Skip to main content
Log in

Nonlinear Analysis of tRNAs Nucleotide Sequences by Random Walks: Randomness and Order in the Primitive Informational Polymers

  • Letter to the Editor
  • Published:
Journal of Molecular Evolution Aims and scope Submit manuscript

Abstract

In order to test the hypothesis that the nucleotide sequences of the primitive informational polymers might not be chosen randomly and in the attempt to compare among taxa, we propose a comparison of computer-generated random sequences with tRNAs nucleotide sequences present in the bacterial and archaeal genomes, being tRNAs molecules possible “fossils” of the time (billions years ago) in which life arose. Our approach is based on the analysis of sequences of tRNAs described as random walks and the distances from the origin evaluated by the use of nonlinear indexes (largest Lyapunov exponent, entropy, BDS statistic). Six different tRNAs of Bacteria and Archaea (ten Archaea and ten Bacteria, thermophilic and mesophilic ones; n = 120), and computer-generated random sequences (n = 50) were studied. Our data show that tRNAs present indices statistical lower than the ones of computer-generated random data (tRNAs own a more ordered sequence than random ones: Lyapunov, p < 0.01; entropy, p < 0.05; BDS, p < 0.01). The observed deviation from pure randomness should be arisen from some constraints like the secondary structure of this biologic macromolecule and/or from a “frozen” stochastic transition, or even from the possible peculiar origin of tRNA by replication of older proto-RNA. Comparing between taxa, in the species studied, Bacteria present BDS and Base ratio (G+C)/(A+T) indexes statistically lower than in Archaea, together which a 20 % of entropy increase. The analysis of a greater number of tRNAs and species will permit to explain if this finding, showing a higher randomness in the bacterial tRNAs sequences, is linked to the different base ratio, to the different environments in which the microorganisms live or to an evolutionary effect.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1

References

The “Genomic tRNA Database” (Chan and Lowe 2009), http://gtrnadb.ucsc.edu/; SPLITSdb (Sugahara et al 2008), http://splits.iab.keio.ac.jp/splitsdb/

  • Adami C, Ofria C, Collier TC (2000) Evolution of biological complexity. PNAS 97(9):4463–4468

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Anastassiou D (2001) Genomic signal processing. IEEE Signal Proc 18(4):8–20

    Article  Google Scholar 

  • Arneodo A, Bacry E, Graves PV et al (1995) Characterizing long-range correlations in DNA sequences from wavelet analysis. Phys Rev Lett 74:3293–3296

    Article  CAS  PubMed  Google Scholar 

  • Berger JA, Mitra SK, Carli M et al (2002) New approaches to genome sequence analysis based on digital signal processing. IEEE Workshop on GENSIPS:1–4

  • Berger JA, Mitra SK, Carli M et al (2004) Visualization and analysis of DNA sequences using DNA walks. J Frankl Inst 341:37–53

    Article  Google Scholar 

  • Brock WA (1986) Distinguishing random and deterministic systems: abridged version. J Econ Theory 40:168–195

    Article  Google Scholar 

  • Ciccarelli FD, Doerks T, von Mering C et al (2006) Toward automatic reconstruction of a highly resolved tree of life. Science 311:1283–1287

    Article  CAS  PubMed  Google Scholar 

  • Claverie J-M (1997) Computational methods for the identification of genes in vertebrate genomic sequences. Hum Mol Genet 6:1735–1744

    Article  CAS  PubMed  Google Scholar 

  • Eigen M, Lindemann BF, Tietze M et al (1989) How old is the genetic code? Statistical geometry of tRNA provides an answer. Science 244:673–679

    Article  CAS  PubMed  Google Scholar 

  • Fasold M, Langenberger D, Binder H et al (2011) DARIO: a ncRNA detection and analysis tool for next-generation sequencing experiments. Nucleic Acids Res 39:W112–W117

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Feller W (1968) An introduction to probability theory and its applications, 3rd edn., Wiley series in probability and mathematical statisticsWiley, Wiley

    Google Scholar 

  • Fujishima K, Kanai A (2014) tRNA gene diversity in the three domains of life. Frontiers Genet 5(142):1–11. doi:10.3389/fgene.2014.00142

    Google Scholar 

  • Gayle KP, Freeland SJ (2011) Did evolution select a nonrandom “alphabet” of amino acids? Astrobiology 11:235–240

    Article  Google Scholar 

  • Grassberger P, Procaccia I (1983) Estimation of the Kolmogorov entropy from a chaotic signal. Phys Rev A 28:2591–2593

    Article  Google Scholar 

  • Haimovich AD, Byrne B, Ramaswamy R, Welsh WJ (2006) Wavelet analysis of DNA walks. J Comp Biol 13(7):1289–1298

    Article  CAS  Google Scholar 

  • Hamori E, Ruskin J (1983) H-curves, a novel method of representation of nucleotide series especially suited for long DNA sequences. J Biol Chem 258:1318–1327

    CAS  PubMed  Google Scholar 

  • Higgs PG, Wu M (2012) The importance of stochastic transitions for the origin of life. Orig Life Evol Biosph 42:453–457. doi:10.1007/s11084-012-9307-0

    Article  CAS  PubMed  Google Scholar 

  • Howland JL (2000) The surprising archaea. Oxford University Press, London

    Google Scholar 

  • Koonin EV, Yutin N (2014) The dispersed archaeal eukaryome and the complex archaeal ancestor of eukaryotes. Cold Spring Harb Perspect Biol 1–16. doi: 10.1101/cshperspect.a016188

  • Mizrahi E, Ninio J (1985) Graphical coding of nucleic acid sequences. Biochimie 67:445–448

    Article  Google Scholar 

  • Press WH, Teukolsky SA (1992) Portable random number generators. Comput Phys 6:522–524

    Article  Google Scholar 

  • Rodin AS, Szathmáry E, Rodin SN (2011) On origin of genetic code and tRNA before translation. Biol Direct 6:14

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Sprott JC, Rowlands G (1995) Chaos data analyzer. Physics Academic Software, New York

    Google Scholar 

  • Videm P, Rose D, Costa F, Backofen R (2014) BlockClust: efficient clustering and classification of non-coding RNAs from short read RNA-seq profiles. Bioinformatics 30(21):274–282

    Article  Google Scholar 

  • Weiss O, Jiménez-Montaño MA, Herzelm H (2000) Information content of protein sequences. J Theor Biol 206:379–386

    Article  CAS  PubMed  Google Scholar 

  • Wolf A, Swift JB, Swinney HL et al (1985) Determining Lyapunov exponents from a time series. Phys D 16:285–317

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to G. Bianciardi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bianciardi, G., Borruso, L. Nonlinear Analysis of tRNAs Nucleotide Sequences by Random Walks: Randomness and Order in the Primitive Informational Polymers. J Mol Evol 80, 81–85 (2015). https://doi.org/10.1007/s00239-015-9664-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00239-015-9664-1

Keywords

Navigation