title: H-PoP and H-PoPG: heuristic partitioning algorithms for single individual haplotyping of polyploids.
abstract: Some economically important plants including wheat and cotton have more than two copies of each chromosome. With the decreasing cost and increasing read length of next-generation sequencing technologies, reconstructing the multiple haplotypes of a polyploid genome from its sequence reads becomes practical. However, the computational challenge in polyploid haplotyping is much greater than that in diploid haplotyping, and there are few related methods.
  
  
  
  This article models the polyploid haplotyping problem as an optimal poly-partition problem of the reads, called the Polyploid Balanced Optimal Partition model. For the reads sequenced from a k-ploid genome, the model tries to divide the reads into k groups such that the difference between the reads of the same group is minimized while the difference between the reads of different groups is maximized. When the genotype information is available, the model is extended to the Polyploid Balanced Optimal Partition with Genotype constraint problem. These models are all NP-hard. We propose two heuristic algorithms, H-PoP and H-PoPG, based on dynamic programming and a strategy of limiting the number of intermediate solutions at each iteration, to solve the two models, respectively. Extensive experimental results on simulated and real data show that our algorithms can solve the models effectively, and are much faster and more accurate than the recent state-of-the-art polyploid haplotyping algorithms. The experiments also show that our algorithms can deal with long reads and deep read coverage effectively and accurately. Furthermore, H-PoP might be applied to help determine the ploidy of an organism.
  
  
  
  https://github.com/MinzhuXie/H-PoPG CONTACT: xieminzhu@hotmail.comSupplementary information: Supplementary data are available at Bioinformatics online.
authors.affiliation: Key Laboratory of Internet of Things Technologies and Application, College of Physics and Information Science, Hunan Normal University, Changsha 410081, China.
authors.forename: Minzhu
authors.initials: M
authors.lastname: Xie
authors.affiliation: State Key Laboratory of Systematic and Evolutionary Botany, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China.
authors.forename: Qiong
authors.initials: Q
authors.lastname: Wu
authors.affiliation: School of Information Science and Engineering, Central South University, Changsha 410083, China.
authors.forename: Jianxin
authors.initials: J
authors.lastname: Wang
authors.affiliation: Department of Computer Science and Engineering, University of California, Riverside, CA 92521, USA.|MOE Key Lab of Bioinformatics and Bioinformatics Division, TNLIST/Department of Computer Science and Technology, Tsinghua University, Beijing, China.
authors.forename: Tao
authors.initials: T
authors.lastname: Jiang
mesh_terms: D000465:Algorithms; D006239:Haplotypes; D000066506:Heuristics; D059014:High-Throughput Nucleotide Sequencing; D008957:Models, Genetic; D010944:Plants / Q000235:genetics*; D011123:Polyploidy; D017422:Sequence Analysis, DNA