Abstract
A central question in pairwise sequence comparison is assessing the statistical significance of the alignment. The alignment score distribution is known to follow an extreme value distribution with analytically calculable parameters K and λ for ungapped alignments with one substitution matrix. But no statistical theory is currently available for the gapped case and for alignments using multiple scoring matrices, although their score distribution is known to closely follow extreme value distribution and the corresponding parameters can be estimated by simulation. Ideal estimation would require simulation for each sequence pair, which is impractical. In this paper, we present a simple clustering-classification approach based on amino acid composition to estimate K and λ for a given sequence pair and scoring scheme, including using multiple parameter sets. The resulting set of K and λ for different cluster pairs has large variability even for the same scoring scheme, underscoring the heavy dependence of K and λ on the amino acid composition. The proposed approach in this paper is an attempt to separate the influence of amino acid composition in estimation of statistical significance of pairwise protein alignments. Experiments and analysis of other approaches to estimate statistical parameters also indicate that the methods used in this work estimate the statistical significance with good accuracy.
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References
Altschul, S.F., Madden, T.L., Schäffer, A.A., Zhang, J., Zhang, Z., Miller, W., Lipman, D.J.: Gapped BLAST and PSI-BLAST: A New Generation of Protein Database Search Programs. Nucleic Acids Research 25(17), 3389–3402 (1997)
Smith, T.F., Waterman, M.S.: Identification of Common Molecular Subsequences. Journal of Molecular Biology 147(1), 195–197 (1981)
Sellers, P.H.: Pattern Recognition in Genetic Sequences by Mismatch Density. Bulletin of Mathematical Biology 46(4), 501–514 (1984)
Pearson, W.R.: Effective Protein Sequence Comparison. Methods in Enzymology 266, 227–259 (1996)
Pearson, W.R.: Flexible Sequence Similarity Searching with the FASTA3 Program Package. Methods in Molecular Biology 132, 185–219 (2000)
Huang, X., Chao, K.M.: A Generalized Global Alignment Algorithm. Bioinformatics 19(2), 228–233 (2003)
Huang, X., Brutlag, D.L.: Dynamic Use of Multiple Parameter Sets in Sequence Alignment. Nucleic Acids Research 35(2), 678–686 (2007)
Karlin, S., Altschul, S.F.: Methods for Assessing the Statistical Significance of Molecular Sequence Features by Using General Scoring Schemes. Proceedings of the National Academy of Sciences, USA 87(6), 2264–2268 (1990)
Pearson, W.R.: Empirical Statistical Estimates for Sequence Similarity Searches. Journal of Molecular Biology 276, 71–84 (1998)
Mott, R., Tribe, R.: Approximate Statistics of Gapped Alignments. Journal of Computational Biology 6(1), 91–112 (1999)
Mott, R.: Accurate Formula for P-values of Gapped Local Sequence and Profile Alignments. Journal of Molecular Biology 300, 649–659 (2000)
Altschul, S.F., Bundschuh, R., Olsen, R., Hwa, T.: The estimation of statistical parameters for local alignment score distributions. Nucleic Acids Research 29(2), 351–361 (2001)
Schäffer, A.A., Aravind, L., Madden, T.L., Shavirin, S., Spouge, J.L., Wolf, Y.I., Koonin, E.V., Altschul, S.F.: Improving the Accuracy of PSI-BLAST Protein Database Searches with Composition-based Statistics and Other Refinements. Nucleic Acids Research 29(14), 2994–3005 (2001)
Bundschuh, R.: Rapid Significance Estimation in Local Sequence Alignment with Gaps. In: RECOMB 2001: Proceedings of the fifth annual International Conference on Computational biology, pp. 77–85. ACM, New York (2001)
Poleksic, A., Danzer, J.F., Hambly, K., Debe, D.A.: Convergent Island Statistics: A Fast Method for Determining Local Alignment Score Significance. Bioinformatics 21(12), 2827–2831 (2005)
Kschischo, M., Lässig, M., Yu, Y.: Toward an Accurate Statistics of Gapped Alignments. Bulletin of Mathematical Biology 67, 169–191 (2004)
Grossmann, S., Yakir, B.: Large Deviations for Global Maxima of Independent Superadditive Processes with Negative Drift and an Application to Optimal Sequence Alignments. Bernoulli 10(5), 829–845 (2004)
Pearson, W.R., Wood, T.C.: Statistical Significance in Biological Sequence Comparison. In: Balding, D.J., Bishop, M., Cannings, C. (eds.) Handbook of Statistical Genetics, pp. 39–66. Wiley, Chichester (2001)
Mott, R.: Alignment: Statistical Significance. Encyclopedia of Life Sciences (2005), http://mrw.interscience.wiley.com/emrw/9780470015902/els/article/a0005264/current/abstract
Mitrophanov, A.Y., Borodovsky, M.: Statistical Significance in Biological Sequence Analysis. Briefings in Bioinformatics 7(1), 2–24 (2006)
Eddy, S.R.: Multiple Alignment Using Hidden Markov Models. In: Rawlings, C., Clark, D., Altman, R., Hunter, L., Lengauer, T., Wodak, S. (eds.) Proceedings of the Third International Conference on Intelligent Systems for Molecular Biology, pp. 114–120. AAAI Press, Menlo Park (1995)
Eddy, S.R.: Maximum Likelihood Fitting of Extreme Value Distributions (1997), unpublished manuscript, citeseer.ist.psu.edu/370503.html
Agrawal, A., Brendel, V., Huang, X.: Pairwise Statistical Significance Versus Database Statistical Significance for Local Alignment of Protein Sequences. In: Măndoiu, I., Sunderraman, R., Zelikovsky, A. (eds.) ISBRA 2008. LNCS(LNBI), vol. 4983, pp. 50–61. Springer, Heidelberg (in press, 2008)
Olsen, R., Bundschuh, R., Hwa, T.: Rapid Assessment of Extremal Statistics for Gapped Local Alignment. In: Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology, pp. 211–222. AAAI Press, Menlo Park (1999)
Anderson, T.W.: An Introduction to Multivariate Statistical Analysis, 2nd edn. Wiley-Interscience, Chichester (2003)
Language, R.A.: Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2006)
Huang, X., Miller, W.: A Time-efficient Linear-space Local Similarity Algorithm. Advances in Applied Mathematics 12(3), 337–357 (1991)
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Agrawal, A., Ghosh, A., Huang, X. (2008). Estimating Pairwise Statistical Significance of Protein Local Alignments Using a Clustering-Classification Approach Based on Amino Acid Composition. In: Măndoiu, I., Sunderraman, R., Zelikovsky, A. (eds) Bioinformatics Research and Applications. ISBRA 2008. Lecture Notes in Computer Science(), vol 4983. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79450-9_7
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DOI: https://doi.org/10.1007/978-3-540-79450-9_7
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