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 at: https://github.com/HKU-BAL/Clair3-MP .
CONCLUSIONS: 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.
结果:我们通过精心设计的基于深度学习的变体调用程序Clair3-MP(多平台)的实验,研究了多平台数据对变体调用性能的影响。通过我们的研究,我们不仅展示了ONT-Illumina数据改进变体调用的能力,而且还确定了利用ONT-Illumina数据的最佳方案。此外,我们发现,使用ONT-Illumina数据的变体调用的改进来自于困难基因组区域的改进,例如大型低复杂度区域以及分段和崩溃重复区域。此外,Clair3-MP可以结合参考基因组分层信息,以实现变体识别的小但可测量的改善。Clair3-MP可以作为开源项目访问:https://github.com/HKU-BAL/Clair3-MP。
结论:这些见解对研究人员和从业人员都具有重要意义,为提高基因组分析在各种应用中的可靠性和效率提供有价值的指导。