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Annual International Conference on Research in Computational Molecular Biology

RECOMB 2006: Research in Computational Molecular Biology pp 231–245Cite as

Algorithms to Distinguish the Role of Gene-Conversion from Single-Crossover Recombination in the Derivation of SNP Sequences in Populations

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Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 3909))

Abstract

Meiotic recombination is a fundamental biological event and one of the principal evolutionary forces responsible for shaping genetic variation within species. In addition to its fundamental role, recombination is central to several critical applied problems. The most important example is “association mapping” in populations, which is widely hoped to help find genes that influence genetic diseases [3, 4]. Hence, a great deal of recent attention has focused on problems of inferring the historical derivation of sequences in populations when both mutations and recombinations have occurred. In the algorithms literature, most of that recent work has been directed to single-crossover recombination. However, gene-conversion is an important, and more common, form of (two-crossover) recombination which has been much less investigated in the algorithms literature.

In this paper we explicitly incorporate gene-conversion into discrete methods to study historical recombination. We are concerned with algorithms for identifying and locating the extent of historical crossing-over and gene-conversion (along with single-nucleotide mutation), and problems of constructing full putative histories of those events. The novel technical issues concern the incorporation of gene-conversion into recently developed discrete methods [20, 26] that compute lower and upper-bound information on the amount of needed recombination without gene-conversion. We first examine the most natural extension of the lower bound methods from [20], showing that the extension can be computed efficiently, but that this extension can only yield weak lower bounds. We then develop additional ideas that lead to higher lower bounds, and show how to solve, via integer-linear programming, a more biologically realistic version of the lower bound problem. We also show how to compute effective upper bounds on the number of needed single-crossovers and gene-conversions, along with explicit networks showing a putative history of mutations, single-crossovers and gene-conversions.

We validate the significance of these methods by showing that they can be effectively used to distinguish simulation-derived sequences generated without gene-conversion from sequences that were generated with gene-conversion. We apply the methods to recently studied sequences of Arabidopsis thaliana, identifying many more regions in the sequences than were previously identified [22], where gene-conversion may have played a significant role. Demonstration software is available at www.cs. ucdavis.edu/~gusfield.

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Authors and Affiliations

  1. Department of Computer Science, University of California, Davis, CA, 95616, USA

    Yun S. Song, Zhihong Ding, Dan Gusfield & Yufeng Wu

  2. Section of Evolution and Ecology, University of California, Davis, CA, 95616, USA

    Charles H. Langley

Authors
  1. Yun S. Song
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  2. Zhihong Ding
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  3. Dan Gusfield
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  4. Charles H. Langley
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  5. Yufeng Wu
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Editor information

Editors and Affiliations

  1. Georgia Institute of Technology and Università di Padova,  

    Alberto Apostolico

  2. Topic Chairs, P.O. Box

    Concettina Guerra

  3. Center for Molecular Biology and Computer Sciecne Department, Brown University, 115 Waterman St., 02912, Providence, RI, USA

    Sorin Istrail

  4. University of California, San Diego, USA

    Pavel A. Pevzner

  5. Department of Molecular and Computational Biology, University of Southern California, 1050 Childs Way, 90089-2910, Los Angeles, CA, USA

    Michael Waterman

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© 2006 Springer-Verlag Berlin Heidelberg

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Song, Y.S., Ding, Z., Gusfield, D., Langley, C.H., Wu, Y. (2006). Algorithms to Distinguish the Role of Gene-Conversion from Single-Crossover Recombination in the Derivation of SNP Sequences in Populations. In: Apostolico, A., Guerra, C., Istrail, S., Pevzner, P.A., Waterman, M. (eds) Research in Computational Molecular Biology. RECOMB 2006. Lecture Notes in Computer Science(), vol 3909. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11732990_20

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  • DOI: https://doi.org/10.1007/11732990_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-33295-4

  • Online ISBN: 978-3-540-33296-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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