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Analysis of High-Throughput RNA Bisulfite Sequencing Data

  • Dietmar RiederEmail author
  • Francesca Finotello
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1562)

Abstract

Methylation of the 5-cytosine (m5C) is a common but not well-understood RNA modification, which can be detected by sequencing of bisulfite-treated transcripts (RNA-BSseq). In this Chapter, we discuss computational RNA-BSseq data analysis methods for transcriptome-wide identification and quantification of m5C.

Key words

RNA-BSseq RNA methylation m5Differential methylation 5-methylcytosine Transcription RNA modification Bisulfite conversion High-throughput sequencing Data analysis RNA Modification (Cytosine-5) methylation Sodium bisulfite 

References

  1. 1.
    Schaefer M (2015) Chapter fourteen – RNA 5-methylcytosine analysis by bisulfite sequencing. Methods Enzymol 560:297–329. doi:  10.1016/bs.mie.2015.03.007, ISSN 0076-6879, ISBN 9780128021927. http://www.sciencedirect.com/science/article/pii/S0076687915002335
  2. 2.
    Cantara WA, Crain PF, Rozenski J et al (2011) The RNA modification database, RNAMDB: 2011 update. Nucleic Acids Res 39:D195–D201. doi: 10.1093/nar/gkq1028 CrossRefPubMedGoogle Scholar
  3. 3.
    Motorin Y, Lyko F, Helm M (2010) 5-methylcytosine in RNA: detection, enzymatic formation and biological functions. Nucleic Acids Res 38:1415–1430. doi: 10.1093/nar/gkp1117 CrossRefPubMedGoogle Scholar
  4. 4.
    Amort T, Soulière MF, Wille A et al (2013) Long non-coding RNAs as targets for cytosine methylation. RNA Biol 10:1003–1008. doi: 10.4161/rna.24454 CrossRefPubMedGoogle Scholar
  5. 5.
    Schaefer M (2015) Chapter fourteen – RNA 5-methylcytosine analysis by bisulfite sequencing. Methods Enzymol 560:297–329. doi:  10.1016/bs.mie.2015.03.007, ISSN 0076–6879, ISBN 9780128021927. http://www.sciencedirect.com/science/article/pii/S0076687915002335
  6. 6.
    Squires JE, Patel HR, Nousch M et al (2012) Widespread occurrence of 5-methylcytosine in human coding and non-coding RNA. Nucleic Acids Res 40:5023–5033. doi: 10.1093/nar/gks144 CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Khoddami V, Cairns BR (2013) Identification of direct targets and modified bases of RNA cytosine methyltransferases. Nat Biotechnol 31:458–464. doi: 10.1038/nbt.2566 CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Hussain S, Sajini AA, Blanco S et al (2013) NSun2-mediated cytosine-5 methylation of vault noncoding RNA determines its processing into regulatory small RNAs. Cell Rep 4:255–261. doi: 10.1016/j.celrep.2013.06.029 CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Khoddami V, Cairns BR (2014) Transcriptome-wide target profiling of RNA cytosine methyltransferases using the mechanism-based enrichment procedure Aza-IP. Nat Protoc 9:337–361. doi: 10.1038/nprot.2014.014 CrossRefPubMedGoogle Scholar
  10. 10.
    Schaefer M, Pollex T, Hanna K, Lyko F (2009) RNA cytosine methylation analysis by bisulfite sequencing. Nucleic Acids Res 37:e12. doi: 10.1093/nar/gkn954 CrossRefPubMedGoogle Scholar
  11. 11.
    Lee J-H, Ang JK, Xiao X (2013) Analysis and design of RNA sequencing experiments for identifying RNA editing and other single-nucleotide variants. RNA 19:725–732. doi: 10.1261/rna.037903.112 CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Sims D, Sudbery I, Ilott NE et al (2014) Sequencing depth and coverage: key considerations in genomic analyses. Nat Rev Genet 15:121–132. doi: 10.1038/nrg3642 CrossRefPubMedGoogle Scholar
  13. 13.
    Storvall H, Ramsköld D, Sandberg R (2013) Efficient and comprehensive representation of uniqueness for next-generation sequencing by minimum unique length analyses. PLoS One 8:e53822. doi: 10.1371/journal.pone.0053822 CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Andrews S (2010) FastQC: a quality control tool for high throughput sequence data. http://www.bioinformatics.babraham.ac.uk/projects/fastqc
  15. 15.
    Bolger AM, Lohse M, Usadel B (2014) Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30:2114–2120. doi: 10.1093/bioinformatics/btu170 CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Dodt M, Roehr JT, Ahmed R, Dieterich C (2012) FLEXBAR-flexible barcode and adapter processing for next-generation sequencing platforms. Biology 1:895–905. doi: 10.3390/biology1030895 CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Gordon A (2009) FASTX-Toolkit FASTQ/A short-reads pre-processing tools. http://hannonlab.cshl.edu/fastx_toolkit/index.html.
  18. 18.
    Martin M (2011) Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17:10–12. doi: 10.14806/ej.17.1.200 CrossRefGoogle Scholar
  19. 19.
    Fabbro CD, Scalabrin S, Morgante M, Giorgi FM (2013) An extensive evaluation of read trimming effects on Illumina NGS data analysis. PLoS One 8:e85024. doi: 10.1371/journal.pone.0085024 CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    sestaton/Pairfq. In: GitHub. https://github.com/sestaton/Pairfq. Accessed 22 Feb 2016
  21. 21.
    Langmead B, Salzberg SL (2012) Fast gapped-read alignment with Bowtie 2. Nat Methods 9:357–359. doi: 10.1038/nmeth.1923 CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25:1754–1760. doi: 10.1093/bioinformatics/btp324 CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Bao S, Jiang R, Kwan W et al (2011) Evaluation of next-generation sequencing software in mapping and assembly. J Hum Genet 56:406–414. doi: 10.1038/jhg.2011.43 CrossRefPubMedGoogle Scholar
  24. 24.
    Dobin A, Davis CA, Schlesinger F et al (2013) STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29:15–21. doi: 10.1093/bioinformatics/bts635 CrossRefPubMedGoogle Scholar
  25. 25.
    Kim D, Pertea G, Trapnell C et al (2013) TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol 14:R36. doi: 10.1186/gb-2013-14-4-r36 CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Kim D, Langmead B, Salzberg SL (2015) HISAT: a fast spliced aligner with low memory requirements. Nat Methods 12:357–360. doi: 10.1038/nmeth.3317 CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Kunde-Ramamoorthy G, Coarfa C, Laritsky E et al (2014) Comparison and quantitative verification of mapping algorithms for whole-genome bisulfite sequencing. Nucleic Acids Res 42:e43. doi: 10.1093/nar/gkt1325 CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Krueger F, Andrews SR (2011) Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications. Bioinformatics 27:1571–1572. doi: 10.1093/bioinformatics/btr167 CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Lim J-Q, Tennakoon C, Li G et al (2012) BatMeth: improved mapper for bisulfite sequencing reads on DNA methylation. Genome Biol 13:R82. doi: 10.1186/gb-2012-13-10-r82 CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Xi Y, Li W (2009) BSMAP: whole genome bisulfite sequence MAPping program. BMC Bioinformatics 10:232. doi: 10.1186/1471-2105-10-232 CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Finotello F, Di Camillo B (2015) Measuring differential gene expression with RNA-seq: challenges and strategies for data analysis. Brief Funct Genomics 14:130–142. doi: 10.1093/bfgp/elu035 CrossRefPubMedGoogle Scholar
  32. 32.
    Rieder D, Amort T, Kugler E et al (2015) meRanTK: methylated RNA analysis ToolKit. Bioinformatics. doi: 10.1093/bioinformatics/btv647
  33. 33.
    Tuorto F, Liebers R, Musch T et al (2012) RNA cytosine methylation by Dnmt2 and NSun2 promotes tRNA stability and protein synthesis. Nat Struct Mol Biol 19:900–905. doi: 10.1038/nsmb.2357 CrossRefPubMedGoogle Scholar
  34. 34.
    Lister R, Pelizzola M, Dowen RH et al (2009) Human DNA methylomes at base resolution show widespread epigenomic differences. Nature 462:315–322. doi: 10.1038/nature08514 CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Barturen G, Rueda A, Oliver JL, Hackenberg M (2013) MethylExtract: high-quality methylation maps and SNV calling from whole genome bisulfite sequencing data. F1000Res 2:217. doi: 10.12688/f1000research.2-217.v2 PubMedGoogle Scholar
  36. 36.
    Koboldt DC, Zhang Q, Larson DE et al (2012) VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res 22:568–576. doi: 10.1101/gr.129684.111 CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Karolchik D, Kuhn RM, Baertsch R et al (2008) The UCSC Genome Browser Database: 2008 update. Nucleic Acids Res 36:D773–D779. doi: 10.1093/nar/gkm966 CrossRefPubMedGoogle Scholar
  38. 38.
    Thorvaldsdóttir H, Robinson JT, Mesirov JP (2013) Integrative genomics viewer (IGV): high-performance genomics data visualization and exploration. Brief Bioinform 14:178–192. doi: 10.1093/bib/bbs017 CrossRefPubMedGoogle Scholar
  39. 39.
    Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15:550. doi: 10.1186/s13059-014-0550-8 CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Meyer KD, Saletore Y, Zumbo P et al (2012) Comprehensive analysis of mRNA methylation reveals enrichment in 3′ UTRs and near stop codons. Cell 149:1635–1646. doi: 10.1016/j.cell.2012.05.003 CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  1. 1.Division of Bioinformatics, BiocenterMedical University of InnsbruckInnsbruckAustria

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