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Comparison of GATK and DeepVariant by trio sequencing

While next-generation sequencing (NGS) has transformed genetic testing, it generates large quantities of noisy data that require a significant amount of bioinformatics to generate useful interpretation. The accuracy of variant calling is therefore critical. Although GATK HaplotypeCaller is a widely...

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Autores principales: Lin, Yi-Lin, Chang, Pi-Chuan, Hsu, Ching, Hung, Miao-Zi, Chien, Yin-Hsiu, Hwu, Wuh-Liang, Lai, FeiPei, Lee, Ni-Chung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8810758/
https://www.ncbi.nlm.nih.gov/pubmed/35110657
http://dx.doi.org/10.1038/s41598-022-05833-4
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author Lin, Yi-Lin
Chang, Pi-Chuan
Hsu, Ching
Hung, Miao-Zi
Chien, Yin-Hsiu
Hwu, Wuh-Liang
Lai, FeiPei
Lee, Ni-Chung
author_facet Lin, Yi-Lin
Chang, Pi-Chuan
Hsu, Ching
Hung, Miao-Zi
Chien, Yin-Hsiu
Hwu, Wuh-Liang
Lai, FeiPei
Lee, Ni-Chung
author_sort Lin, Yi-Lin
collection PubMed
description While next-generation sequencing (NGS) has transformed genetic testing, it generates large quantities of noisy data that require a significant amount of bioinformatics to generate useful interpretation. The accuracy of variant calling is therefore critical. Although GATK HaplotypeCaller is a widely used tool for this purpose, newer methods such as DeepVariant have shown higher accuracy in assessments of gold-standard samples for whole-genome sequencing (WGS) and whole-exome sequencing (WES), but a side-by-side comparison on clinical samples has not been performed. Trio WES was used to compare GATK (4.1.2.0) HaplotypeCaller and DeepVariant (v0.8.0). The performance of the two pipelines was evaluated according to the Mendelian error rate, transition-to-transversion (Ti/Tv) ratio, concordance rate, and pathological variant detection rate. Data from 80 trios were analyzed. The Mendelian error rate of the 77 biological trios calculated from the data by DeepVariant (3.09 ± 0.83%) was lower than that calculated from the data by GATK (5.25 ± 0.91%) (p < 0.001). DeepVariant also yielded a higher Ti/Tv ratio (2.38 ± 0.02) than GATK (2.04 ± 0.07) (p < 0.001), suggesting that DeepVariant proportionally called more true positives. The concordance rate between the 2 pipelines was 88.73%. Sixty-three disease-causing variants were detected in the 80 trios. Among them, DeepVariant detected 62 variants, and GATK detected 61 variants. The one variant called by DeepVariant but not GATK HaplotypeCaller might have been missed by GATK HaplotypeCaller due to low coverage. OTC exon 2 (139 bp) deletion was not detected by either method. Mendelian error rate calculation is an effective way to evaluate variant callers. By this method, DeepVariant outperformed GATK, while the two pipelines performed equally in other parameters.
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spelling pubmed-88107582022-02-03 Comparison of GATK and DeepVariant by trio sequencing Lin, Yi-Lin Chang, Pi-Chuan Hsu, Ching Hung, Miao-Zi Chien, Yin-Hsiu Hwu, Wuh-Liang Lai, FeiPei Lee, Ni-Chung Sci Rep Article While next-generation sequencing (NGS) has transformed genetic testing, it generates large quantities of noisy data that require a significant amount of bioinformatics to generate useful interpretation. The accuracy of variant calling is therefore critical. Although GATK HaplotypeCaller is a widely used tool for this purpose, newer methods such as DeepVariant have shown higher accuracy in assessments of gold-standard samples for whole-genome sequencing (WGS) and whole-exome sequencing (WES), but a side-by-side comparison on clinical samples has not been performed. Trio WES was used to compare GATK (4.1.2.0) HaplotypeCaller and DeepVariant (v0.8.0). The performance of the two pipelines was evaluated according to the Mendelian error rate, transition-to-transversion (Ti/Tv) ratio, concordance rate, and pathological variant detection rate. Data from 80 trios were analyzed. The Mendelian error rate of the 77 biological trios calculated from the data by DeepVariant (3.09 ± 0.83%) was lower than that calculated from the data by GATK (5.25 ± 0.91%) (p < 0.001). DeepVariant also yielded a higher Ti/Tv ratio (2.38 ± 0.02) than GATK (2.04 ± 0.07) (p < 0.001), suggesting that DeepVariant proportionally called more true positives. The concordance rate between the 2 pipelines was 88.73%. Sixty-three disease-causing variants were detected in the 80 trios. Among them, DeepVariant detected 62 variants, and GATK detected 61 variants. The one variant called by DeepVariant but not GATK HaplotypeCaller might have been missed by GATK HaplotypeCaller due to low coverage. OTC exon 2 (139 bp) deletion was not detected by either method. Mendelian error rate calculation is an effective way to evaluate variant callers. By this method, DeepVariant outperformed GATK, while the two pipelines performed equally in other parameters. Nature Publishing Group UK 2022-02-02 /pmc/articles/PMC8810758/ /pubmed/35110657 http://dx.doi.org/10.1038/s41598-022-05833-4 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Lin, Yi-Lin
Chang, Pi-Chuan
Hsu, Ching
Hung, Miao-Zi
Chien, Yin-Hsiu
Hwu, Wuh-Liang
Lai, FeiPei
Lee, Ni-Chung
Comparison of GATK and DeepVariant by trio sequencing
title Comparison of GATK and DeepVariant by trio sequencing
title_full Comparison of GATK and DeepVariant by trio sequencing
title_fullStr Comparison of GATK and DeepVariant by trio sequencing
title_full_unstemmed Comparison of GATK and DeepVariant by trio sequencing
title_short Comparison of GATK and DeepVariant by trio sequencing
title_sort comparison of gatk and deepvariant by trio sequencing
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8810758/
https://www.ncbi.nlm.nih.gov/pubmed/35110657
http://dx.doi.org/10.1038/s41598-022-05833-4
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