Computational Experiments on Algorithms for Haplotype Inference Problems by Pure Parsimony
I-Lin Wang 0, Hui-E Yang
0Department of Industrial and Information Management
Available Online October 2006.
- https://doi.org/10.2991/jcis.2006.243How to use a DOI?
- Haplotype inference, Genotype, Pure parsimony, Mathematical programming, Heuristic
- To analyze the function of DNA, researchers have to obtain each haplotype, the genetic constitution of an individual chromosome, of an individual for analysis. Due to the significant efforts required in collecting haplotypes, genotypes, which are the descriptions of one conflated pair of haplotypes, are usually collected. Since the genotype data contains insufficient information to identify the combination of DNA sequence in each copy of a chromosome, one has to solve the population haplotype inference problem by pure parsimony which uses the minimum number of haplotypes to infer the haplotype data from genotype data for a population. Previous researches use mathematical programming methods such as integer programming, dynamic programming, and semidefinite programming models to solve the population haplotype inference problem. However, no computational experiment has ever been conducted to evaluate the algorithmic effectiveness. This paper thus conducts the first computational experiments on four haplotyping algorithms, including our new greedy heuristic and three pervious haplotyping algorithms.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - I-Lin Wang AU - Hui-E Yang PY - 2006/10 DA - 2006/10 TI - Computational Experiments on Algorithms for Haplotype Inference Problems by Pure Parsimony PB - Atlantis Press SP - 307 EP - 310 SN - 1951-6851 UR - https://doi.org/10.2991/jcis.2006.243 DO - https://doi.org/10.2991/jcis.2006.243 ID - Wang2006/10 ER -