By Andrew G. Clark, Emmanouil T. Dermitzakis, Stylianos E. Antonarakis (auth.), Sorin Istrail, Michael Waterman, Andrew Clark (eds.)
This publication constitutes the post-proceedings of the DIMACS/RECOMB satellite tv for pc Workshop on Computational tools for SNPs and Haplotype Inference held in Piscataway, NJ, united states, in November 2002.
The e-book provides ten revised complete papers in addition to abstracts of the rest workshop papers. All appropriate present matters in computational tools for SNP and haplotype research and their purposes to sickness institutions are addressed.
Read or Download Computational Methods for SNPs and Haplotype Inference: DIMACS/RECOMB Satellite Workshop, Piscataway, NJ, USA, November 21-22, 2002. Revised Papers PDF
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Extra info for Computational Methods for SNPs and Haplotype Inference: DIMACS/RECOMB Satellite Workshop, Piscataway, NJ, USA, November 21-22, 2002. Revised Papers
Lander and M. Waterman, editors, Calculating the Secretes of Life. National Academy Press, 1995. 36. L. Wang and L. Xu. Haplotype inference by maximum parsimony. Bioinformatics, 19:1773–1780, 2003. A Survey of Computational Methods for Determining Haplotypes Bjarni V. Halld´ orsson, Vineet Bafna , Nathan Edwards, Ross Lippert, Shibu Yooseph , and Sorin Istrail Informatics Research, Celera Genomics/Applied Biosystems, 45 W. com Abstract. It is widely anticipated that the study of variation in the human genome will provide a means of predicting risk of a variety of complex diseases.
This is an approximation to the probability that h can come from some h after a geometrically distributed number of single site mutations. This approximation arose from considering a random population model in . It should be noted that while the matrix M appears to be of exponential size, an arbitrary element of (I − 2Nθ+θ M )−1 can be computed in O(m) time. An implementation of this algorithm by Stephens, Smith, and Donnelly is PHASE [50,49]. An alternative implementation, which more closely follows the maximum likelihood method was produced by Niu et al .
Gideon Greenspan and Dan Geiger. Model-based inference of haplotype block variation. In Proceedings of the Seventh Annual International Conference on Computational Molecular Biology (RECOMB), pages 131–137, 2003. 19. D. Gusﬁeld. A practical algorithm for optimal inference of haplotypes from diploid populations. In Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology (ISMB), pages 183–189, 2000. 20. D. Gusﬁeld. Inference of haplotypes from samples of diploid populations: Complexity and algorithms.