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Article: Boosting variant-calling performance with multi-platform sequencing data using Clair3-MP

TitleBoosting variant-calling performance with multi-platform sequencing data using Clair3-MP
Authors
KeywordsDeep learning
Multi-platform sequencing data
Variant calling
Issue Date3-Aug-2023
PublisherBioMed Central
Citation
BMC Bioinformatics, 2023, v. 24, n. 1 How to Cite?
AbstractBackground: With the continuous advances in third-generation sequencing technology and the increasing affordability of next-generation sequencing technology, sequencing data from different sequencing technology platforms is becoming more common. While numerous benchmarking studies have been conducted to compare variant-calling performance across different platforms and approaches, little attention has been paid to the potential of leveraging the strengths of different platforms to optimize overall performance, especially integrating Oxford Nanopore and Illumina sequencing data.Results: We investigated the impact of multi-platform data on the performance of variant calling through carefully designed experiments with a deep learning-based variant caller named Clair3-MP (Multi-Platform). Through our research, we not only demonstrated the capability of ONT-Illumina data for improved variant calling, but also identified the optimal scenarios for utilizing ONT-Illumina data. In addition, we revealed that the improvement in variant calling using ONT-Illumina data comes from an improvement in difficult genomic regions, such as the large low-complexity regions and segmental and collapse duplication regions. Moreover, Clair3-MP can incorporate reference genome stratification information to achieve a small but measurable improvement in variant calling. Clair3-MP is accessible as an open-source project atConclusions: These insights have important implications for researchers and practitioners alike, providing valuable guidance for improving the reliability and efficiency of genomic analysis in diverse applications.
Persistent Identifierhttp://hdl.handle.net/10722/340458
ISSN
2023 Impact Factor: 2.9
2023 SCImago Journal Rankings: 1.005
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorYu, HJ-
dc.contributor.authorZheng, ZX-
dc.contributor.authorSu, JH-
dc.contributor.authorLam, TW-
dc.contributor.authorLuo, RB-
dc.date.accessioned2024-03-11T10:44:47Z-
dc.date.available2024-03-11T10:44:47Z-
dc.date.issued2023-08-03-
dc.identifier.citationBMC Bioinformatics, 2023, v. 24, n. 1-
dc.identifier.issn1471-2105-
dc.identifier.urihttp://hdl.handle.net/10722/340458-
dc.description.abstractBackground: With the continuous advances in third-generation sequencing technology and the increasing affordability of next-generation sequencing technology, sequencing data from different sequencing technology platforms is becoming more common. While numerous benchmarking studies have been conducted to compare variant-calling performance across different platforms and approaches, little attention has been paid to the potential of leveraging the strengths of different platforms to optimize overall performance, especially integrating Oxford Nanopore and Illumina sequencing data.Results: We investigated the impact of multi-platform data on the performance of variant calling through carefully designed experiments with a deep learning-based variant caller named Clair3-MP (Multi-Platform). Through our research, we not only demonstrated the capability of ONT-Illumina data for improved variant calling, but also identified the optimal scenarios for utilizing ONT-Illumina data. In addition, we revealed that the improvement in variant calling using ONT-Illumina data comes from an improvement in difficult genomic regions, such as the large low-complexity regions and segmental and collapse duplication regions. Moreover, Clair3-MP can incorporate reference genome stratification information to achieve a small but measurable improvement in variant calling. Clair3-MP is accessible as an open-source project atConclusions: These insights have important implications for researchers and practitioners alike, providing valuable guidance for improving the reliability and efficiency of genomic analysis in diverse applications.-
dc.languageeng-
dc.publisherBioMed Central-
dc.relation.ispartofBMC Bioinformatics-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectDeep learning-
dc.subjectMulti-platform sequencing data-
dc.subjectVariant calling-
dc.titleBoosting variant-calling performance with multi-platform sequencing data using Clair3-MP-
dc.typeArticle-
dc.identifier.doi10.1186/s12859-023-05434-6-
dc.identifier.pmid37537536-
dc.identifier.scopuseid_2-s2.0-85166595091-
dc.identifier.volume24-
dc.identifier.issue1-
dc.identifier.eissn1471-2105-
dc.identifier.isiWOS:001042752700001-
dc.publisher.placeLONDON-
dc.identifier.issnl1471-2105-

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