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A chaotic viewpoint-based approach to solve haplotype assembly using hypergraph model

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  • Mohammad Hossein Olyaee
  • Alireza Khanteymoori
  • Khosrow Khalifeh

Abstract

Decreasing the cost of high-throughput DNA sequencing technologies, provides a huge amount of data that enables researchers to determine haplotypes for diploid and polyploid organisms. Although various methods have been developed to reconstruct haplotypes in diploid form, their accuracy is still a challenging task. Also, most of the current methods cannot be applied to polyploid form. In this paper, an iterative method is proposed, which employs hypergraph to reconstruct haplotype. The proposed method by utilizing chaotic viewpoint can enhance the obtained haplotypes. For this purpose, a haplotype set was randomly generated as an initial estimate, and its consistency with the input fragments was described by constructing a weighted hypergraph. Partitioning the hypergraph specifies those positions in the haplotype set that need to be corrected. This procedure is repeated until no further improvement could be achieved. Each element of the finalized haplotype set is mapped to a line by chaos game representation, and a coordinate series is defined based on the position of mapped points. Then, some positions with low qualities can be assessed by applying a local projection. Experimental results on both simulated and real datasets demonstrate that this method outperforms most other approaches, and is promising to perform the haplotype assembly.

Suggested Citation

  • Mohammad Hossein Olyaee & Alireza Khanteymoori & Khosrow Khalifeh, 2020. "A chaotic viewpoint-based approach to solve haplotype assembly using hypergraph model," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-19, October.
    • Handle: RePEc:plo:pone00:0241291
    DOI: 10.1371/journal.pone.0241291
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    References listed on IDEAS

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    3. Daniel John Lawson & Garrett Hellenthal & Simon Myers & Daniel Falush, 2012. "Inference of Population Structure using Dense Haplotype Data," PLOS Genetics, Public Library of Science, vol. 8(1), pages 1-16, January.
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