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Power Boosts for Cluster Tests

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Comparative Genomics (RCG 2005)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 3678))

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Abstract

Gene cluster significance tests that are based on the number of genes in a cluster in two genomes, and how compactly they are distributed, but not their order, may be made more powerful by the addition of a test component that focuses solely on the similarity of the ordering of the common genes in the clusters in the two genomes. Here we suggest four such tests, compare them, and investigate one of them, the maximum adjacency disruption criterion, in some detail, analytically and through simulation.

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References

  1. Calabrese, P.P., Chakravarty, S., Vision, T.J.: Fast identification and statistical evaluation of segmental homologies in comparative maps. Bioinformatics 19, i74–i80 (2003)

    Google Scholar 

  2. Durand, D., Sankoff, D.: Tests for gene clustering. Journal of Computational Biology 10, 453–482 (2003)

    Article  Google Scholar 

  3. Hoberman, R., Sankoff, D., Durand, D.: The statistical significance of max-gap clusters. In: Lagergren, J. (ed.) RECOMB-WS 2004. LNCS (LNBI), vol. 3388, pp. 55–71. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  4. Hoberman, R., Sankoff, D., Durand, D.: The statistical analysis of spatially clustered genes under the maximum gap criterion. Journal of Computational Biology (2005) (in press)

    Google Scholar 

  5. Sankoff, D., Blanchette, M.: Multiple genome rearrangement and breakpoint phylogeny. Journal of Computational Biology 5, 555–570 (1998)

    Article  Google Scholar 

  6. Sankoff, D., El-Mabrouk, N.: Genome rearrangement. In: Jiang, T., Smith, T., Xu, Y., Zhang, M. (eds.) Current Topics in Computational Biology, pp. 135–155. MIT Press, Cambridge (2002)

    Google Scholar 

  7. Sloane, N.J.A.: The On-Line Encyclopedia of Integer Sequences (2005), http://www.research.att.com/~njas/sequences/

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© 2005 Springer-Verlag Berlin Heidelberg

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Sankoff, D., Haque, L. (2005). Power Boosts for Cluster Tests. In: McLysaght, A., Huson, D.H. (eds) Comparative Genomics. RCG 2005. Lecture Notes in Computer Science(), vol 3678. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11554714_11

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  • DOI: https://doi.org/10.1007/11554714_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28932-6

  • Online ISBN: 978-3-540-31814-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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