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      Nanopore native RNA sequencing of a human poly(A) transcriptome

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          Abstract

          High throughput cDNA sequencing technologies have advanced our understanding of transcriptome complexity and regulation. However, these methods lose information contained in biological RNA because the copied reads are often short and because modifications are not retained. We address these limitations using a native poly(A) RNA sequencing strategy developed by Oxford Nanopore Technologies (ONT). Our study generated 9.9 million aligned sequence reads for the human cell line GM12878, using thirty MinION flow cells at six institutions. These native RNA reads had a median length of 771 bases, and a maximum aligned length of over 21,000 bases. Mitochondrial poly(A) reads provided an internal measure of read length quality. We combined these long nanopore reads with higher accuracy short-reads and annotated GM12878 promoter regions, to identify 33,984 plausible RNA isoforms. We describe strategies for assessing 3′ poly(A) tail length, base modifications, and transcript haplotypes.

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          Most cited references54

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          Minimap2: pairwise alignment for nucleotide sequences

          Heng Li (2018)
          Recent advances in sequencing technologies promise ultra-long reads of ∼100 kb in average, full-length mRNA or cDNA reads in high throughput and genomic contigs over 100 Mb in length. Existing alignment programs are unable or inefficient to process such data at scale, which presses for the development of new alignment algorithms.
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            Comprehensive analysis of mRNA methylation reveals enrichment in 3' UTRs and near stop codons.

            Methylation of the N(6) position of adenosine (m(6)A) is a posttranscriptional modification of RNA with poorly understood prevalence and physiological relevance. The recent discovery that FTO, an obesity risk gene, encodes an m(6)A demethylase implicates m(6)A as an important regulator of physiological processes. Here, we present a method for transcriptome-wide m(6)A localization, which combines m(6)A-specific methylated RNA immunoprecipitation with next-generation sequencing (MeRIP-Seq). We use this method to identify mRNAs of 7,676 mammalian genes that contain m(6)A, indicating that m(6)A is a common base modification of mRNA. The m(6)A modification exhibits tissue-specific regulation and is markedly increased throughout brain development. We find that m(6)A sites are enriched near stop codons and in 3' UTRs, and we uncover an association between m(6)A residues and microRNA-binding sites within 3' UTRs. These findings provide a resource for identifying transcripts that are substrates for adenosine methylation and reveal insights into the epigenetic regulation of the mammalian transcriptome. Copyright © 2012 Elsevier Inc. All rights reserved.
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              Dynamic RNA Modifications in Gene Expression Regulation

              Over 100 types of chemical modifications have been identified in cellular RNAs. While the 5' cap modification and the poly(A) tail of eukaryotic mRNA play key roles in regulation, internal modifications are gaining attention for their roles in mRNA metabolism. The most abundant internal mRNA modification is N6-methyladenosine (m6A), and identification of proteins that install, recognize, and remove this and other marks have revealed roles for mRNA modification in nearly every aspect of the mRNA life cycle, as well as in various cellular, developmental, and disease processes. Abundant noncoding RNAs such as tRNAs, rRNAs, and spliceosomal RNAs are also heavily modified and depend on the modifications for their biogenesis and function. Our understanding of the biological contributions of these different chemical modifications is beginning to take shape, but it's clear that in both coding and noncoding RNAs, dynamic modifications represent a new layer of control of genetic information.
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                Author and article information

                Journal
                101215604
                32338
                Nat Methods
                Nat Methods
                Nature methods
                1548-7091
                1548-7105
                16 October 2019
                18 November 2019
                December 2019
                28 December 2020
                : 16
                : 12
                : 1297-1305
                Affiliations
                [1 ]Department of Biomedical Engineering, Johns Hopkins University, Baltimore, 21218, USA
                [2 ]Department of Biomolecular Engineering, University of California, Santa Cruz, 95064, USA
                [3 ]UCSC Genomics Institute, University of California, Santa Cruz, 95064, USA
                [4 ]Ontario Institute for Cancer Research, Toronto, M5G 0A3, Canada
                [5 ]Michael Smith Laboratories and Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, V6T 1Z4, Canada
                [6 ]University of Birmingham, B15 2TT, UK
                [7 ]DeepSeq, School of Life Sciences, University of Nottingham, NG7 2RD, UK
                [8 ]Department of Computer Science, University of Toronto, M5S 1A1, Canada
                Author notes
                [*]

                Contributed equally to the work.

                [**]

                Co-lead the project.

                AUTHOR CONTRIBUTIONS

                MA, WT, HEO, MJ, and JRT conceived the study. MA, ANB, and WT coordinated the collaboration. REW, NS, NH, JQ, JT, PCZ, HEO, MJ, JRT, NH, and TG acquired data. REW, ADT, NS, TG, ML, AP, NL, RR, ANB, PST, JTS, BP, HEO, JRT, WT, MA, and MJ analyzed and interpreted data. Specifically, REW performed a first pass analysis and data indexing; TG and RR performed the allele specific analysis; REW and RR performed the m6A modification analysis; PST and JTS designed and implemented the poly(A) tail length estimation software; ADT and ANB performed transcript isoform analysis; PST, WT, RR and NS performed the polyA tail analysis; MJ and HEO performed the A-to-I base modification analysis; JT, MJ, NL, and HEO performed sequencer performance analysis; and MA, MJ, HEO, ML, and AP performed mitochondrial gene expression analysis. The following were principally responsible for text and figures by topic: RNA preparation, nanopore sequencing, and computational pipeline (MJ, HEO, JRT, MA); native poly(A) RNA sequencing statistics (MJ, HEO, JRT, MA); FLAIR-based isoform detection and analysis (ADT, CMS, ANB); assignment of transcripts to parental alleles using nanopore reads (TG, RR, WT); Mitochondrially-encoded transcripts (MA, HEO, MJ, ML, AP); kmer coverage (HEO, MJ); 3′ poly(A) analysis (PST, JTS, WT, RR, TG); m6A analysis (REW, WT, RR, NS); A-to-I conversion (MJ, HEO). Manuscript revisions and edits (REW, ADT, PST, MJ, JRT, PCZ, TG, RR, NS, TPS, NL, BP, ML, JTP, HEO, ANB, MA, WT). KLJ and JG replicated and distributed GM12878 cells.

                [# ] Corresponding authors.
                Article
                NIHMS1540455
                10.1038/s41592-019-0617-2
                7768885
                31740818
                bcd726e4-72b4-4f6e-8b1f-e9cd474c1eb2

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