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Issue No.02 - April-June (2009 vol.6)
pp: 213-220
Qian Zhu , University of Ottawa, Ottawa
Zaky Adam , University of Ottawa, Ottawa
Vicky Choi , Virginia Tech, Blacksburg
David Sankoff , University of Ottawa, Ottawa
We present a parameterized definition of gene clusters that allows us to control the emphasis placed on conserved order within a cluster. Though motivated by biological rather than mathematical considerations, this parameter turns out to be closely related to the bandwidth parameter of a graph. Our focus will be on how this parameter affects the characteristics of clusters: how numerous they are, how large they are, how rearranged they are, and to what extent they are preserved from ancestor to descendant in a phylogenetic tree. We infer the latter property by dynamic programming optimization of the presence of individual edges at the ancestral nodes of the phylogeny. We apply our analysis to a set of genomes drawn from the Yeast Gene Order Browser.
Comparative genomics, gene clusters, yeast, evolution, phylogeny, genome rearrangements, graph bandwidth, dynamic programming, Saccharomyces cerevisiae, Candida glabrata, Ashbya gossypii, Kluyveromyces waltii, Kluyveromyces lactis.
Qian Zhu, Zaky Adam, Vicky Choi, David Sankoff, "Generalized Gene Adjacencies, Graph Bandwidth, and Clusters in Yeast Evolution", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.6, no. 2, pp. 213-220, April-June 2009, doi:10.1109/TCBB.2008.121
[1] A. Bergeron, S. Corteel, and M. Raffinot, “The Algorithmic of Gene Teams,” Proc. Second Int'l Workshop Bioinformatics (WABI '02), D. Gusfield and R. Guigo, eds., pp.464-476, 2002.
[2] K.P. Byrne and K.H. Wolfe, “The Yeast Gene Order Browser: Combining Curated Homology and Syntenic Context Reveals Gene Fate in Polyploid Species,” Genome Research, vol. 15, pp.1456-1461, 2005.
[3] B. Dujon etal., “Genome Evolution in Yeasts,” Nature, vol. 430, pp.35-44, 2004.
[4] J. Felsenstein, Inferring Phylogenies. Sinauer Assoc., 2004.
[5] A. George, “Computer Implementation of the Finite Element Method,” Technical Report STAN-CS-71-208, Computer Science Dept., Stanford Univ., 1971.
[6] E.M. Gurari and I.H. Sudborough, “Improved Dynamic Programming Algorithms for Bandwidth Minimization and the Mincut Linear Arrangement Problem,” J. Algorithms, vol. 5, pp.531-546, 1984.
[7] R. Hoberman, D. Sankoff, and D. Durand, “The Statistical Analysis of Spatially Clustered Genes under the Maximum Gap Criterion,” J. Computational Biology, vol. 12, pp.1081-1100, 2005.
[8] J. Liu and A. Sherman, “Comparative Analysis of the Cuthill-Mckee and the Reverse Cuthill-Mckee Ordering Algorithms for Sparse Matrices,” SIAM J. Numerical Analysis, vol. 13, pp.198-213, 1975.
[9] C.H. Papadimitriou, “The NP-Completeness of the Bandwidth Minimization Problem,” Computing, vol. 16, pp.263-270, 1976.
[10] J. Saxe, “Dynamic-Programming Algorithms for Recognizing Small-Band-Width Graphs in Polynomial Time,” SIAM J. Algebraic and Discrete Methods, vol. 1, pp.363-369, 1980.
[11] X. Xu and D. Sankoff, “Tests for Gene Clusters Satisfying the Generalized Adjacency Criterion,” Proc. Brazilian Symp. Bioinformatics (BSB '08), A.L.C. Bazzan, M. Craven, and N. F. Martins,eds., pp.152-160, 2008.
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