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.

中文翻译：

---

基于深度学习的方法能够自动准确地组装染色体水平的基因组


高通量染色体构象捕获 （Hi-C） 技术的应用使染色体水平组装体的构建成为可能。然而，纠正错误和将序列锚定到组装中的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，通过提高连续性和准确性来解决染色体水平基因组组装的挑战。传统的 Hi-C 辅助脚手架通常需要手动优化，但 AutoHiC 反而利用 Hi-C 数据进行自动化工作流程和迭代纠错。当使用来自 300+ 物种的数据进行训练时，AutoHiC 表现出超过 90% 的稳健平均错误检测准确率。基准测试结果证实了其对基因组连续性和纠错的显着影响。AutoHiC 的创新方法和综合结果构成了自动错误检测的突破，有望为推进基因组学研究提供更准确的基因组组装。