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Application of Chaotic Laws to Improve Haplotype Assembly Using Chaos Game Representation

Sequence data are deposited in the form of unphased genotypes and it is not possible to directly identify the location of a particular allele on a specific parental chromosome or haplotype. This study employed nonlinear time series modeling approaches to analyze the haplotype sequences obtained from...

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Autores principales: Olyaee, Mohammad Hossein, Khanteymoori, Alireza, Khalifeh, Khosrow
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6637069/
https://www.ncbi.nlm.nih.gov/pubmed/31316124
http://dx.doi.org/10.1038/s41598-019-46844-y
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author Olyaee, Mohammad Hossein
Khanteymoori, Alireza
Khalifeh, Khosrow
author_facet Olyaee, Mohammad Hossein
Khanteymoori, Alireza
Khalifeh, Khosrow
author_sort Olyaee, Mohammad Hossein
collection PubMed
description Sequence data are deposited in the form of unphased genotypes and it is not possible to directly identify the location of a particular allele on a specific parental chromosome or haplotype. This study employed nonlinear time series modeling approaches to analyze the haplotype sequences obtained from the NGS sequencing method. To evaluate the chaotic behavior of haplotypes, we analyzed their whole sequences, as well as several subsequences from distinct haplotypes, in terms of the SNP distribution on their chromosomes. This analysis utilized chaos game representation (CGR) followed by the application of two different scaling methods. It was found that chaotic behavior clearly exists in most haplotype subsequences. For testing the applicability of the proposed model, the present research determined the alleles in gap positions and positions with low coverage by using chromosome subsequences in which 10% of each subsequence’s alleles are replaced by gaps. After conversion of the subsequences’ CGR into the coordinate series, a Local Projection (LP) method predicted the measure of ambiguous positions in the coordinate series. It was discovered that the average reconstruction rate for all input data is more than 97%, demonstrating that applying this knowledge can effectively improve the reconstruction rate of given haplotypes.
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spelling pubmed-66370692019-07-25 Application of Chaotic Laws to Improve Haplotype Assembly Using Chaos Game Representation Olyaee, Mohammad Hossein Khanteymoori, Alireza Khalifeh, Khosrow Sci Rep Article Sequence data are deposited in the form of unphased genotypes and it is not possible to directly identify the location of a particular allele on a specific parental chromosome or haplotype. This study employed nonlinear time series modeling approaches to analyze the haplotype sequences obtained from the NGS sequencing method. To evaluate the chaotic behavior of haplotypes, we analyzed their whole sequences, as well as several subsequences from distinct haplotypes, in terms of the SNP distribution on their chromosomes. This analysis utilized chaos game representation (CGR) followed by the application of two different scaling methods. It was found that chaotic behavior clearly exists in most haplotype subsequences. For testing the applicability of the proposed model, the present research determined the alleles in gap positions and positions with low coverage by using chromosome subsequences in which 10% of each subsequence’s alleles are replaced by gaps. After conversion of the subsequences’ CGR into the coordinate series, a Local Projection (LP) method predicted the measure of ambiguous positions in the coordinate series. It was discovered that the average reconstruction rate for all input data is more than 97%, demonstrating that applying this knowledge can effectively improve the reconstruction rate of given haplotypes. Nature Publishing Group UK 2019-07-17 /pmc/articles/PMC6637069/ /pubmed/31316124 http://dx.doi.org/10.1038/s41598-019-46844-y Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Olyaee, Mohammad Hossein
Khanteymoori, Alireza
Khalifeh, Khosrow
Application of Chaotic Laws to Improve Haplotype Assembly Using Chaos Game Representation
title Application of Chaotic Laws to Improve Haplotype Assembly Using Chaos Game Representation
title_full Application of Chaotic Laws to Improve Haplotype Assembly Using Chaos Game Representation
title_fullStr Application of Chaotic Laws to Improve Haplotype Assembly Using Chaos Game Representation
title_full_unstemmed Application of Chaotic Laws to Improve Haplotype Assembly Using Chaos Game Representation
title_short Application of Chaotic Laws to Improve Haplotype Assembly Using Chaos Game Representation
title_sort application of chaotic laws to improve haplotype assembly using chaos game representation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6637069/
https://www.ncbi.nlm.nih.gov/pubmed/31316124
http://dx.doi.org/10.1038/s41598-019-46844-y
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