Abstract
Progressive methods offer efficient and reasonably good solutions to the multiple sequence alignment problem. However, resulting alignments are biased by guide-trees, especially for relatively distant sequences.
We propose MSARC, a new graph-clustering based algorithm that aligns sequence sets without guide-trees. Experiments on the BAliBASE dataset show that MSARC achieves alignment quality similar to best progressive methods and substantially higher than the quality of other non-progressive algorithms. Furthermore, MSARC outperforms all other methods on sequence sets whose evolutionary distances are hardly representable by a phylogenetic tree. These datasets are most exposed to the guide-tree bias of alignments.
MSARC is available at http://bioputer.mimuw.edu.pl/msarc .
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References
Do, C.B., Mahabhashyam, M.S.P., Brudno, M., Batzoglou, S.: Probcons: Probabilistic consistency-based multiple sequence alignment. Genome. Res. 15(2), 330–340 (2005), http://dx.doi.org/10.1101/gr.2821705
Edgar, R.C.: Muscle: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32(5), 1792–1797 (2004), http://dx.doi.org/10.1093/nar/gkh340
Fiduccia, C.M., Mattheyses, R.M.: A linear-time heuristic for improving network partitions. In: Proceedings of the 19th Design Automation Conference, DAC 1982, pp. 175–181. IEEE Press, Piscataway (1982), http://dl.acm.org/citation.cfm?id=800263.809204
Gonnet, G.H., Cohen, M.A., Benner, S.A.: Exhaustive matching of the entire protein sequence database. Science 256(5062), 1443–1445 (1992)
Gotoh, O.: An improved algorithm for matching biological sequences. J. Mol. Biol. 162(3), 705–708 (1982)
Guindon, S., Dufayard, J.F., Lefort, V., Anisimova, M., Hordijk, W., Gascuel, O.: New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of phyml 3.0. Syst. Biol. 59(3), 307–321 (2010), http://dx.doi.org/10.1093/sysbio/syq010
Hendrickson, B., Leland, R.: A multilevel algorithm for partitioning graphs. In: Proceedings of the 1995 ACM/IEEE Conference on Supercomputing (CDROM). Supercomputing 1995. ACM, New York (1995), http://doi.acm.org/10.1145/224170.224228
Katoh, K., Kuma, K.-I., Toh, H., Miyata, T.: Mafft version 5: improvement in accuracy of multiple sequence alignment. Nucleic Acids Res. 33(2), 511–518 (2005), http://dx.doi.org/10.1093/nar/gki198
Kececioglu, J.: The maximum weight trace problem in multiple sequence alignment. In: Apostolico, A., Crochemore, M., Galil, Z., Manber, U. (eds.) CPM 1993. LNCS, vol. 684, pp. 106–119. Springer, Heidelberg (1993)
Liu, K., Raghavan, S., Nelesen, S., Linder, C.R., Warnow, T.: Rapid and accurate large-scale coestimation of sequence alignments and phylogenetic trees. Science 324(5934), 1561–1564 (2009), http://dx.doi.org/10.1126/science.1171243
Löytynoja, A., Goldman, N.: Phylogeny-aware gap placement prevents errors in sequence alignment and evolutionary analysis. Science 320(5883), 1632–1635 (2008), http://dx.doi.org/10.1126/science.1158395
Lunter, G., Miklós, I., Drummond, A., Jensen, J.L., Hein, J.: Bayesian coestimation of phylogeny and sequence alignment. BMC Bioinformatics 6, 83 (2005), http://dx.doi.org/10.1186/1471-2105-6-83
Miyazawa, S.: A reliable sequence alignment method based on probabilities of residue correspondences. Protein Eng. 8(10), 999–1009 (1995)
Mückstein, U., Hofacker, I.L., Stadler, P.F.: Stochastic pairwise alignments. Bioinformatics 18(suppl. 2), S153–S160 (2002)
Notredame, C., Higgins, D.G., Heringa, J.: T-coffee: A novel method for fast and accurate multiple sequence alignment. J. Mol. Biol. 302(1), 205–217 (2000), http://dx.doi.org/10.1006/jmbi.2000.4042
Redelings, B.D., Suchard, M.A.: Joint bayesian estimation of alignment and phylogeny. Syst. Biol. 54(3), 401–418 (2005), http://dx.doi.org/10.1080/10635150590947041
Roshan, U., Livesay, D.R.: Probalign: multiple sequence alignment using partition function posterior probabilities. Bioinformatics 22(22), 2715–2721 (2006), http://dx.doi.org/10.1093/bioinformatics/btl472
Sievers, F., Wilm, A., Dineen, D., Gibson, T.J., Karplus, K., Li, W., Lopez, R., McWilliam, H., Remmert, M., Söding, J., Thompson, J.D., Higgins, D.G.: Fast, scalable generation of high-quality protein multiple sequence alignments using clustal omega. Mol. Syst. Biol. 7, 539 (2011), http://dx.doi.org/10.1038/msb.2011.75
Subramanian, A.R., Kaufmann, M., Morgenstern, B.: Dialign-tx: greedy and progressive approaches for segment-based multiple sequence alignment. Algorithms Mol. Biol. 3, 6 (2008), http://dx.doi.org/10.1186/1748-7188-3-6
Subramanian, A.R., Weyer-Menkhoff, J., Kaufmann, M., Morgenstern, B.: Dialign-t: an improved algorithm for segment-based multiple sequence alignment. BMC Bioinformatics 6, 66 (2005), http://dx.doi.org/10.1186/1471-2105-6-66
Thompson, J.D., Higgins, D.G., Gibson, T.J.: Clustal w: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 22(22), 4673–4680 (1994)
Thompson, J.D., Koehl, P., Ripp, R., Poch, O.: Balibase 3.0: latest developments of the multiple sequence alignment benchmark. Proteins 61(1), 127–136 (2005), http://dx.doi.org/10.1002/prot.20527
Wong, K.M., Suchard, M.A., Huelsenbeck, J.P.: Alignment uncertainty and genomic analysis. Science 319(5862), 473–476 (2008), http://dx.doi.org/10.1126/science.1151532
Yu, Y.K., Hwa, T.: Statistical significance of probabilistic sequence alignment and related local hidden markov models. J. Comput. Biol. 8(3), 249–282 (2001), http://dx.doi.org/10.1089/10665270152530845
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Modzelewski, M., Dojer, N. (2013). MSARC: Multiple Sequence Alignment by Residue Clustering. In: Darling, A., Stoye, J. (eds) Algorithms in Bioinformatics. WABI 2013. Lecture Notes in Computer Science(), vol 8126. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40453-5_20
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DOI: https://doi.org/10.1007/978-3-642-40453-5_20
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