Integrative Science Center of Germplasm Creation in Western China (CHONGQING) Science City, Biological Science Research Center, Southwest University, Chongqing, China.
Nucleic Acids Res. 2024 Oct 28;52(19):e92. doi: 10.1093/nar/gkae789.
The application of high-throughput chromosome conformation capture (Hi-C) technology enables the construction of chromosome-level assemblies. However, the correction of errors and the anchoring of sequences to chromosomes in the assembly remain significant challenges. In this study, we developed a deep learning-based method, AutoHiC, to address the challenges in chromosome-level genome assembly by enhancing contiguity and accuracy. Conventional Hi-C-aided scaffolding often requires manual refinement, but AutoHiC instead utilizes Hi-C data for automated workflows and iterative error correction. When trained on data from 300+ species, AutoHiC demonstrated a robust average error detection accuracy exceeding 90%. The benchmarking results confirmed its significant impact on genome contiguity and error correction. The innovative approach and comprehensive results of AutoHiC constitute a breakthrough in automated error detection, promising more accurate genome assemblies for advancing genomics research.
高通量染色体构象捕获（Hi-C）技术的应用使得构建染色体水平的组装成为可能。然而，在组装中纠正错误和将序列锚定到染色体仍然是重大挑战。在这项研究中，我们开发了一种基于深度学习的方法 AutoHiC，通过增强连续性和准确性来解决染色体水平基因组组装中的挑战。传统的基于 Hi-C 的支架构建通常需要手动细化，但 AutoHiC 则利用 Hi-C 数据进行自动化工作流程和迭代错误校正。在对来自 300 多种物种的数据进行训练后，AutoHiC 表现出超过 90%的稳健平均错误检测准确性。基准测试结果证实了它对基因组连续性和错误校正的重大影响。AutoHiC 的创新方法和全面结果在自动化错误检测方面取得了突破，有望为推进基因组学研究提供更准确的基因组组装。
