Skip to main content
SpringerLink
Log in

Computational Methods for Elucidating Gene Expression Regulation in Bacteria

  • Protocol
  • First Online:
Artificial Neural Networks

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2190))

Abstract

Research over the past two decades has uncovered an unexpected complexity and intricacy of gene expression regulation in bacteria. Bacteria have (1) numerous small noncoding RNAs (sRNAs) which are ubiquitous regulators of gene expression, (2) a flexible and diverse promoter structure, and (3) transcription termination as another means of gene expression regulation.

To understand bacteria gene expression regulation, one needs to identify promoters, terminators, and sRNAs together with their targets. Here we describe the state of the art in computational methods to perform promoter recognition, sRNA identification, and sRNA target prediction. Additionally, we provide step-by-step instructions to use current approaches to perform these tasks.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Hor J, Gorski SA, Vogel J (2018) Bacterial RNA biology on a genome scale. Mol Cell 70(5):785–799

    Article  PubMed  Google Scholar 

  2. Dutta T, Srivastava S (2018) Small RNA-mediated regulation in bacteria: a growing palette of diverse mechanisms. Gene 656:60–72

    Article  CAS  PubMed  Google Scholar 

  3. Barquist L, Vogel J (2015) Accelerating discovery and functional analysis of small RNAs with new technologies. Annu Rev Genet 49:367–394. https://doi.org/10.1146/annurev-genet-112414-054804

    Article  CAS  PubMed  Google Scholar 

  4. Hook-Barnard IG, Hinton DM (2007) Transcription initiation by mix and match elements: flexibility for polymerase binding to bacterial promoters. Gene Regul Syst Bio 1:275–293

    PubMed  PubMed Central  Google Scholar 

  5. Turnbough CL Jr (2019) Regulation of bacterial gene expression by transcription attenuation. Microbiol Mol Biol Rev 83(3). https://doi.org/10.1128/MMBR.00019-19

  6. Leonard S, Meyer S, Lacour S et al (2019) APERO: a genome-wide approach for identifying bacterial small RNAs from RNA-Seq data. Nucleic Acids Res 47(15):e88. https://doi.org/10.1093/nar/gkz485

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Yu SH, Vogel J, Forstner KU (2018) ANNOgesic: a Swiss army knife for the RNA-seq based annotation of bacterial/archaeal genomes. Gigascience 7(9):giy096. https://doi.org/10.1093/gigascience/giy096

    Article  CAS  PubMed Central  Google Scholar 

  8. Pena-Castillo L, Gruell M, Mulligan ME et al (2016) Detection of bacterial small transcripts from Rna-Seq data: a comparative assessment. Pac Symp Biocomput 21:456–467

    PubMed  Google Scholar 

  9. Eppenhof EJJ, Pena-Castillo L (2019) Prioritizing bona fide bacterial small RNAs with machine learning classifiers. PeerJ 7:e6304. https://doi.org/10.7717/peerj.6304

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Pain A, Ott A, Amine H et al (2015) An assessment of bacterial small RNA target prediction programs. RNA Biol 12(5):509–513. https://doi.org/10.1080/15476286.2015.1020269

    Article  PubMed  PubMed Central  Google Scholar 

  11. Wright PR, Richter AS, Papenfort K et al (2013) Comparative genomics boosts target prediction for bacterial small RNAs. Proc Natl Acad Sci U S A 110(37):E3487–E3496. https://doi.org/10.1073/pnas.1303248110

    Article  PubMed  PubMed Central  Google Scholar 

  12. Wagner EG, Romby P (2015) Small RNAs in bacteria and archaea: who they are, what they do, and how they do it. Adv Genet 90:133–208. https://doi.org/10.1016/bs.adgen.2015.05.001

    Article  CAS  PubMed  Google Scholar 

  13. King AM, Vanderpool CK, Degnan PH (2019) sRNA target prediction organizing tool (SPOT) integrates computational and experimental data to facilitate functional characterization of bacterial small RNAs. mSphere 4(1):e00561–e00518. https://doi.org/10.1128/mSphere.00561-18

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Sharma CM, Vogel J (2014) Differential RNA-seq: the approach behind and the biological insight gained. Curr Opin Microbiol 19:97–105. https://doi.org/10.1016/j.mib.2014.06.010

    Article  CAS  PubMed  Google Scholar 

  15. Ettwiller L, Buswell J, Yigit E et al (2016) A novel enrichment strategy reveals unprecedented number of novel transcription start sites at single base resolution in a model prokaryote and the gut microbiome. BMC Genomics 17:199. https://doi.org/10.1186/s12864-016-2539-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Conway T, Creecy JP, Maddox SM et al (2014) Unprecedented high-resolution view of bacterial operon architecture revealed by RNA sequencing. MBio 5(4):e01442–e01414. https://doi.org/10.1128/mBio.01442-14

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Thomason MK, Bischler T, Eisenbart SK et al (2015) Global transcriptional start site mapping using differential RNA sequencing reveals novel antisense RNAs in Escherichia coli. J Bacteriol 197(1):18–28. https://doi.org/10.1128/JB.02096-14

    Article  CAS  PubMed  Google Scholar 

  18. Guzina J, Djordjevic M (2016) Promoter recognition by extracytoplasmic function sigma factors: analyzing DNA and protein interaction motifs. J Bacteriol 198(14):1927–1938. https://doi.org/10.1128/JB.00244-16

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Zhu Y, Mao C, Ge X et al (2017) Characterization of a minimal type of promoter containing the −10 element and a guanine at the −14 or −13 position in mycobacteria. J Bacteriol 199(21):e00385–e00317. https://doi.org/10.1128/JB.00385-17

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Solovjev V, Salamov A (2011) Automatic annotation of microbial genomes and metagenomic sequences. In: Li RW (ed) Metagenomics and its applications in agriculture, biomedicine and environmental studies. Nova Science, Hauppauge, p 61

    Google Scholar 

  21. Song K (2012) Recognition of prokaryotic promoters based on a novel variable-window Z-curve method. Nucleic Acids Res 40(3):963–971. https://doi.org/10.1093/nar/gkr795

    Article  CAS  PubMed  Google Scholar 

  22. de Avila E Silva S, Echeverrigaray S, Gerhardt GJ (2011) BacPP: bacterial promoter prediction—a tool for accurate sigma-factor specific assignment in enterobacteria. J Theor Biol 287:92–99. https://doi.org/10.1016/j.jtbi.2011.07.017

    Article  CAS  PubMed  Google Scholar 

  23. Umarov RK, Solovyev VV (2017) Recognition of prokaryotic and eukaryotic promoters using convolutional deep learning neural networks. PLoS One 12(2):e0171410. https://doi.org/10.1371/journal.pone.0171410

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Shahmuradov IA, Mohamad Razali R, Bougouffa S et al (2017) bTSSfinder: a novel tool for the prediction of promoters in cyanobacteria and Escherichia coli. Bioinformatics 33(3):334–340. https://doi.org/10.1093/bioinformatics/btw629

    Article  CAS  PubMed  Google Scholar 

  25. Di Salvo M, Pinatel E, Tala A et al (2018) G4PromFinder: an algorithm for predicting transcription promoters in GC-rich bacterial genomes based on AT-rich elements and G-quadruplex motifs. BMC Bioinformatics 19(1):36. https://doi.org/10.1186/s12859-018-2049-x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. 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 

  27. Li H, Durbin R (2010) Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26(5):589–595. https://doi.org/10.1093/bioinformatics/btp698

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Anders S, Pyl PT, Huber W (2015) HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31(2):166–169. https://doi.org/10.1093/bioinformatics/btu638

    Article  CAS  PubMed  Google Scholar 

  29. 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 

  30. Di Tommaso P, Chatzou M, Floden EW et al (2017) Nextflow enables reproducible computational workflows. Nat Biotechnol 35(4):316–319. https://doi.org/10.1038/nbt.3820

    Article  CAS  PubMed  Google Scholar 

  31. Liaw A, Wiener M (2002) Classification and regression by randomForest. R News 2(3):18–22

    Google Scholar 

Download references

Acknowledgments

Funding provided by a Discovery Grant to L.P.C. from the Natural Sciences and Engineering Research Council (NSERC).

Author information

Authors and Affiliations

  1. Department of Computer Science, Memorial University of Newfoundland, St. John’s, NL, Canada

    Kratika Naskulwar, Ruben Chevez-Guardado & Lourdes Peña-Castillo

  2. Department of Biology, Memorial University of Newfoundland, St. John’s, NL, Canada

    Lourdes Peña-Castillo

Authors
  1. Kratika Naskulwar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ruben Chevez-Guardado
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lourdes Peña-Castillo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lourdes Peña-Castillo .

Editor information

Editors and Affiliations

  1. Chemistry, Oxford University, Oxford, UK

    Hugh Cartwright

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Naskulwar, K., Chevez-Guardado, R., Peña-Castillo, L. (2021). Computational Methods for Elucidating Gene Expression Regulation in Bacteria. In: Cartwright, H. (eds) Artificial Neural Networks. Methods in Molecular Biology, vol 2190. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0826-5_4

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-0826-5_4

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-0825-8

  • Online ISBN: 978-1-0716-0826-5

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation