Bayesian sampling of genomic rearrangement scenarios via double cut and join

Bioinformatics

Volumn 26, Issue 24, 2010, Pages 3012-3019

  Miklós, István ^a,b   Tannier, Eric ^c  

^a ALFRÉD RÉNYI INSTITUTE OF MATHEMATICS   (Hungary)

^b Data Mining and Search Research Group   (France)

^c UNIVERSITÉ LYON 1   (France)

Author keywords

[No Author keywords available]

Indexed keywords

MAMMALIA;

ANIMAL; ARTICLE; BAYES THEOREM; BIOLOGICAL MODEL; GENETICS; GENOME; HUMAN; MOLECULAR EVOLUTION; MONTE CARLO METHOD; PROBABILITY; SACCHAROMYCES CEREVISIAE;

ANIMALS; BAYES THEOREM; EVOLUTION, MOLECULAR; GENOME; HUMANS; MARKOV CHAINS; MODELS, GENETIC; MONTE CARLO METHOD; SACCHAROMYCES CEREVISIAE;

EID: 79951749932     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btq574     Document Type: Article

References (29)

1. Bergeron, A. et al. (2006) A unifying view of genome rearrangements. Lect. Notes Comput. Sci., 4175, 163-173.
2. Bergeron, A. et al. (2008) On computing the breakpoint reuse rate in rearrangement scenarios. Lect. Notes Comput. Sci., 5267, 226-240.
3. Bergeron, A. et al. (2009)Anew linear time algorithm to compute the genomic distance via the double cut and join distance. Theor. Comput. Sci., 410, 5300-5316.
4. Braga, M.D.V. et al. (2008) Exploring the solution space of sorting by reversals with experiments and an application to evolution. IEEE-ACM Trans. Comput. Biol. Bioinform., 5, 348-356.
5. Braga, M.D.V. and Stoye, J. (2009) Counting all DCJ sorting scenarios. Lect. Notes Comput. Sci., 5817, 36-47.
6. Chauve, C. and Tannier, E. (2008)Amethodological framework for the reconstruction of contiguous regions of ancestral genomes and its application to mammalian genomes. PLoS Comput. Biol., 4, 1097-1112.
7. Darling, A. et al. (2008) Dynamics of genome rearrangement in bacterial populations. PLoS Genet., 4, e1000128.
8. Durrett, R. et al. (2004) Bayesian estimation of genomic distance. Genetics, 166, 621-629.
9. Gavranovic, H. and Tannier, E. (2010) Guided genome halving: provably optimal solutions provide good insights into the preduplication ancestral genome of Saccharomyces cerevisiae Proc. PSB, 15, 21-30.
10. Geyer, C.J. (1991) Markov Chain Monte Carlo Maximum Likelihood. In Keramidas, E.M. and Selma, S.M. (eds) Computing Science and Statistics: Proceedings of the 23rd Symposium on the Interface, Interface Foundation of North America Inc., Fairfax Station, VA, pp. 156-163.
11. Gordon, J.L. et al. (2009) Additions, losses, and rearrangements on the evolutionary route from a reconstructed ancestor to the modern Saccharomyces cerevisiae genome. PLoS Genet., 5, e1000485.
12. Hannenhalli, S. and Pevzner, P.A. (1995) Transforming men into mice (polynomial algorithm for genomic distance problem). In Proceedings of 36th Annual Symposium on Foundations of Computer Science (FOCS'95), IEEE Computer Society Press, Los Alamitos, CA, USA, pp. 581-592.
13. Hannenhalli, S. and Pevzner, P.A. (1999) Transforming cabbage into Turnip: polynomial algorithm for sorting signed permutations by reversals. J. ACM, 46, 1-27.
14. Hastings, W.K. (1950) Monte Carlo sampling methods using Markov chains and their applications. Biometrica, 67, 97-109.
15. Larget, B. et al. (2002) Bayesian phylogenetic inference from animal mitochondrial genome arrangements. J.R. Stat. Soc. B., 64, 681-695.
16. Larget, B. et al. (2005) A Bayesian analysis of metazoan mitochondrial genome arrangements. Mol. Biol. Evol., 22, 485-495.
17. Liu, J.S. (2001) Monte Carlo Strategies in Scientific Computing. Cambridge University Press, New York.
18. Metropolis, N. et al. (1953) Equations of state calculations by fast computing machines. J. Chem. Phys., 21, 1087-1091.
19. Miklós, I. (2003) MCMC genome rearrangement. Bioinformatics, 19, ii130-ii137.
20. Miklós, I. and Darling, A. (2009) Efficient sampling of parsimonious inversion histories with application to genome rearrangement in Yersinia. Genome Biol. Evol., 1, 153-164.
21. Miklós, I. et al. (2004) A 'long indel' model for evolutionary sequence alignment. Mol. Biol. Evol., 21, 529-540.
22. Miklós, I. et al. (2010) The metropolized partial importance sampling MCMC mixes slowly on minimum reversal rearrangement paths. ACM/IEEE Trans. Comput. Biol. Bioinformatics, to appear. Available at: http://www.computer.org/portal/web/csdl/doi/10.1109/TCBB.2009.26.
23. Ouangraoua, A. and Bergeron, A. (2009) Parking functions, labeled trees and DCJ sorting scenarios. Lect. Notes Comput. Sci., 5817, 24-35.
24. Ouangraoua, A. and Bergeron, A. (2010) Combinatorial structure of genome rearrangements scenarios. J. Comput. Biol., 17, 1129-1144.
25. Paten, B. et al. (2008) Enredo and pecan: genome-wide mammalian consistency-based multiple alignment with paralogs. Genome Res., 18, 1814-1828.
26. Pevzner, P. and Tesler, G. (2003) Human and mouse genomic sequences reveal extensive breakpoint reuse in mammalian evolution. Proc. Natl Acad. Sci. USA, 100, 7672-7677.
27. Ross, M.T. et al. (2005) The DNA sequence of the human X chromosome. Nature, 434, 325-337.
28. Sturtevant, A.H. and Novitski, E. (1941) The homologies of chromosome elements in the genus Drosophila. Genetics, 26, 517-541.
29. Yancopoulos, S. et al. (2005) Efficient sorting of genomic permutations by translocation, inversion and block interchange. Bioinformatics, 21, 3340-3346.