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Accuracy benchmark of the GeneMind GenoLab M sequencing platform for WGS and WES analysis

BACKGROUND: GenoLab M is a recently developed next-generation sequencing (NGS) platform from GeneMind Biosciences. To establish the performance of GenoLab M, we present the first report to benchmark and compare the WGS and WES sequencing data of the GenoLab M sequencer to NovaSeq 6000 and NextSeq 55...

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Autores principales: Li, Chaoyang, Fan, Xue, Guo, Xin, Liu, Yongfeng, Wang, Miao, Zhao, Xiao Chao, Wu, Ping, Yan, Qin, Sun, Lei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9308344/
https://www.ncbi.nlm.nih.gov/pubmed/35869426
http://dx.doi.org/10.1186/s12864-022-08775-3
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author Li, Chaoyang
Fan, Xue
Guo, Xin
Liu, Yongfeng
Wang, Miao
Zhao, Xiao Chao
Wu, Ping
Yan, Qin
Sun, Lei
author_facet Li, Chaoyang
Fan, Xue
Guo, Xin
Liu, Yongfeng
Wang, Miao
Zhao, Xiao Chao
Wu, Ping
Yan, Qin
Sun, Lei
author_sort Li, Chaoyang
collection PubMed
description BACKGROUND: GenoLab M is a recently developed next-generation sequencing (NGS) platform from GeneMind Biosciences. To establish the performance of GenoLab M, we present the first report to benchmark and compare the WGS and WES sequencing data of the GenoLab M sequencer to NovaSeq 6000 and NextSeq 550 platform in various types of analysis. For WGS, thirty-fold sequencing from Illumina NovaSeq platform and processed by GATK pipeline is currently considered as the golden standard. Thus this dataset is generated as a benchmark reference in this study. RESULTS: GenoLab M showed an average of 94.62% of Q20 percentage for base quality, while the NovaSeq was slightly higher at 96.97%. However, GenoLab M outperformed NovaSeq or NextSeq at a duplication rate, suggesting more usable data after deduplication. For WGS short variant calling, GenoLab M showed significant accuracy improvement over the same depth dataset from NovaSeq, and reached similar accuracy to NovaSeq 33X dataset with 22x depth. For 100X WES, the F-score and Precision in GenoLab M were higher than NovaSeq or NextSeq, especially for InDel calling. CONCLUSIONS: GenoLab M is a promising NGS platform for high-performance WGS and WES applications. For WGS, 22X depth in the GenoLab M sequencing platform offers a cost-effective alternative to the current mainstream 33X depth on Illumina.
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spelling pubmed-93083442022-07-24 Accuracy benchmark of the GeneMind GenoLab M sequencing platform for WGS and WES analysis Li, Chaoyang Fan, Xue Guo, Xin Liu, Yongfeng Wang, Miao Zhao, Xiao Chao Wu, Ping Yan, Qin Sun, Lei BMC Genomics Research BACKGROUND: GenoLab M is a recently developed next-generation sequencing (NGS) platform from GeneMind Biosciences. To establish the performance of GenoLab M, we present the first report to benchmark and compare the WGS and WES sequencing data of the GenoLab M sequencer to NovaSeq 6000 and NextSeq 550 platform in various types of analysis. For WGS, thirty-fold sequencing from Illumina NovaSeq platform and processed by GATK pipeline is currently considered as the golden standard. Thus this dataset is generated as a benchmark reference in this study. RESULTS: GenoLab M showed an average of 94.62% of Q20 percentage for base quality, while the NovaSeq was slightly higher at 96.97%. However, GenoLab M outperformed NovaSeq or NextSeq at a duplication rate, suggesting more usable data after deduplication. For WGS short variant calling, GenoLab M showed significant accuracy improvement over the same depth dataset from NovaSeq, and reached similar accuracy to NovaSeq 33X dataset with 22x depth. For 100X WES, the F-score and Precision in GenoLab M were higher than NovaSeq or NextSeq, especially for InDel calling. CONCLUSIONS: GenoLab M is a promising NGS platform for high-performance WGS and WES applications. For WGS, 22X depth in the GenoLab M sequencing platform offers a cost-effective alternative to the current mainstream 33X depth on Illumina. BioMed Central 2022-07-22 /pmc/articles/PMC9308344/ /pubmed/35869426 http://dx.doi.org/10.1186/s12864-022-08775-3 Text en © The Author(s) 2022 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
Li, Chaoyang
Fan, Xue
Guo, Xin
Liu, Yongfeng
Wang, Miao
Zhao, Xiao Chao
Wu, Ping
Yan, Qin
Sun, Lei
Accuracy benchmark of the GeneMind GenoLab M sequencing platform for WGS and WES analysis
title Accuracy benchmark of the GeneMind GenoLab M sequencing platform for WGS and WES analysis
title_full Accuracy benchmark of the GeneMind GenoLab M sequencing platform for WGS and WES analysis
title_fullStr Accuracy benchmark of the GeneMind GenoLab M sequencing platform for WGS and WES analysis
title_full_unstemmed Accuracy benchmark of the GeneMind GenoLab M sequencing platform for WGS and WES analysis
title_short Accuracy benchmark of the GeneMind GenoLab M sequencing platform for WGS and WES analysis
title_sort accuracy benchmark of the genemind genolab m sequencing platform for wgs and wes analysis
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9308344/
https://www.ncbi.nlm.nih.gov/pubmed/35869426
http://dx.doi.org/10.1186/s12864-022-08775-3
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