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Computational Prediction of sRNA in Acinetobacter baumannii

  • Sankalp Arya
  • Vineet Dubey
  • Deepak Sen
  • Atin Sharma
  • Ranjana PathaniaEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1946)

Abstract

Small RNAs in bacteria are noncoding RNAs that act as posttranscriptional regulators of gene expression. Over time, they have gained importance as fine-tuners of expression of genes involved in critical biological processes like metabolism, fitness, virulence, and antibiotic resistance. The availability of various high-throughput strategies enable the detection of these molecules but are technically challenging and time-intensive. Thus, to fulfil the need of a simple computational algorithm pipeline to predict these sRNAs in bacterial species, we detail a user-friendly ensemble method with specific application in Acinetobacter spp. The developed algorithms primarily look for intergenic regions in the genome of related Acinetobacter spp., thermodynamic stability, and conservation of RNA secondary structures to generate a model input for the sRNAPredict3 tool which utilizes all this information to generate a list of putative sRNA. We confirmed the accuracy of the method by comparing its output with the RNA-seq data and found the method to be faster and more accurate for Acinetobacter baumannii ATCC 17978. Thus, this method improves the identification of sRNA in Acinetobacter and other bacterial species.

Key words

Computational algorithms Noncoding RNA Small RNA sRNAPredict3 

References

  1. 1.
    Gomes AQ, Nolasco S, Soares H (2013) Non-coding RNAs: multi-tasking molecules in the cell. Int J Mol Sci 14(8):16010–16039.  https://doi.org/10.3390/ijms140816010CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Lee YS, Shibata Y, Malhotra A, Dutta A (2009) A novel class of small RNAs: tRNA-derived RNA fragments (tRFs). Genes Dev 23(22):2639–2649.  https://doi.org/10.1101/gad.1837609CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Wei H, Zhou B, Zhang F, Tu Y, Hu Y, Zhang B, Zhai Q (2013) Profiling and identification of small rDNA-derived RNAs and their potential biological functions. PLoS One 8(2):e56842CrossRefGoogle Scholar
  4. 4.
    Barman RK, Mukhopadhyay A, Das S (2017) An improved method for identification of small non-coding RNAs in bacteria using support vector machine. Sci Rep 7:46070.  https://doi.org/10.1038/srep46070CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Storz G, Vogel J, Wassarman KM (2011) Regulation by small RNAs in bacteria: expanding frontiers. Mol Cell 43(6):880–891.  https://doi.org/10.1016/j.molcel.2011.08.022CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Mercer TR, Dinger ME, Mattick JS (2009) Long noncoding RNAs: insights into functions. Nat Rev Genet 10:155–159CrossRefGoogle Scholar
  7. 7.
    Gómez-Lozano M, Marvig RL, Molin S, Long KS (2014) Identification of bacterial small RNAs by RNA sequencing. In: Filloux A, Ramos JL (eds) Pseudomonas methods and protocols. Methods in molecular biology (methods and protocols), vol 1149. Humana Press, New York, NYGoogle Scholar
  8. 8.
    Sharma R, Arya S, Patil SD, Sharma A, Jain PK, Navani NK, Pathania R (2014) Identification of novel regulatory small RNAs in Acinetobacter baumannii. PLoS One 9(4):e93833CrossRefGoogle Scholar
  9. 9.
    Sharma A, Sharma R, Bhattacharyya T, Bhando T, Pathania R (2016) Fosfomycin resistance in Acinetobacter baumannii is mediated by efflux through a major facilitator superfamily (MFS) transporter—AbaF. J Antimicrob Chemother 72(1):68–74CrossRefGoogle Scholar
  10. 10.
    Livny J, Brencic A, Lory S, Waldor MK (2006) Identification of 17 Pseudomonas aeruginosa sRNAs and prediction of sRNA-encoding genes in 10 diverse pathogens using the bioinformatic tool sRNAPredict2. Nucleic Acids Res 34(12):3484–3493.  https://doi.org/10.1093/nar/gkl453CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Macke TJ, Ecker DJ, Gutell RR, Gautheret D, Case DA, Sampath R (2001) RNAMotif, an RNA secondary structure definition and search algorithm. Nucleic Acids Res 29(22):4724–4735CrossRefGoogle Scholar
  12. 12.
    Kingsford CL, Ayanbule K, Salzberg SL (2007) Rapid, accurate, computational discovery of Rho-independent transcription terminators illuminates their relationship to DNA uptake. Genome Biol 8(2):R22.  https://doi.org/10.1186/gb-2007-8-2-r22CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Rivas E, Eddy SR (2001) Noncoding RNA gene detection using comparative sequence analysis. BMC Bioinformatics 2:8.  https://doi.org/10.1186/1471-2105-2-8CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Livny J, Fogel MA, Davis BM, Waldor MK (2005) sRNAPredict: an integrative computational approach to identify sRNAs in bacterial genomes. Nucleic Acids Res 33(13):4096–4105.  https://doi.org/10.1093/nar/gki715CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Rosenkranz D, Zischler H (2012) proTRAC—a software for probabilistic piRNA cluster detection, visualization and analysis. BMC Bioinformatics 13:5.  https://doi.org/10.1186/1471-2105-13-5CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Livny J (2012) Bioinformatic discovery of bacterial regulatory RNAs using SIPHT. In: Keiler K (ed) Bacterial regulatory RNA. Methods in molecular biology (methods and protocols), vol 905. Humana Press, Totowa, NJGoogle Scholar

Copyright information

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

Authors and Affiliations

  • Sankalp Arya
    • 1
    • 2
  • Vineet Dubey
    • 1
  • Deepak Sen
    • 1
  • Atin Sharma
    • 1
  • Ranjana Pathania
    • 1
    Email author
  1. 1.Department of BiotechnologyIndian Institute of Technology-RoorkeeRoorkeeIndia
  2. 2.Division of Agricultural and Environmental SciencesUniversity of NottinghamNottinghamUK

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