Accurate statistics for local sequence alignment with position-dependent scoring by rare-event sampling

Wolfsheimer S, Herms I, Rahmann S, Hartmann AK (2011)
BMC Bioinformatics 12(1): 47-2105.

Journal Article | Published | English

No fulltext has been uploaded

Author
; ; ;
Abstract
Background: Molecular database search tools need statistical models to assess the significance for the resulting hits. In the classical approach one asks the question how probable a certain score is observed by pure chance. Asymptotic theories for such questions are available for two random i.i.d. sequences. Some effort had been made to include effects of finite sequence lengths and to account for specific compositions of the sequences. In many applications, such as a large-scale database homology search for transmembrane proteins, these models are not the most appropriate ones. Search sensitivity and specificity benefit from position-dependent scoring schemes or use of Hidden Markov Models. Additional, one may wish to go beyond the assumption that the sequences are i.i.d. Despite their practical importance, the statistical properties of these settings have not been well investigated yet. Results: In this paper, we discuss an efficient and general method to compute the score distribution to any desired accuracy. The general approach may be applied to different sequence models and and various similarity measures that satisfy a few weak assumptions. We have access to the low-probability region ("tail") of the distribution where scores are larger than expected by pure chance and therefore relevant for practical applications. Our method uses recent ideas from rare-event simulations, combining Markov chain Monte Carlo simulations with importance sampling and generalized ensembles. We present results for the score statistics of fixed and random queries against random sequences. In a second step, we extend the approach to a model of transmembrane proteins, which can hardly be described as i.i.d. sequences. For this case, we compare the statistical properties of a fixed query model as well as a hidden Markov sequence model in connection with a position based scoring scheme against the classical approach. Conclusions: The results illustrate that the sensitivity and specificity strongly depend on the underlying scoring and sequence model. A specific ROC analysis for the case of transmembrane proteins supports our observation.
Publishing Year
ISSN
PUB-ID

Cite this

Wolfsheimer S, Herms I, Rahmann S, Hartmann AK. Accurate statistics for local sequence alignment with position-dependent scoring by rare-event sampling. BMC Bioinformatics. 2011;12(1):47-2105.
Wolfsheimer, S., Herms, I., Rahmann, S., & Hartmann, A. K. (2011). Accurate statistics for local sequence alignment with position-dependent scoring by rare-event sampling. BMC Bioinformatics, 12(1), 47-2105.
Wolfsheimer, S., Herms, I., Rahmann, S., and Hartmann, A. K. (2011). Accurate statistics for local sequence alignment with position-dependent scoring by rare-event sampling. BMC Bioinformatics 12, 47-2105.
Wolfsheimer, S., et al., 2011. Accurate statistics for local sequence alignment with position-dependent scoring by rare-event sampling. BMC Bioinformatics, 12(1), p 47-2105.
S. Wolfsheimer, et al., “Accurate statistics for local sequence alignment with position-dependent scoring by rare-event sampling”, BMC Bioinformatics, vol. 12, 2011, pp. 47-2105.
Wolfsheimer, S., Herms, I., Rahmann, S., Hartmann, A.K.: Accurate statistics for local sequence alignment with position-dependent scoring by rare-event sampling. BMC Bioinformatics. 12, 47-2105 (2011).
Wolfsheimer, Stefan, Herms, Inke, Rahmann, Sven, and Hartmann, Alexander K. “Accurate statistics for local sequence alignment with position-dependent scoring by rare-event sampling”. BMC Bioinformatics 12.1 (2011): 47-2105.
This data publication is cited in the following publications:
This publication cites the following data publications:

3 Citations in Europe PMC

Data provided by Europe PubMed Central.

Discovery of prognostic biomarkers for predicting lung cancer metastasis using microarray and survival data.
Huang HL, Wu YC, Su LJ, Huang YJ, Charoenkwan P, Chen WL, Lee HC, Chu WC, Ho SY., BMC Bioinformatics 16(), 2015
PMID: 25881029
SCMHBP: prediction and analysis of heme binding proteins using propensity scores of dipeptides.
Liou YF, Charoenkwan P, Srinivasulu Y, Vasylenko T, Lai SC, Lee HC, Chen YH, Huang HL, Ho SY., BMC Bioinformatics 15 Suppl 16(), 2014
PMID: 25522279

46 References

Data provided by Europe PubMed Central.

Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes.
Krogh A, Larsson B, von Heijne G, Sonnhammer EL., J. Mol. Biol. 305(3), 2001
PMID: 11152613
Monte Carlo Sampling Methods Using Markov Chains and Their Applications
AUTHOR UNKNOWN, 1970

AUTHOR UNKNOWN, 1999

AUTHOR UNKNOWN, 2008
New Monte Carlo algorithm: Entropic sampling.
Lee J., Phys. Rev. Lett. 71(2), 1993
PMID: 10054892
Transition Matrix Monte Carlo Reweighting and Dynamics
AUTHOR UNKNOWN, 1999
Transition matrix Monte Carlo method
AUTHOR UNKNOWN, 1999
Monte Carlo algorithms based on the number of potential moves
AUTHOR UNKNOWN, 2000
Determining the density of states for classical statistical models: a random walk algorithm to produce a flat histogram.
Wang F, Landau DP., Phys Rev E Stat Nonlin Soft Matter Phys 64(5 Pt 2), 2001
PMID: 11736008
Error estimates on averages of correlated data
AUTHOR UNKNOWN, 1989
On orthogonal and symplectic matrix ensembles
AUTHOR UNKNOWN, 1996
Exact asymptotic results for the Bernoulli matching model of sequence alignment.
Majumdar SN, Nechaev S., Phys Rev E Stat Nonlin Soft Matter Phys 72(2 Pt 1), 2005
PMID: 16196539
Exact solution of the Bernoulli matching model of sequence alignment
AUTHOR UNKNOWN, 2008
The ASTRAL Compendium in 2004.
Chandonia JM, Hon G, Walker NS, Lo Conte L, Koehl P, Levitt M, Brenner SE., Nucleic Acids Res. 32(Database issue), 2004
PMID: 14681391

AUTHOR UNKNOWN, 1976
Performance limitations of flat-histogram methods.
Dayal P, Trebst S, Wessel S, Wurtz D, Troyer M, Sabhapandit S, Coppersmith SN., Phys. Rev. Lett. 92(9), 2004
PMID: 15089505
Optimizing the ensemble for equilibration in broad-histogram Monte Carlo simulations.
Trebst S, Huse DA, Troyer M., Phys Rev E Stat Nonlin Soft Matter Phys 70(4 Pt 2), 2004
PMID: 15600559
Significance of gapped sequence alignments.
Newberg LA., J. Comput. Biol. 15(9), 2008
PMID: 18973434

Export

0 Marked Publications

Open Data PUB

Web of Science

View record in Web of Science®

Sources

PMID: 21291566
PubMed | Europe PMC

Search this title in

Google Scholar