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

BMC Bioinformatics

Software

Tak-Wah Lam¹  Junhao Su¹  Ruibang Luo¹  Huijing Yu¹  Zhenxian Zheng¹ 

[1] Department of Computer Science, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China;

关键词: Multi-platform sequencing data;    Deep learning;    Variant calling;   

DOI  :  10.1186/s12859-023-05434-6

 received in 2023-06-07, accepted in 2023-07-31,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundWith 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.ResultsWe 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 at: https://github.com/HKU-BAL/Clair3-MP.ConclusionsThese insights have important implications for researchers and practitioners alike, providing valuable guidance for improving the reliability and efficiency of genomic analysis in diverse applications.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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