced sequence changes, sequence variant correction, basecall quality filters, and quality-control warnings to now identify RNA adduct sites as accurately as achieved by careful manual analysis of electrophoresis data, the prior highest-accuracy standard. MaP and ShapeMapper 2 provide a robust, experimentally concise, and accurate approach for reading out nucleic acid chemical probing experiments.
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Project description:N6-methyladenosine (m6A) is the most abundant internal RNA modification in eukaryotic mRNAs and influences many aspects of RNA processing. miCLIP (m6A individual-nucleotide resolution UV crosslinking and immunoprecipitation) is an antibody-based approach to map m6A sites with single-nucleotide resolution. However, due to broad antibody reactivity, reliable identification of m6A sites from miCLIP data remains challenging. Here, we present miCLIP2 in combination with machine learning to significantly improve m6A detection. The optimized miCLIP2 results in high-complexity libraries from less input material. Importantly, we established a robust computational pipeline to tackle the inherent issue of false positives in antibody-based m6A detection. The analyses were calibrated with Mettl3 knockout cells to learn the characteristics of m6A deposition, including m6A sites outside of DRACH motifs. To make our results universally applicable, we trained a machine learning model, m6Aboost, based on the experimental and RNA sequence features. Importantly, m6Aboost allows prediction of genuine m6A sites in miCLIP2 data without filtering for DRACH motifs or the need for Mettl3 depletion. Using m6Aboost, we identify thousands of high-confidence m6A sites in different murine and human cell lines, which provide a rich resource for future analysis. Collectively, our combined experimental and computational methodology greatly improves m6A identification.
| S-EPMC8450095 | biostudies-literature
Project description:Tendons and ligaments are important biological structures in both humans and animals. They are part of dense connective tissue and are crucial to the functioning of the musculoskeletal system. However, they are commonly damaged due to age-related wear and tear, trauma or sports related incidents, which can lead to severe immobility for the individual and can lead to injury of other tissues and development of degenerative joint disease such as osteoarthritis. Tissue engineering can offer great potential in the treatment of tendon and ligament injury by seeking a biological replacement with fully regenerated autologenous tissue. This approach commonly involves an artificial ECM (scaffold) on which cell can proliferate and differentiate with subsequent new tissue generation. Fibrin is a natural scaffold with no expected toxic degradation or inflammatory reaction and can be used as an autologous scaffold for fibroblast from connective tissue to create a three dimensional structure. The purpose of this study was to compare the proteomic differences between native tendon, ligament and 3D tissue engineered tendon and ligament fibrin constructs.
2016-06-27 | PXD003094 | Pride
Project description:Deep transcriptome sequencing (RNA-Seq) has become a vital tool for studying the state of cells in the context of varying environments, genotypes and other factors. RNA-Seq profiling data enable identification of novel isoforms, quantification of known isoforms and detection of changes in transcriptional or RNA-processing activity. Existing approaches to detect differential isoform abundance between samples either require a complete isoform annotation or fall short in providing statistically robust and calibrated significance estimates. Here, we propose a suite of statistical tests to address these open needs: a parametric test that uses known isoform annotations to detect changes in relative isoform abundance and a non-parametric test that detects differential read coverages and can be applied when isoform annotations are not available. Both methods account for the discrete nature of read counts and the inherent biological variability. We demonstrate that these tests compare favorably to previous methods, both in terms of accuracy and statistical calibrations. We use these techniques to analyze RNA-Seq libraries from Arabidopsis thaliana and Drosophila melanogaster. The identified differential RNA processing events were consistent with RT-qPCR measurements and previous studies. The proposed toolkit is available from http://bioweb.me/rdiff and enables in-depth analyses of transcriptomes, with or without available isoform annotation.
| S-EPMC3664801 | biostudies-other
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