Respectively solves the Polyploid Balanced Optimal Partition (PBOP) problem and the Optimal Partition with Genotype constraint (PBOPG) problem. H-PoP and H-PoPG are two heuristic partitioning algorithms for single individual haplotyping of polyploids. The softwares are based on dynamic programming and a strategy of limiting the number of intermediate solutions at each iteration. H-PoP might also be applied to help determine the ploidy of an organism.
Key Laboratory of Internet of Things Technologies and Application, College of Physics and Information Science, Hunan Normal University, Changsha, China; State Key Laboratory of Systematic and Evolutionary Botany, Institute of Botany, Chinese; Academy of Sciences, Beijing, China; School of Information Science and Engineering, Central South University, Changsha, China; Department of Computer Science and Engineering, University of California, Riverside, CA, USA; MOE Key Lab of Bioinformatics and Bioinformatics Division, TNLIST / Department of Computer Science and Technology, Tsinghua University, Beijing, China
H-PoP/H-PoPG funding source(s)
Supported in part by the National Natural Science Foundation of China under grant 61370172 and US National Science Foundation grant DBI-1262107.