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Using RAMPAGE to Identify and Annotate Promoters in Insect Genomes

  • R. Taylor Raborn
  • Volker P. Brendel
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1858)

Abstract

Application of Transcription Start Site (TSS) profiling technologies, coupled with large-scale next-generation sequencing (NGS) has yielded valuable insights into the location, structure, and activity of promoters across diverse metazoan model systems. In insects, TSS profiling has been used to characterize the promoter architecture of Drosophila melanogaster (Hoskins et al., Genome Res 21(2):182–192, 2011) and subsequently was employed to reveal widespread transposon-driven alternative promoter usage in the fruit fly (Batut et al., Genome Res 23:169–180, 2012).

In this chapter we discuss the computational analysis of the experimental data derived from one of TSS profiling methods, RAMPAGE (RNA Annotation and Mapping of Promoters for Analysis of Gene Expression) that can be used for the precise, quantitative identification of promoters in insect genomes. We demonstrate this using the software tools GoRAMPAGE (Brendel and Raborn, GoRAMPAGE—A workflow for promoter detection by 5-read mapping. https://github.com/BrendelGroup/GoRAMPAGE, 2016) and TSRchitect (Raborn and Brendel, TSRchitect: promoter identification from large-scale TSS profiling data. R Bioconductor package version 1.8.0 [Online]. Available: http://bioconductor.org/packages/release/bioc/html/TSRchitect.html, 2017), providing detailed instructions with the aim of taking the user from raw reads to processed results.

Key words

cis-regulatory regions Promoter architecture Transcription initiation Transcription start sites (TSSs) 

Notes

Acknowledgements

The authors would like to thank Philippe Batut for generous technical assistance with the RAMPAGE protocol, and to Nathan Keith for his help establishing the protocol in our laboratory. The authors are grateful to Thomas W. McCarthy for his help testing the code and providing editorial feedback.

Disclosure Declaration The authors declare that they have no competing interests.

References

  1. 1.
    Kadonaga JT (2012) Perspectives on the RNA polymerase II core promoter. Wiley interdisciplinary reviews: developmental biology, vol 1(1). Wiley, New York, pp 40–51CrossRefGoogle Scholar
  2. 2.
    Kodzius R, Kojima M, Nishiyori H, Nakamura M, Fukuda S, Tagami M et al (2006) CAGE: cap analysis of gene expression. Nat Methods 3(3):211–222CrossRefGoogle Scholar
  3. 3.
    Carninci P, Kasukawa T, Katayama S, Gough J, Frith MC, Maeda N et al (2005) The transcriptional landscape of the mammalian genome. Science (New York, NY) 309(5740):1559–1563Google Scholar
  4. 4.
    Hoskins RA, Hoskins RA, Landolin JM, Landolin JM, Brown JB, Brown JB et al (2011) Genome-wide analysis of promoter architecture in Drosophila melanogaster. Genome Res 21(2):182–192CrossRefGoogle Scholar
  5. 5.
    Rach EA, Yuan H-Y, Majoros WH, Tomancak P, Ohler U (2009) Motif composition, conservation and condition-specificity of single and alternative transcription start sites in the Drosophila genome. Genome Biol 10(7):R73CrossRefGoogle Scholar
  6. 6.
    Lenhard B, Sandelin A, Carninci P (2012) Metazoan promoters: emerging characteristics and insights into transcriptional regulation. Nat Rev Genet 13(4):233–245CrossRefGoogle Scholar
  7. 7.
    Ni T, Corcoran DL, Rach EA, Song S, Spana EP, Gao Y et al (2010) A paired-end sequencing strategy to map the complex landscape of transcription initiation. Nat Methods 7(7):521–527CrossRefGoogle Scholar
  8. 8.
    Ohler U, Liao G-c, Niemann H, Rubin GM (2002) Computational analysis of core promoters in the Drosophila genome. Genome Biol 3(12):research0087.1–0087.12CrossRefGoogle Scholar
  9. 9.
    Raborn RT, Spitze K, Brendel VP, Lynch M (2016) Promoter architecture and sex-specific gene expression in Daphnia pulex. Genetics 204(2):593–612CrossRefGoogle Scholar
  10. 10.
    Nepal C, Hadzhiev Y, Previti C, Haberle V, Li N, Takahashi H et al (2013) Dynamic regulation of the transcription initiation landscape at single nucleotide resolution during vertebrate embryogenesis. Genome Res 23(11):1938–1950CrossRefGoogle Scholar
  11. 11.
    Carninci P, Sandelin A, Lenhard B, Katayama S, Shimokawa K, Ponjavic J et al (2006) Genome-wide analysis of mammalian promoter architecture and evolution. Nat Gen 38(6):626–635Google Scholar
  12. 12.
    Mwangi S, Attardo G, Suzuki Y, Aksoy S, Christoffels A (2015) TSS seq based core promoter architecture in blood feeding Tsetse fly (Glossina morsitans morsitans) vector of Trypanosomiasis. BMC Genomics 16(1):722Google Scholar
  13. 13.
    Tsuchihara K, Suzuki Y, Wakaguri H, Irie T, Tanimoto K, Hashimoto S-i et al (2009) Massive transcriptional start site analysis of human genes in hypoxia cells. Nucleic Acids Res 37(7):2249–2263CrossRefGoogle Scholar
  14. 14.
    Cvetesic N, Lenhard B (2017) Core promoters across the genome. Nat Biotechnol 35(2):123–124CrossRefGoogle Scholar
  15. 15.
    Batut PJ, Dobin A, Plessy C, Carninci P, Gingeras TR (2012) High-fidelity promoter profiling reveals widespread alternative promoter usage and transposon-driven developmental gene expression. Genome Res 23:169–180CrossRefGoogle Scholar
  16. 16.
    Batut PJ, Gingeras TR (2013) RAMPAGE: promoter activity profiling by paired-end sequencing of 5’-complete cDNAs. In: Ausubel FM et al (eds) Current protocols in molecular biology. Wiley, Hoboken, pp 25B.11.1–25B.11.16Google Scholar
  17. 17.
    Plessy C, Bertin N, Takahashi H, Simone R, Salimullah M, Lassmann T et al (2010) Linking promoters to functional transcripts in small samples with nanoCAGE and CAGEscan. Nat Methods 7(7):528–534CrossRefGoogle Scholar
  18. 18.
    Cumbie JS, Ivanchenko MG, Megraw M (2015) NanoCAGE-XL and CapFilter: an approach to genome wide identification of high confidence transcription start sites. BMC Genomics 16(1):528Google Scholar
  19. 19.
    Morton T, Petricka J, Corcoran DL, Li S, Winter CM, Carda A et al (2014) Paired-end analysis of transcription start sites in Arabidopsis reveals plant-specific promoter signatures. Plant cell 26(7):2746–2760 (2014)CrossRefGoogle Scholar
  20. 20.
    ENCODE Project Consortium (2012) An integrated encyclopedia of DNA elements in the human genome. Nature 489(7414):57–74Google Scholar
  21. 21.
    Consortium E (2017) Rampage and cage data standards and processing pipeline [Online]. Available: https://www.encodeproject.org/rampage/
  22. 22.
    Merchant N, Lyons E, Goff S, Vaughn M, Ware D, Micklos D et al (2016) The iPlant collaborative: cyberinfrastructure for enabling data to discovery for the life sciences. PLoS Biol 14(1):e1002342CrossRefGoogle Scholar
  23. 23.
    Stewart CA, Cockerill TM, Foster I, Hancock D, Merchant N, Skidmore E et al (2015) Jetstream: a self-provisioned, scalable science and engineering cloud environment. In: Proceedings of the 2015 XSEDE conference: scientific advancements enabled by enhanced cyberinfrastructure. XSEDE ’15. ACM, New York, pp 29:1–29:8 [Online]. Available:  https://doi.org/10.1145/2792745.2792774
  24. 24.
    Leinonen R, Sugawara H, Shumway M, International Nucleotide Sequence Database Collaboration (2011) The sequence read archive. Nucleic Acids Res 39(Database issue):D19–D21Google Scholar
  25. 25.
    Brendel VP, Raborn RT (2016) GoRAMPAGE- a workflow for promoter detection by 5’-Read mapping. https://github.com/BrendelGroup/GoRAMPAGE
  26. 26.
    Aronesty E (2013) Comparison of sequencing utility programs. Open Bioinform J 7(1):1–8CrossRefGoogle Scholar
  27. 27.
    Lab H, FASTX Toolkit [Online]. Available: http://hannonlab.cshl.edu/fastx_toolkit/
  28. 28.
    Lassmann T (2015) TagDust2: a generic method to extract reads from sequencing data. BMC Bioinform 16(1):1Google Scholar
  29. 29.
    Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N et al (2009) The sequence alignment/map format and SAMtools. Bioinformatics (Oxford, England) 25(16):2078–2079CrossRefGoogle Scholar
  30. 30.
    Dobin A, Gingeras TR (2016) Optimizing RNA-Seq mapping with STAR. In: Transcription factor regulatory networks. Springer, New York, pp 245–262Google Scholar
  31. 31.
    R Core Team (2017) R: a language and environment for statistical computing. R foundation for statistical computing, Vienna [Online]. Available: https://www.R-project.org
  32. 32.
    Lawrence M, Morgan M (2014) Scalable genomics with R and Bioconductor. Stat Sci 29(2):214–226CrossRefGoogle Scholar
  33. 33.
    Raborn RT, Brendel V (2017) TSRchitect: promoter identification from large-scale TSS profiling data. r Bioconductor package version 1.0.0 [Online]. Available: http://bioconductor.org/packages/release/bioc/html/TSRchitect.html
  34. 34.
    Yates A, Akanni W, Amode MR, Barrell D, Billis K, Carvalho-Silva D et al (2016) Ensembl (2016). Nucleic Acids Res 44:D1, D710–D716 [Online]. Available:  https://doi.org/10.1093/nar/gkv1157CrossRefGoogle Scholar
  35. 35.
    Haberle V, Forrest ARR, Hayashizaki Y, Carninci P, Lenhard B (2015) CAGEr: precise TSS data retrieval and high-resolution promoterome mining for integrative analyses. Nucleic Acids Res 43(8):gkv054–e51Google Scholar
  36. 36.
    Pagès H (2016) BSgenome: infrastructure for biostrings-based genome data packages and support for efficient SNP representation. R package version 1.42.0Google Scholar
  37. 37.
    Thorvaldsdottir H, Robinson JT, Mesirov JP (2013) Integrative genomics viewer (IGV): high-performance genomics data visualization and exploration. Brief Bioinform 14(2):178–192CrossRefGoogle Scholar
  38. 38.
    Sayers EWE, Barrett TT, Benson DAD, Bolton EE, Bryant SHS, Canese KK et al (2012) Database resources of the national center for biotechnology information. Nucleic Acids Res 40(Database issue):D13–D25CrossRefGoogle Scholar
  39. 39.
    Tange O (2018) GNU parallel 2018, p 112. ISBN 978-1-387-50988-1Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of BiologyIndiana UniversityBloomingtonUSA
  2. 2.School of Informatics and ComputingIndiana UniversityBloomingtonUSA

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