Abstract
Genome rearrangement is known as one of the main evolutionary mechanisms on the genomic level. Phylogenetic analysis based on rearrangement played a crucial role in biological research in the past decades, especially with the increasing availability of fully sequenced genomes. In general, phylogenetic analysis tries to solve two problems: Small Parsimony Problem (SPP) and Big Parsimony Problem (BPP). Maximum parsimony is a popular approach for SPP and BPP which relies on iteratively solving a NP hard problem, the median problem. As a result, current median solvers and phylogenetic inference methods based on the median problem all face serious problems on scalability and cannot be applied to datasets with large and distant genomes.
In this paper, we propose a new median solver for gene order data that combines double-cut-and-join sorting with the Simulated Annealing algorithm (SAMedian). Based on the median solver, we built a new phylogenetic inference method to solve both SPP and BPP problems. Our experimental results show that the new median solver presents an excellent performance on simulated datasets and the phylogenetic inference tool built based on the new median solver has a better performance than other existing methods.
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References
Zhou, J., Lin, Y., Rajan, V., Hoskins, W., Feng, B., Tang, J.: Analysis of gene copy number changes in tumor phylogenetics. Algorithms Mol. Biol. 11(1), 26 (2016)
Feng, B., Zhou, l., Tang, J.: Ancestral genome reconstruction on whole genome level. Curr. Genomics (2017)
Zhou, J., Lin, Y., Hoskins, W., Tang, J.: An iterative approach for phylogenetic analysis of tumor progression using FISH copy number. In: Harrison, R., Li, Y., Măndoiu, I. (eds.) ISBRA 2015. LNCS, vol. 9096, pp. 402–412. Springer, Cham (2015). doi:10.1007/978-3-319-19048-8_34
Cai, J., Liu, X., Vanneste, K., Proost, S., Tsai, W.-C., Liu, K.-W., Chen, L.-J., He, Y., Xu, Q., Bian, C., et al.: The genome sequence of the orchid phalaenopsis equestris. Nat. Genet. 47(1), 65–72 (2015)
Zhou, J., Lin, Y., Rajan, V., Hoskins, W., Tang, J.: Maximum parsimony analysis of gene copy number changes. In: Pop, M., Touzet, H. (eds.) WABI 2015. LNCS, vol. 9289, pp. 108–120. Springer, Heidelberg (2015). doi:10.1007/978-3-662-48221-6_8
Sankoff, D., Blanchette, M.: Multiple genome rearrangement and breakpoint phylogeny. J. Comput. Biol. 5(3), 555–570 (1998)
Zhou, J., Hu, F., Hoskins, W., Tang, J.: Assessing ancestral genome reconstruction methods by resampling. In: 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 25–31. IEEE (2014)
Moret, B.M.E., Siepel, A.C., Tang, J., Liu, T.: Inversion medians outperform breakpoint medians in phylogeny reconstruction from gene-order data. In: Guigó, R., Gusfield, D. (eds.) WABI 2002. LNCS, vol. 2452, pp. 521–536. Springer, Heidelberg (2002). doi:10.1007/3-540-45784-4_40
Xu, A.W., Moret, B.M.E.: GASTS: parsimony scoring under rearrangements. In: Przytycka, T.M., Sagot, M.-F. (eds.) WABI 2011. LNCS, vol. 6833, pp. 351–363. Springer, Heidelberg (2011). doi:10.1007/978-3-642-23038-7_29
Hu, F., Zhou, J., Zhou, L., Tang, J.: Probabilistic reconstruction of ancestral gene orders with insertions and deletions. IEEE/ACM Trans. Comput. Biol. Bioinform. 11(4), 667–672 (2014)
Yancopoulos, S., Attie, O., Friedberg, R.: Efficient sorting of genomic permutations by translocation, inversion and block interchange. Bioinformatics 21(16), 3340–3346 (2005)
Xu, A.W., Sankoff, D.: Decompositions of multiple breakpoint graphs and rapid exact solutions to the median problem. In: Crandall, K.A., Lagergren, J. (eds.) WABI 2008. LNCS, vol. 5251, pp. 25–37. Springer, Heidelberg (2008). doi:10.1007/978-3-540-87361-7_3
Gao, N., Yang, N., Tang, J.: Ancestral genome inference using a genetic algorithm approach. PLoS ONE 8(5), 62156 (2013)
Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H., Teller, E.: Equation of state calculations by fast computing machines. J. Chem. Phys. 21(6), 1087–1092 (1953)
Kirkpatrick, S.: Optimization by simulated annealing: quantitative studies. J. Stat. Phys. 34(5–6), 975–986 (1984)
Černỳ, V.: Thermodynamical approach to the traveling salesman problem: an efficient simulation algorithm. J. Optim. Theory Appl. 45(1), 41–51 (1985)
Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P., et al.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)
Caprara, A.: On the practical solution of the reversal median problem. In: Gascuel, O., Moret, B.M.E. (eds.) WABI 2001. LNCS, vol. 2149, pp. 238–251. Springer, Heidelberg (2001). doi:10.1007/3-540-44696-6_19
Lin, Y., Rajan, V., Moret, B.M.: Fast and accurate phylogenetic reconstruction from high-resolution whole-genome data and a novel robustness estimator. J. Comput. Biol. 18(9), 1131–1139 (2011)
Feijão, P.: Reconstruction of ancestral gene orders using intermediate genomes. BMC Bioinform. 16(14), 3 (2015)
Braga, M.D.V., Willing, E., Stoye, J.: Genomic distance with DCJ and indels. In: Moulton, V., Singh, M. (eds.) WABI 2010. LNCS, vol. 6293, pp. 90–101. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15294-8_8
Shao, M., Moret, B.M.E.: On computing breakpoint distances for genomes with duplicate genes. In: Singh, M. (ed.) RECOMB 2016. LNCS, vol. 9649, pp. 189–203. Springer, Cham (2016). doi:10.1007/978-3-319-31957-5_14
Hu, F., Lin, Y., Tang, J.: MLGO: phylogeny reconstruction and ancestral inference from gene-order data. BMC Bioinform. 15(1), 354 (2014)
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Xia, R., Zhou, J., Zhou, L., Feng, B., Tang, J. (2017). A Median Solver and Phylogenetic Inference Based on DCJ Sorting. In: Cai, Z., Daescu, O., Li, M. (eds) Bioinformatics Research and Applications. ISBRA 2017. Lecture Notes in Computer Science(), vol 10330. Springer, Cham. https://doi.org/10.1007/978-3-319-59575-7_19
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DOI: https://doi.org/10.1007/978-3-319-59575-7_19
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