Systematic comparison of tools used for m6A mapping from nanopore direct RNA sequencing
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ABSTRACT:
PROVIDER: PRJNA801506 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR17803871_1.fastq.gz | Fastqsanger.gz | |||
| SRR17803872_1.fastq.gz | Fastqsanger.gz |
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Project description:State-of-the-art algorithms for m6A detection and quantification via nanopore direct RNA sequencing have been continuously developed, little is known about their capacities and limitations, which makes a comprehensive assessment in urgent need. Therefore, we performed comprehensive benchmarking of 10 computational tools relying on current-based and base-calling “errors” strategies for m6A detection by nanopore sequencing.
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Project description:N6-methyladenosine (m6A) has been increasingly recognized as a new and important regulator of gene expression. To date, transcriptome-wide m6A detection primarily relies on well-established methods using next-generation sequencing (NGS) platform. However, direct RNA sequencing (DRS) using the Oxford Nanopore Technologies (ONT) platform has recently emerged as a promising alternative method to study m6A. While multiple computational tools are being developed to facilitate the direct detection of nucleotide modifications, little is known about the capabilities and limitations of these tools. Here, we systematically compare ten tools used for mapping m6A from ONT DRS data. We find that most tools present a trade-off between precision and recall, and integrating results from multiple tools greatly improve performance. Using a negative control could improve precision by subtracting certain intrinsic bias. We also observed variation in detection capabilities and quantitative information among motifs, and identified sequencing depth and m6A stoichiometry as potential factors affecting performance. Our study provides insight into the computational tools currently used for mapping m6A based on ONT DRS data and highlights the potential for further improving these tools, which may serve as the basis for future research.
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Project description:XIST is a long non-coding RNA (lncRNA) that mediates transcriptional silencing of X chromosome genes. Here we show that XIST is highly methylated with at least 78 N6-methyladenosine (m6A) residues, a reversible base modification whose function in lncRNAs is unknown. We show that m6A formation in XIST, as well as cellular mRNAs, is mediated by RBM15 and its paralog RBM15B, which bind the m6A-methylation complex and recruit it to specific sites in RNA. This results in methylation of adenosines in adjacent m6A consensus motifs. Furthermore, knockdown of RBM15 and RBM15B, or knockdown of the m6A methyltransferase METTL3 impairs XIST-mediated gene silencing. A systematic comparison of m6A-binding proteins shows that YTHDC1 preferentially recognizes m6A in XIST and is required for XIST function. Additionally, artificial tethering of YTHDC1 to XIST rescues XIST-mediated silencing upon loss of m6A. These data reveal a pathway of m6A formation and recognition required for XIST-mediated transcriptional repression. Three to four biological HEK293T replicates were used to perform iCLIP of endogenous YTH proteins, RBM15, and RBM15B. Crosslinking induced truncations were identified using CIMS-CITS pipeline.
2016-09-07 | E-GEOD-78030 | biostudies-arrayexpress
Project description:N6-methyladenosine (m6A) has been one of the most abundant and well-known modifications in mRNA since its discovery in 1970s. Recent studies have demonstrated that m6A gets involved in various biological processes such as alternative splicing and RNA degradation, playing an important role in all kinds of diseases. To better understand the role of m6A, transcriptome-wide m6A profiling data is indispensable. In these years, the Oxford Nanopore Technology Direct RNA Sequencing (DRS) platform has shown promise in RNA modification detection based on current disruptions measured in transcripts. However, decoding current intensity data into modification profiles remains a challenging task. Here, we introduce m6A Transcriptome-wide Mapper (m6ATM), a novel Python-based computational pipeline that applies deep neural networks to predict m6A sites at single-base resolution using DRS data. The m6ATM model architecture incorporates a WaveNet encoder and a dual-stream multiple instance learning model to extract features from specific target sites and characterize the m6A epitranscriptome. For validation, m6ATM achieved an accuracy of 80 to 98% across in-vitro transcription datasets containing varying m6A modification ratios and outperformed other tools in benchmarking with human cell-line data. Moreover, we demonstrated the versatility of m6ATM in providing reliable stoichiometric information and used it to pinpoint PEG10 as a potential m6A target transcript in liver cancer cells. In conclusion, we showed that m6ATM is a high-performance m6A detection tool and our results paved the way for epitranscriptomic precision medicine.
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Project description:N6-methyladenosine (m6A) has been one of the most abundant and well-known modifications in mRNA since its discovery in 1970s. Recent studies have demonstrated that m6A gets involved in various biological processes such as alternative splicing and RNA degradation, playing an important role in all kinds of diseases. To better understand the role of m6A, transcriptome-wide m6A profiling data is indispensable. In these years, the Oxford Nanopore Technology Direct RNA Sequencing (DRS) platform has shown promise in RNA modification detection based on current disruptions measured in transcripts. However, decoding current intensity data into modification profiles remains a challenging task. Here, we introduce m6A Transcriptome-wide Mapper (m6ATM), a novel Python-based computational pipeline that applies deep neural networks to predict m6A sites at single-base resolution using DRS data. The m6ATM model architecture incorporates a WaveNet encoder and a dual-stream multiple instance learning model to extract features from specific target sites and characterize the m6A epitranscriptome. For validation, m6ATM achieved an accuracy of 80 to 98% across in-vitro transcription datasets containing varying m6A modification ratios and outperformed other tools in benchmarking with human cell-line data. Moreover, we demonstrated the versatility of m6ATM in providing reliable stoichiometric information and used it to pinpoint PEG10 as a potential m6A target transcript in liver cancer cells. In conclusion, we showed that m6ATM is a high-performance m6A detection tool and our results paved the way for epitranscriptomic precision medicine.
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Project description:RNA modifications are integral to regulation of RNA metabolism. One such abundant mRNA modification is m6A, which impacts various aspects of RNA metabolism including splicing, transport and degradation. Current knowledge about proteins recruited to m6A to carry out these molecular processes is still limited. Here we describe a comprehensive and systematic mass spectrometry-based screening of m6A interactors in various cell types and species. Amongst the main findings, we identified G3BP1 as a protein, which is repelled by m6A and which positively regulates mRNA stability in an m6A regulated manner. Furthermore, we identified FMR1 as a novel, RNA sequence context dependent m6A reader, thus revealing a connection between an mRNA modification and an autism spectrum disorder. Collectively, our data represents a rich resource for the community and sheds further light on the complex interplay between m6A, m6A interactors and mRNA homeostasis.
2017-08-22 | PXD006498 | Pride
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