Non-parametric and Semi-parametric Support Estimation Using SEquential RESampling Random Walks on Biomolecular Sequences

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11183)


Non-parametric and semi-parametric resampling procedures are widely used to perform support estimation in computational biology and bioinformatics. Among the most widely used methods in this class is the standard bootstrap method, which consists of random sampling with replacement. While not requiring assumptions about any particular parametric model for resampling purposes, the bootstrap and related techniques assume that sites are independent and identically distributed (i.i.d.). The i.i.d. assumption can be an over-simplification for many problems in computational biology and bioinformatics. In particular, sequential dependence within biomolecular sequences is often an essential biological feature due to biochemical function, evolutionary processes such as recombination, and other factors.

To relax the simplifying i.i.d. assumption, we propose a new non-parametric/semi-parametric sequential resampling technique that generalizes “Heads-or-Tails” mirrored inputs, a simple but clever technique due to Landan and Graur. The generalized procedure takes the form of random walks along either aligned or unaligned biomolecular sequences. We refer to our new method as the SERES (or “SEquential RESampling”) method.

To demonstrate the performance of the new technique, we apply SERES to estimate support for the multiple sequence alignment problem. Using simulated and empirical data, we show that SERES-based support estimation yields comparable or typically better performance compared to state-of-the-art methods.



This work has been supported in part by the National Science Foundation (grant nos. CCF-1565719, CCF-1714417, and DEB-1737898 to KJL) and MSU faculty startup funds (to KJL). Computational experiments were performed using the High Performance Computing Center (HPCC) at MSU.

Supplementary material

473852_1_En_17_MOESM1_ESM.pdf (210 kb)
Supplementary material 1 (pdf 210 KB)


  1. 1.
    Benjamini, Y., Hochberg, Y.: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Royal Stat. Soc. Ser. B (Methodol) 57(1), 289–300 (1995)MathSciNetzbMATHGoogle Scholar
  2. 2.
    Cannone, J.J., et al.: The Comparative RNA Web (CRW) site: an online database of comparative sequence and structure information for Ribosomal, Intron and Other RNAs. BMC Bioinform. 3(15) (2002).
  3. 3.
    Daskalakis, C., Roch, S.: Alignment-free phylogenetic reconstruction. In: Berger, B. (ed.) RECOMB 2010. LNCS, vol. 6044, pp. 123–137. Springer, Heidelberg (2010). Scholar
  4. 4.
    DeLong, E.R., DeLong, D.M., Clarke-Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44(3), 837–845 (1988)CrossRefGoogle Scholar
  5. 5.
    Efron, B.: Bootstrap methods: another look at the jackknife. Ann. Stat. 7(1), 1–26 (1979)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Felsenstein, J.: Confidence limits on phylogenies: an approach using the bootstrap. Evolution 39(4), 783–791 (1985)CrossRefGoogle Scholar
  7. 7.
    Fletcher, W., Yang, Z.: INDELible: a flexible simulator of biological sequence evolution. Mol. Biol. Evol. 26(8), 1879–1888 (2009)CrossRefGoogle Scholar
  8. 8.
    Katoh, K., Standley, D.M., Kazutaka Katoh and Daron: MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30(4), 772–780 (2013)CrossRefGoogle Scholar
  9. 9.
    Kim, J., Ma, J.: PSAR: measuring multiple sequence alignment reliability by probabilistic sampling. Nucleic Acids Res. 39(15), 6359–6368 (2011)CrossRefGoogle Scholar
  10. 10.
    Landan, G., Graur, D.: Heads or tails: a simple reliability check for multiple sequence alignments. Mol. Biol. Evol. 24(6), 1380–1383 (2007)CrossRefGoogle Scholar
  11. 11.
    Landan, G., Graur, D.: Local reliability measures from sets of co-optimal multiple sequence alignments. In: Biocomputing, pp. 15–24. World Scientific (2008)Google Scholar
  12. 12.
    Liu, K., et al.: SATé-II: very fast and accurate simultaneous estimation of multiple sequence alignments and phylogenetic trees. Syst. Biol. 61(1), 90–106 (2012)Google Scholar
  13. 13.
    Notredame, C., Higgins, D.G., Heringa, J.: T-Coffee: a novel method for fast and accurate multiple sequence alignment. J. Mol. Biol. 302, 205–217 (2000)CrossRefGoogle Scholar
  14. 14.
    Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)Google Scholar
  15. 15.
    Penn, O., Privman, E., Landan, G., Graur, D., Pupko, T.: An alignment confidence score capturing robustness to guide tree uncertainty. Mol. Biol. Evol. 27(8), 1759–1767 (2010)CrossRefGoogle Scholar
  16. 16.
    Rodriguez, F., Oliver, J.L., Marin, A., Medina, J.R.: The general stochastic model of nucleotide substitution. J. Theor. Biol. 142, 485–501 (1990)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Sela, I., Ashkenazy, H., Katoh, K., Pupko, T.: GUIDANCE2: accurate detection of unreliable alignment regions accounting for the uncertainty of multiple parameters. Nucleic Acids Res. 43(W1), W7–W14 (2015)CrossRefGoogle Scholar
  18. 18.
    Yang, Z., Rannala, B.: Bayesian phylogenetic inference using DNA sequences: a Markov chain Monte Carlo method. Mol. Biol. Evol. 14(7), 717–724 (1997)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringMichigan State UniversityEast LansingUSA
  2. 2.Simons Center for Quantitative BiologyCold Spring Harbor LaboratoryCold Spring HarborUSA

Personalised recommendations