Improved Recombination Lower Bounds for Haplotype Data
- Vineet BafnaAffiliated withDepartment of Computer Science and Engineering, University of California at San Diego
- , Vikas BansalAffiliated withDepartment of Computer Science and Engineering, University of California at San Diego
We show that computing the lower bound R h is NP-hard and adapt the greedy algorithm for the set cover problem  to obtain a polynomial time algorithm for computing a diversity based bound R g . This algorithm is several orders of magnitude faster than the Recmin program  and the bound R g matches the bound R h almost always.
We also show that computing the lower bound is also NP-hard using a reduction from MAX-2SAT. We give a O(m 2 n ) time algorithm for computing R s for a dataset with n haplotypes and m SNP’s. We propose a new bound R I which extends the history based bound R s using the notion of intermediate haplotypes. This bound detects more recombination events than both R h and R s bounds on many real datasets.
We extend our algorithms for computing R g and R s to obtain lower bounds for haplotypes with missing data. These methods can detect more recombination events for the LPL dataset  than previous bounds and provide stronger evidence for the presence of a recombination hotspot.
We apply our lower bounds to a real dataset  and demonstrate that these can provide a good indication for the presence and the location of recombination hotspots.
- Improved Recombination Lower Bounds for Haplotype Data
- Book Title
- Research in Computational Molecular Biology
- Book Subtitle
- 9th Annual International Conference, RECOMB 2005, Cambridge, MA, USA, May 14-18, 2005. Proceedings
- pp 569-584
- Print ISBN
- Online ISBN
- Series Title
- Lecture Notes in Computer Science
- Series Volume
- Series ISSN
- Springer Berlin Heidelberg
- Copyright Holder
- Springer-Verlag Berlin Heidelberg
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- Editor Affiliations
- 19. Human Genome Center, Institute of Medical Science, University of Tokyo
- 20. Broad Institute of MIT and Harvard
- 21. Computational Genomics Laboratory, Department of Bioengineering, Boston University
- 22. Center for Molecular Biology and Computer Sciecne Department, Brown University
- 23. University of California
- 24. Department of Molecular and Computational Biology, University of Southern California
- Author Affiliations
- 25. Department of Computer Science and Engineering, University of California at San Diego, La Jolla, CA, 92093-0114, USA
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