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Benchmarking datasets for assembly-based variant calling using high-fidelity long reads

BACKGROUND: Recent advances in long-read sequencing technologies have enabled accurate identification of all genetic variants in individuals or cells; this procedure is known as variant calling. However, benchmarking studies on variant calling using different long-read sequencing technologies are st...

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Autores principales: Lee, Hyunji, Kim, Jun, Lee, Junho
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10045170/
https://www.ncbi.nlm.nih.gov/pubmed/36973656
http://dx.doi.org/10.1186/s12864-023-09255-y
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author Lee, Hyunji
Kim, Jun
Lee, Junho
author_facet Lee, Hyunji
Kim, Jun
Lee, Junho
author_sort Lee, Hyunji
collection PubMed
description BACKGROUND: Recent advances in long-read sequencing technologies have enabled accurate identification of all genetic variants in individuals or cells; this procedure is known as variant calling. However, benchmarking studies on variant calling using different long-read sequencing technologies are still lacking. RESULTS: We used two Caenorhabditis elegans strains to measure several variant calling metrics. These two strains shared true-positive genetic variants that were introduced during strain generation. In addition, both strains contained common and distinguishable variants induced by DNA damage, possibly leading to false-positive estimation. We obtained accurate and noisy long reads from both strains using high-fidelity (HiFi) and continuous long-read (CLR) sequencing platforms, and compared the variant calling performance of the two platforms. HiFi identified a 1.65-fold higher number of true-positive variants on average, with 60% fewer false-positive variants, than CLR did. We also compared read-based and assembly-based variant calling methods in combination with subsampling of various sequencing depths and demonstrated that variant calling after genome assembly was particularly effective for detection of large insertions, even with 10 × sequencing depth of accurate long-read sequencing data. CONCLUSIONS: By directly comparing the two long-read sequencing technologies, we demonstrated that variant calling after genome assembly with 10 × or more depth of accurate long-read sequencing data allowed reliable detection of true-positive variants. Considering the high cost of HiFi sequencing, we herein propose appropriate methodologies for performing cost-effective and high-quality variant calling: 10 × assembly-based variant calling. The results of the present study may facilitate the development of methods for identifying all genetic variants at the population level. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-023-09255-y.
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spelling pubmed-100451702023-03-29 Benchmarking datasets for assembly-based variant calling using high-fidelity long reads Lee, Hyunji Kim, Jun Lee, Junho BMC Genomics Research BACKGROUND: Recent advances in long-read sequencing technologies have enabled accurate identification of all genetic variants in individuals or cells; this procedure is known as variant calling. However, benchmarking studies on variant calling using different long-read sequencing technologies are still lacking. RESULTS: We used two Caenorhabditis elegans strains to measure several variant calling metrics. These two strains shared true-positive genetic variants that were introduced during strain generation. In addition, both strains contained common and distinguishable variants induced by DNA damage, possibly leading to false-positive estimation. We obtained accurate and noisy long reads from both strains using high-fidelity (HiFi) and continuous long-read (CLR) sequencing platforms, and compared the variant calling performance of the two platforms. HiFi identified a 1.65-fold higher number of true-positive variants on average, with 60% fewer false-positive variants, than CLR did. We also compared read-based and assembly-based variant calling methods in combination with subsampling of various sequencing depths and demonstrated that variant calling after genome assembly was particularly effective for detection of large insertions, even with 10 × sequencing depth of accurate long-read sequencing data. CONCLUSIONS: By directly comparing the two long-read sequencing technologies, we demonstrated that variant calling after genome assembly with 10 × or more depth of accurate long-read sequencing data allowed reliable detection of true-positive variants. Considering the high cost of HiFi sequencing, we herein propose appropriate methodologies for performing cost-effective and high-quality variant calling: 10 × assembly-based variant calling. The results of the present study may facilitate the development of methods for identifying all genetic variants at the population level. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-023-09255-y. BioMed Central 2023-03-27 /pmc/articles/PMC10045170/ /pubmed/36973656 http://dx.doi.org/10.1186/s12864-023-09255-y Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Lee, Hyunji
Kim, Jun
Lee, Junho
Benchmarking datasets for assembly-based variant calling using high-fidelity long reads
title Benchmarking datasets for assembly-based variant calling using high-fidelity long reads
title_full Benchmarking datasets for assembly-based variant calling using high-fidelity long reads
title_fullStr Benchmarking datasets for assembly-based variant calling using high-fidelity long reads
title_full_unstemmed Benchmarking datasets for assembly-based variant calling using high-fidelity long reads
title_short Benchmarking datasets for assembly-based variant calling using high-fidelity long reads
title_sort benchmarking datasets for assembly-based variant calling using high-fidelity long reads
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10045170/
https://www.ncbi.nlm.nih.gov/pubmed/36973656
http://dx.doi.org/10.1186/s12864-023-09255-y
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