Quantitative profiling of native RNA modifications and their dynamics using nanopore sequencing

ABSTRACTA 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 and quantify RNA modifications 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 (Y) or 2’-O-methylation (Nm) modifications. We then demonstrate the value of these signatures for de novo prediction of Y modifications transcriptome-wide, confirming known Y-modified sites as well as uncovering novel Y sites in mRNAs, ncRNAs and rRNAs, including a previously unreported Pus4-dependent Y 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, contrary to the general belief. By contrast, our method reveals many novel heat-sensitive Y-modified sites in snRNAs, snoRNAs and mRNAs, 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 and trace profiles, and quantify the RNA modification stoichiometry changes between two conditions. Our work demonstrates that Y RNA modifications can be predicted de novo and in a quantitative manner using native RNA nanopore sequencing.

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DeepSimulator: a deep simulator for Nanopore sequencing

10.1101/238683 ◽

2017 ◽

Cited By ~ 1

Author(s):

Yu Li ◽

Renmin Han ◽

Chongwei Bi ◽

Mo Li ◽

Sheng Wang ◽

...

Keyword(s):

Deep Learning ◽

De Novo ◽

Real Data ◽

Electrical Current ◽

Complex Nature ◽

Main Task ◽

Nanopore Sequencing ◽

Base Calling ◽

Context Dependent ◽

Sequencing Procedure

ABSTRACTMotivationOxford Nanopore sequencing is a rapidly developed sequencing technology in recent years. To keep pace with the explosion of the downstream data analytical tools, a versatile Nanopore sequencing simulator is needed to complement the experimental data as well as to benchmark those newly developed tools. However, all the currently available simulators are based on simple statistics of the produced reads, which have difficulty in capturing the complex nature of the Nanopore sequencing procedure, the main task of which is the generation of raw electrical current signals.ResultsHere we propose a deep learning based simulator, DeepSimulator, to mimic the entire pipeline of Nanopore sequencing. Starting from a given reference genome or assembled contigs, we simulate the electrical current signals by a context-dependent deep learning model, followed by a base-calling procedure to yield simulated reads. This workflow mimics the sequencing procedure more naturally. The thorough experiments performed across four species show that the signals generated by our context-dependent model are more similar to the experimentally obtained signals than the ones generated by the official context-independent pore model. In terms of the simulated reads, we provide a parameter interface to users so that they can obtain the reads with different accuracies ranging from 83% to 97%. The reads generated by the default parameter have almost the same properties as the real data. Two case studies demonstrate the application of DeepSimulator to benefit the development of tools in de novo assembly and in low coverage SNP detection.AvailabilityThe software can be accessed freely at: https://github.com/lykaust15/deep_simulator.

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Accurate detection of m6A RNA modifications in native RNA sequences

10.1101/525741 ◽

2019 ◽

Cited By ~ 6

Author(s):

Huanle Liu ◽

Oguzhan Begik ◽

Morghan C Lucas ◽

Christopher E. Mason ◽

Schraga Schwartz ◽

...

Keyword(s):

Rna Modifications ◽

Rna Sequences ◽

Single Nucleotide ◽

Rna Molecules ◽

Base Calling ◽

Oxford Nanopore ◽

Nucleotide Resolution ◽

The Universe ◽

Oxford Nanopore Technologies ◽

Single Nucleotide Resolution

ABSTRACTThe 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.

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Repurposing RNA sequencing for discovery of RNA modifications in clinical cohorts

Science Advances ◽

10.1126/sciadv.abd2605 ◽

2021 ◽

Vol 7 (32) ◽

pp. eabd2605

Author(s):

Kar-Tong Tan ◽

Ling-Wen Ding ◽

Chan-Shuo Wu ◽

Daniel G. Tenen ◽

Henry Yang

Keyword(s):

Rna Sequencing ◽

Cancer Progression ◽

De Novo ◽

Rna Modification ◽

Rna Modifications ◽

Multiple Cancer ◽

Patients With Cancer ◽

Statistical Framework ◽

Cancer Types ◽

Nucleotide Resolution

The study of RNA modifications in large clinical cohorts can reveal relationships between the epitranscriptome and human diseases, although this is especially challenging. We developed ModTect (https://github.com/ktan8/ModTect), a statistical framework to identify RNA modifications de novo by standard RNA-sequencing with deletion and mis-incorporation signals. We show that ModTect can identify both known (N1-methyladenosine) and previously unknown types of mRNA modifications (N2,N2-dimethylguanosine) at nucleotide-resolution. Applying ModTect to 11,371 patient samples and 934 cell lines across 33 cancer types, we show that the epitranscriptome was dysregulated in patients across multiple cancer types and was additionally associated with cancer progression and survival outcomes. Some types of RNA modification were also more disrupted than others in patients with cancer. Moreover, RNA modifications contribute to multiple types of RNA-DNA sequence differences, which unexpectedly escape detection by Sanger sequencing. ModTect can thus be used to discover associations between RNA modifications and clinical outcomes in patient cohorts.

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ModPhred: an integrative toolkit for the analysis and storage of nanopore sequencing DNA and RNA modification data

10.1101/2021.03.26.437220 ◽

2021 ◽

Author(s):

Leszek P Pryszcz ◽

Eva Maria Novoa

Keyword(s):

Rna Modification ◽

Nanopore Sequencing ◽

Computationally Efficient ◽

Rna Modifications ◽

Efficient Manner ◽

Dna And Rna ◽

File Formats ◽

User Friendly ◽

And Storage

DNA and RNA modifications can now be identified using Nanopore sequencing. However, we currently lack a flexible software to efficiently encode, store, analyze and visualize DNA and RNA modification data. Here we present ModPhred, a versatile toolkit that facilitates DNA and RNA modification analysis from nanopore sequencing reads in a user-friendly manner. ModPhred integrates probabilistic DNA and RNA modification information within the FASTQ and BAM file formats, can be used to encode multiple types of modifications simultaneously, and its output can be easily coupled to genomic track viewers, facilitating the visualization and analysis of DNA and RNA modification information in individual reads in a simple and computationally efficient manner. ModPhred is available at https://github.com/novoalab/modPhred, is implemented in Python3, and is released under an MIT license.

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poreTally: run and publish de novo Nanopore assembler benchmarks

10.1101/424184 ◽

2018 ◽

Author(s):

Carlos de Lannoy ◽

Judith Risse ◽

Dick de Ridder

Keyword(s):

Best Practices ◽

De Novo ◽

Nanopore Sequencing ◽

Base Calling ◽

Novel Approach ◽

Tool Performance ◽

Assembly Pipeline ◽

Nucleic Acid Analysis ◽

Sequencing Platforms ◽

Assembly Tool

AbstractNanopore sequencing is a novel approach to nucleic acid analysis that generates long, error-prone reads. Since device components, base calling software and best practices for sample preparation are updated frequently and extensively, the nature of the produced data also changes frequently. As a result, peer-reviewed publications on de novo assembly pipeline benchmarking efforts are quickly rendered outdated by the next major improvement to the sequencing platforms. To provide the user community with a faster, more flexible alternative to peer-reviewed benchmark papers for de novo assembly tool performance we constructed poreTally, a comprehensive benchmarking tool. poreTally automatically assembles a given read set using several often-used assembly pipelines, analyzes the resulting assemblies for correctness and continuity, and finally generates a quality report. Results can immediately be shared with peers in a Github/Gitlab repository. Furthermore, we aim to give a more inclusive overview of assembly pipeline performance than any individual research group can, by offering users the possibility to submit their results to a collective benchmarking effort. poreTally is available on Github.

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Gaussian mixture model-based unsupervised nucleotide modification number detection using nanopore-sequencing readouts

Bioinformatics ◽

10.1093/bioinformatics/btaa601 ◽

2020 ◽

Vol 36 (19) ◽

pp. 4928-4934 ◽

Cited By ~ 4

Author(s):

Hongxu Ding ◽

Andrew D Bailey ◽

Miten Jain ◽

Hugh Olsen ◽

Benedict Paten

Keyword(s):

De Novo ◽

Ionic Current ◽

Gaussian Mixture ◽

Supplementary Information ◽

Nanopore Sequencing ◽

Rna Molecules ◽

Dna And Rna ◽

Nucleotide Modification ◽

Oxford Nanopore

Abstract Motivation Nucleotide modification status can be decoded from the Oxford Nanopore Technologies nanopore-sequencing ionic current signals. Although various algorithms have been developed for nanopore-sequencing-based modification analysis, more detailed characterizations, such as modification numbers, corresponding signal levels and proportions are still lacking. Results We present a framework for the unsupervised determination of the number of nucleotide modifications from nanopore-sequencing readouts. We demonstrate the approach can effectively recapitulate the number of modifications, the corresponding ionic current signal levels, as well as mixing proportions under both DNA and RNA contexts. We further show, by integrating information from multiple detected modification regions, that the modification status of DNA and RNA molecules can be inferred. This method forms a key step of de novo characterization of nucleotide modifications, shedding light on the interpretation of various biological questions. Availability and implementation Modified nanopolish: https://github.com/adbailey4/nanopolish/tree/cigar_output. All other codes used to reproduce the results: https://github.com/hd2326/ModificationNumber. Supplementary information Supplementary data are available at Bioinformatics online.

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Induction of Promoter DNA Methylation Upon High-Pressure Spraying of Double-Stranded RNA in Plants

Agronomy ◽

10.3390/agronomy11040789 ◽

2021 ◽

Vol 11 (4) ◽

pp. 789

Author(s):

Athanasios Dalakouras ◽

Ioannis Ganopoulos

Keyword(s):

High Pressure ◽

De Novo ◽

Epigenetic Changes ◽

Double Stranded Rna ◽

Rna Molecules ◽

Promoter Sequences ◽

Promoter Dna Methylation ◽

Promoter Dna ◽

First Time ◽

De Novo Methylation

Exogenous application of RNA molecules is a potent method to trigger RNA interference (RNAi) in plants in a transgene-free manner. So far, all exogenous RNAi (exo-RNAi) applications have aimed to trigger mRNA degradation of a given target. However, the issue of concomitant epigenetic changes was never addressed. Here, we report for the first time that high-pressure spraying of dsRNAs can trigger de novo methylation of promoter sequences in plants.

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The m6A RNA methylation regulates oncogenic signaling pathways driving cell malignant transformation and carcinogenesis

Molecular Cancer ◽

10.1186/s12943-021-01356-0 ◽

2021 ◽

Vol 20 (1) ◽

Author(s):

Mohammad Burhan Uddin ◽

Zhishan Wang ◽

Chengfeng Yang

Keyword(s):

Signaling Pathways ◽

Malignant Transformation ◽

Noncoding Rna ◽

Rna Modification ◽

Oncogenic Signaling ◽

Aberrant Expression ◽

Rna Methylation ◽

Rna Molecules ◽

M6a Rna Methylation ◽

Oncogenic Signaling Pathways

AbstractThe m6A RNA methylation is the most prevalent internal modification in mammalian mRNAs which plays critical biological roles by regulating vital cellular processes. Dysregulations of the m6A modification due to aberrant expression of its regulatory proteins are frequently observed in many pathological conditions, particularly in cancer. Normal cells undergo malignant transformation via activation or modulation of different oncogenic signaling pathways through complex mechanisms. Accumulating evidence showing regulation of oncogenic signaling pathways at the epitranscriptomic level has added an extra layer of the complexity. In particular, recent studies demonstrated that, in many types of cancers various oncogenic signaling pathways are modulated by the m6A modification in the target mRNAs as well as noncoding RNA transcripts. m6A modifications in these RNA molecules control their fate and metabolism by regulating their stability, translation or subcellular localizations. In this review we discussed recent exciting studies on oncogenic signaling pathways that are modulated by the m6A RNA modification and/or their regulators in cancer and provided perspectives for further studies. The regulation of oncogenic signaling pathways by the m6A modification and its regulators also render them as potential druggable targets for the treatment of cancer.

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Hypoxia-induced alteration of RNA modifications in the mouse testis and sperm

Biology of Reproduction ◽

10.1093/biolre/ioab142 ◽

2021 ◽

Author(s):

Tong He ◽

Huanping Guo ◽

Xipeng Shen ◽

Xiao Wu ◽

Lin Xia ◽

...

Keyword(s):

Hypobaric Hypoxia ◽

Environmental Changes ◽

Epigenetic Inheritance ◽

Transcriptional Regulators ◽

Extreme Environment ◽

Mouse Testis ◽

Rna Modification ◽

Rna Modifications ◽

Sperm Counts ◽

Health Studies

Abstract Hypobaric hypoxia as an extreme environment in a plateau may have deleterious effects on human health. Studies have indicated that rush entry into a plateau may reduce male fertility and manifest in decreased sperm counts and weakened sperm motility. RNA modifications are sensitive to environmental changes and have recently emerged as novel post-transcriptional regulators in male spermatogenesis and intergenerational epigenetic inheritance. In the present study, we generated a mouse hypoxia model simulating the environment of 5500 meters in altitude for 35 days, which led to compromised spermatogenesis, decreased sperm counts, and an increased sperm deformation rate. Using this hypoxia model, we further applied our recently developed high-throughput RNA modification quantification platform based on LC–MS/MS, which exhibited the capacity to simultaneously examine 25 types of RNA modifications. Our results revealed an altered sperm RNA modifications signature in the testis (6 types) and mature sperm (11 types) under the hypoxia model, with 4 types showing overlap (Am, Gm, m7G, and m22G). Our data first drew the signature of RNA modification profiles and comprehensively analyzed the alteration of RNA modification levels in mouse testis and sperm under a mouse hypoxia model. These data may be highly related to human conditions under a similar hypoxia environment.

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RNA Modification Level Estimation with pulseR

Genes ◽

10.3390/genes9120619 ◽

2018 ◽

Vol 9 (12) ◽

pp. 619

Author(s):

Etienne Boileau ◽

Christoph Dieterich

Keyword(s):

Experimental Approach ◽

High Efficiency ◽

Rna Modification ◽

Model Parameters ◽

Metabolic Labeling ◽

Rna Seq ◽

Rna Modifications ◽

Log Odds ◽

Pros And Cons ◽

Wide Scale

RNA modifications regulate the complex life of transcripts. An experimental approach called LAIC-seq was developed to characterize modification levels on a transcriptome-wide scale. In this method, the modified and unmodified molecules are separated using antibodies specific for a given RNA modification (e.g., m6A). In essence, the procedure of biochemical separation yields three fractions: Input, eluate, and supernatent, which are subjected to RNA-seq. In this work, we present a bioinformatics workflow, which starts from RNA-seq data to infer gene-specific modification levels by a statistical model on a transcriptome-wide scale. Our workflow centers around the pulseR package, which was originally developed for the analysis of metabolic labeling experiments. We demonstrate how to analyze data without external normalization (i.e., in the absence of spike-ins), given high efficiency of separation, and how, alternatively, scaling factors can be derived from unmodified spike-ins. Importantly, our workflow provides an estimate of uncertainty of modification levels in terms of confidence intervals for model parameters, such as gene expression and RNA modification levels. We also compare alternative model parametrizations, log-odds, or the proportion of the modified molecules and discuss the pros and cons of each representation. In summary, our workflow is a versatile approach to RNA modification level estimation, which is open to any read-count-based experimental approach.

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