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

BMC Bioinformatics

Research Article

Sven Rahmann¹  Alexander K Hartmann²  Stefan Wolfsheimer³  Inke Herms⁴ 

[1] Department of Computer Science, TU Dortmund, Germany;Institut für Physik, Universität Oldenburg, D-26111, Oldenburg, Germany;Laboratoire MAP5 (UMR CNRS 8145), Université Paris Descartes, 75006, Paris, France;Technische Fakultät, Universität Bielefeld, 33501, Bielefeld, Germany;

关键词: Hide Markov Model;    Null Model;    Importance Sampling;    Round Trip;    Viterbi Algorithm;   

DOI  :  10.1186/1471-2105-12-47

 received in 2010-02-26, accepted in 2011-02-03,  发布年份 2011

来源: Springer

【 摘 要 】

BackgroundMolecular 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.ResultsIn 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.ConclusionsThe 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.

【 授权许可】

CC BY   
© Wolfsheimer et al; licensee BioMed Central Ltd. 2011