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Data supporting the high-accuracy haplotype imputation using unphased genotype data as the references

The data presented in this article is related to the research article entitled “High-accuracy haplotype imputation using unphased genotype data as the references” which reports the unphased genotype data can be used as reference for haplotyping imputation [1]. This article reports different implemen...

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Detalles Bibliográficos
Autores principales: Li, Wenzhi, Xu, Wei, He, Shaohua, Ma, Li, Song, Qing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4995474/
https://www.ncbi.nlm.nih.gov/pubmed/27595130
http://dx.doi.org/10.1016/j.dib.2016.06.029
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author Li, Wenzhi
Xu, Wei
He, Shaohua
Ma, Li
Song, Qing
author_facet Li, Wenzhi
Xu, Wei
He, Shaohua
Ma, Li
Song, Qing
author_sort Li, Wenzhi
collection PubMed
description The data presented in this article is related to the research article entitled “High-accuracy haplotype imputation using unphased genotype data as the references” which reports the unphased genotype data can be used as reference for haplotyping imputation [1]. This article reports different implementation generation pipeline, the results of performance comparison between different implementations (A, B, and C) and between HiFi and three major imputation software tools. Our data showed that the performances of these three implementations are similar on accuracy, in which the accuracy of implementation-B is slightly but consistently higher than A and C. HiFi performed better on haplotype imputation accuracy and three other software performed slightly better on genotype imputation accuracy. These data may provide a strategy for choosing optimal phasing pipeline and software for different studies.
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spelling pubmed-49954742016-09-02 Data supporting the high-accuracy haplotype imputation using unphased genotype data as the references Li, Wenzhi Xu, Wei He, Shaohua Ma, Li Song, Qing Data Brief Data Article The data presented in this article is related to the research article entitled “High-accuracy haplotype imputation using unphased genotype data as the references” which reports the unphased genotype data can be used as reference for haplotyping imputation [1]. This article reports different implementation generation pipeline, the results of performance comparison between different implementations (A, B, and C) and between HiFi and three major imputation software tools. Our data showed that the performances of these three implementations are similar on accuracy, in which the accuracy of implementation-B is slightly but consistently higher than A and C. HiFi performed better on haplotype imputation accuracy and three other software performed slightly better on genotype imputation accuracy. These data may provide a strategy for choosing optimal phasing pipeline and software for different studies. Elsevier 2016-06-29 /pmc/articles/PMC4995474/ /pubmed/27595130 http://dx.doi.org/10.1016/j.dib.2016.06.029 Text en © 2016 Published by Elsevier Inc. http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Data Article
Li, Wenzhi
Xu, Wei
He, Shaohua
Ma, Li
Song, Qing
Data supporting the high-accuracy haplotype imputation using unphased genotype data as the references
title Data supporting the high-accuracy haplotype imputation using unphased genotype data as the references
title_full Data supporting the high-accuracy haplotype imputation using unphased genotype data as the references
title_fullStr Data supporting the high-accuracy haplotype imputation using unphased genotype data as the references
title_full_unstemmed Data supporting the high-accuracy haplotype imputation using unphased genotype data as the references
title_short Data supporting the high-accuracy haplotype imputation using unphased genotype data as the references
title_sort data supporting the high-accuracy haplotype imputation using unphased genotype data as the references
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4995474/
https://www.ncbi.nlm.nih.gov/pubmed/27595130
http://dx.doi.org/10.1016/j.dib.2016.06.029
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