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Decoding the Atlas of RNA Modifications from Epitranscriptome Sequencing Data

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Epitranscriptomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1870))

Abstract

Over 100 types of chemical modifications have been identified in protein-coding and noncoding RNAs (ncRNAs). However, the prevalence, regulation, and function of diverse RNA modifications remain largely unknown. In this chapter, we describe how to annotate, visualize, and analyze the RNA modification sites from the high-throughput epitranscriptome sequencing data using RMBase platform and software. We developed two stand-alone computational software, modAnnotator and metaProfile, to annotate and visualize RNA modification sites and their prevalence in the gene body. In addition, we constructed interactive web implementations to decode the atlas of various RNA modifications, including the N6-methyladenosine (m6A) modification, pseudouridine (Ψ) modification, 5-methylcytosine (m5C) modification, and 2′-O-methylation (2′-O-Me) modification, as well as other types of modifications. We also developed web-based interfaces to analyze the associations between RNA modification sites with miRNA target sites and disease-related single-nucleotide polymorphisms (SNPs). Moreover, RMBase provides a genome browser and a web-based modTool to query, annotate, and visualize various RNA modifications. RMBase is expected to provide comprehensive interfaces and tools to facilitate the analysis and functional study of the massive RNA modification sites. The software and platform are available at http://rna.sysu.edu.cn/rmbase/modSoftware.php.

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References

  1. Chen T, Hao YJ, Zhang Y, Li MM, Wang M, Han W, Wu Y, Lv Y, Hao J, Wang L, Li A, Yang Y, Jin KX, Zhao X, Li Y, Ping XL, Lai WY, Wu LG, Jiang G, Wang HL, Sang L, Wang XJ, Yang YG, Zhou Q (2015) M(6)A RNA methylation is regulated by microRNAs and promotes reprogramming to pluripotency. Cell Stem Cell 16(3):289–301. https://doi.org/10.1016/j.stem.2015.01.016

    Article  CAS  PubMed  Google Scholar 

  2. Geula S, Moshitch-Moshkovitz S, Dominissini D, Mansour AA, Kol N, Salmon-Divon M, Hershkovitz V, Peer E, Mor N, Manor YS, Ben-Haim MS, Eyal E, Yunger S, Pinto Y, Jaitin DA, Viukov S, Rais Y, Krupalnik V, Chomsky E, Zerbib M, Maza I, Rechavi Y, Massarwa R, Hanna S, Amit I, Levanon EY, Amariglio N, Stern-Ginossar N, Novershtern N, Rechavi G, Hanna JH (2015) Stem cells. m6A mRNA methylation facilitates resolution of naive pluripotency toward differentiation. Science 347(6225):1002–1006. https://doi.org/10.1126/science.1261417

    Article  CAS  PubMed  Google Scholar 

  3. Gilbert WV, Bell TA, Schaening C (2016) Messenger RNA modifications: Form, distribution, and function. Science 352(6292):1408–1412. https://doi.org/10.1126/science.aad8711

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Helm M, Motorin Y (2017) Detecting RNA modifications in the epitranscriptome: predict and validate. Nat Rev Genet 18(5):275–291. https://doi.org/10.1038/nrg.2016.169

    Article  CAS  PubMed  Google Scholar 

  5. Jaffrey SR (2014) An expanding universe of mRNA modifications. Nat Struct Mol Biol 21(11):945–946. https://doi.org/10.1038/nsmb.2911

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Kirchner S, Ignatova Z (2015) Emerging roles of tRNA in adaptive translation, signalling dynamics and disease. Nat Rev Genet 16(2):98–112. https://doi.org/10.1038/nrg3861

    Article  CAS  PubMed  Google Scholar 

  7. Lewis CJ, Pan T, Kalsotra A (2017) RNA modifications and structures cooperate to guide RNA-protein interactions. Nat Rev Mol Cell Biol 18(3):202–210. https://doi.org/10.1038/nrm.2016.163

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Li S, Mason CE (2014) The pivotal regulatory landscape of RNA modifications. Annu Rev Genomics Hum Genet 15:127–150. https://doi.org/10.1146/annurev-genom-090413-025405

    Article  CAS  PubMed  Google Scholar 

  9. Li X, Xiong X, Yi C (2016) Epitranscriptome sequencing technologies: decoding RNA modifications. Nat Methods 14(1):23–31. https://doi.org/10.1038/nmeth.4110

    Article  CAS  PubMed  Google Scholar 

  10. Machnicka MA, Milanowska K, Osman Oglou O, Purta E, Kurkowska M, Olchowik A, Januszewski W, Kalinowski S, Dunin-Horkawicz S, Rother KM, Helm M, Bujnicki JM, Grosjean H (2013) MODOMICS: a database of RNA modification pathways—2013 update. Nucleic Acids Res 41(Database issue):D262–D267. https://doi.org/10.1093/nar/gks1007

    Article  CAS  PubMed  Google Scholar 

  11. Meyer KD, Jaffrey SR (2014) The dynamic epitranscriptome: N6-methyladenosine and gene expression control. Nat Rev Mol Cell Biol 15(5):313–326. https://doi.org/10.1038/nrm3785

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Meyer KD, Saletore Y, Zumbo P, Elemento O, Mason CE, Jaffrey SR (2012) Comprehensive analysis of mRNA methylation reveals enrichment in 3′ UTRs and near stop codons. Cell 149(7):1635–1646. https://doi.org/10.1016/j.cell.2012.05.003

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Roundtree IA, Evans ME, Pan T, He C (2017) Dynamic RNA modifications in gene expression regulation. Cell 169(7):1187–1200. https://doi.org/10.1016/j.cell.2017.05.045

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Song CX, Yi C, He C (2012) Mapping recently identified nucleotide variants in the genome and transcriptome. Nat Biotechnol 30(11):1107–1116. https://doi.org/10.1038/nbt.2398

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Schwartz S, Bernstein DA, Mumbach MR, Jovanovic M, Herbst RH, Leon-Ricardo BX, Engreitz JM, Guttman M, Satija R, Lander ES, Fink G, Regev A (2014) Transcriptome-wide mapping reveals widespread dynamic-regulated pseudouridylation of ncRNA and mRNA. Cell 159(1):148–162. https://doi.org/10.1016/j.cell.2014.08.028

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Dominissini D, Moshitch-Moshkovitz S, Schwartz S, Salmon-Divon M, Ungar L, Osenberg S, Cesarkas K, Jacob-Hirsch J, Amariglio N, Kupiec M, Sorek R, Rechavi G (2012) Topology of the human and mouse m6A RNA methylomes revealed by m6A-seq. Nature 485(7397):201–206. https://doi.org/10.1038/nature11112

    Article  CAS  PubMed  Google Scholar 

  17. Li X, Xiong X, Wang K, Wang L, Shu X, Ma S, Yi C (2016) Transcriptome-wide mapping reveals reversible and dynamic N(1)-methyladenosine methylome. Nat Chem Biol 12(5):311–316. https://doi.org/10.1038/nchembio.2040

    Article  CAS  PubMed  Google Scholar 

  18. Li X, Zhu P, Ma S, Song J, Bai J, Sun F, Yi C (2015) Chemical pulldown reveals dynamic pseudouridylation of the mammalian transcriptome. Nat Chem Biol 11(8):592–597. https://doi.org/10.1038/nchembio.1836

    Article  CAS  PubMed  Google Scholar 

  19. Dai Q, Moshitch-Moshkovitz S, Han D, Kol N, Amariglio N, Rechavi G, Dominissini D, He C (2017) Nm-seq maps 2′-O-methylation sites in human mRNA with base precision. Nat Methods 14(7):695–698. https://doi.org/10.1038/nmeth.4294

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Carlile TM, Rojas-Duran MF, Zinshteyn B, Shin H, Bartoli KM, Gilbert WV (2014) Pseudouridine profiling reveals regulated mRNA pseudouridylation in yeast and human cells. Nature 515(7525):143–146. https://doi.org/10.1038/nature13802

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Linder B, Grozhik AV, Olarerin-George AO, Meydan C, Mason CE, Jaffrey SR (2015) Single-nucleotide-resolution mapping of m6A and m6Am throughout the transcriptome. Nat Methods 12(8):767–772. https://doi.org/10.1038/nmeth.3453

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Dominissini D, Nachtergaele S, Moshitch-Moshkovitz S, Peer E, Kol N, Ben-Haim MS, Dai Q, Di Segni A, Salmon-Divon M, Clark WC, Zheng G, Pan T, Solomon O, Eyal E, Hershkovitz V, Han D, Dore LC, Amariglio N, Rechavi G, He C (2016) The dynamic N(1)-methyladenosine methylome in eukaryotic messenger RNA. Nature 530(7591):441–446. https://doi.org/10.1038/nature16998

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Li X, Xiong X, Wang K, Wang L, Shu X, Ma S, Yi C (2016) Transcriptome-wide mapping reveals reversible and dynamic N-methyladenosine methylome. Nat Chem Biol 12:311. https://doi.org/10.1038/nchembio.2040

    Article  CAS  PubMed  Google Scholar 

  24. Khoddami V, Cairns BR (2013) Identification of direct targets and modified bases of RNA cytosine methyltransferases. Nat Biotechnol 31(5):458–464. https://doi.org/10.1038/nbt.2566

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Birkedal U, Christensen-Dalsgaard M, Krogh N, Sabarinathan R, Gorodkin J, Nielsen H (2015) Profiling of ribose methylations in RNA by high-throughput sequencing. Angew Chem Int Ed Engl 54(2):451–455. https://doi.org/10.1002/anie.201408362

    Article  CAS  PubMed  Google Scholar 

  26. Sun WJ, Li JH, Liu S, Wu J, Zhou H, Qu LH, Yang JH (2016) RMBase: a resource for decoding the landscape of RNA modifications from high-throughput sequencing data. Nucleic Acids Res 44(D1):D259–D265. https://doi.org/10.1093/nar/gkv1036

    Article  CAS  PubMed  Google Scholar 

  27. Dominissini D, Moshitch-Moshkovitz S, Salmon-Divon M, Amariglio N, Rechavi G (2013) Transcriptome-wide mapping of N(6)-methyladenosine by m(6)A-seq based on immunocapturing and massively parallel sequencing. Nat Protoc 8(1):176–189. https://doi.org/10.1038/nprot.2012.148

    Article  CAS  PubMed  Google Scholar 

  28. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR (2013) STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29(1):15–21. https://doi.org/10.1093/bioinformatics/bts635

    Article  CAS  PubMed  Google Scholar 

  29. Langmead B, Salzberg SL (2012) Fast gapped-read alignment with Bowtie 2. Nat Methods 9(4):357–359. https://doi.org/10.1038/nmeth.1923

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25(14):1754–1760. https://doi.org/10.1093/bioinformatics/btp324

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R (2009) The sequence alignment/map format and SAMtools. Bioinformatics. Genome Project Data Processing Subgroup 25(16):2078–2079. https://doi.org/10.1093/bioinformatics/btp352

    Article  CAS  Google Scholar 

  32. Meng J, Cui X, Rao MK, Chen Y, Huang Y (2013) Exome-based analysis for RNA epigenome sequencing data. Bioinformatics 29(12):1565–1567. https://doi.org/10.1093/bioinformatics/btt171

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Bailey TL, Williams N, Misleh C, Li WW (2006) MEME: discovering and analyzing DNA and protein sequence motifs. Nucleic Acids Res 34(Web Server):W369–W373. https://doi.org/10.1093/nar/gkl198

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Heinz S, Benner C, Spann N, Bertolino E, Lin YC, Laslo P, Cheng JX, Murre C, Singh H, Glass CK (2010) Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol Cell 38(4):576–589. https://doi.org/10.1016/j.molcel.2010.05.004

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Yang JH, Li JH, Shao P, Zhou H, Chen YQ, Qu LH (2011) starBase: a database for exploring microRNA-mRNA interaction maps from Argonaute CLIP-Seq and Degradome-Seq data. Nucleic Acids Res 39(Database issue):D202–D209. https://doi.org/10.1093/nar/gkq1056

    Article  CAS  PubMed  Google Scholar 

  36. Li JH, Liu S, Zhou H, Qu LH, Yang JH (2014) starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data. Nucleic Acids Res 42(Database issue):D92–D97. https://doi.org/10.1093/nar/gkt1248

    Article  CAS  PubMed  Google Scholar 

  37. Quinlan AR, Hall IM (2010) BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26(6):841–842. https://doi.org/10.1093/bioinformatics/btq033

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Skinner ME, Uzilov AV, Stein LD, Mungall CJ, Holmes IH (2009) JBrowse: a next-generation genome browser. Genome Res 19(9):1630–1638. https://doi.org/10.1101/gr.094607.109

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgments

This research is supported by National Key R&D Program of China (2017YFA0504400). National Natural Science Foundation of China (91440110, 31770879, 31370791, and 81702945); the funds from Guangdong Province (2017A030313106 and 2017A030313483); The project of Science and Technology New Star in ZhuJiang Guangzhou city (No. 2012J2200025); Fundamental Research Funds for the Central Universities (2011330003161070, 14lgjc18,2017MS071); Seeding project fund at School of Medicine, South China University of Technology (yxy2016005). Guangdong Province Key Laboratory of Computational Science and the Guangdong Province Computational Science Innovative Research Team.

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Authors and Affiliations

  1. School of Medicine, South China University of Technology (SCUT), Guangzhou, China

    Xiao-Qin Zhang

  2. Key Laboratory of Gene Engineering of the Ministry of Education, State Key Laboratory for Biocontrol, Sun Yat-sen University, Guangzhou, China

    Jian-Hua Yang

Authors
  1. Xiao-Qin Zhang
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  2. Jian-Hua Yang
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Correspondence to Jian-Hua Yang .

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Editors and Affiliations

  1. Department of Pathology, Yale University School of Medicine, New Haven, CT, USA

    Narendra Wajapeyee

  2. Yale Cancer Center, Yale University School of Medicine, New Haven, CT, USA

    Romi Gupta

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Zhang, XQ., Yang, JH. (2019). Decoding the Atlas of RNA Modifications from Epitranscriptome Sequencing Data. In: Wajapeyee, N., Gupta, R. (eds) Epitranscriptomics. Methods in Molecular Biology, vol 1870. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8808-2_8

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  • DOI: https://doi.org/10.1007/978-1-4939-8808-2_8

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  • Publisher Name: Humana Press, New York, NY

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