Jorge Elise, Foissac Sylvain, Neuvial Pierre, Zytnicki Matthias, Vialaneix Nathalie
GenPhySE, Université de Toulouse, INRAE, ENVT, 31326 Castanet-Tolosan, France.
Institut de Mathématiques de Toulouse, UMR 5219, Université de Toulouse, CNRS UPS, 31062 Toulouse, France.
Brief Bioinform. 2025 Mar 4;26(2). doi: 10.1093/bib/bbaf074.
The 3D organization of the genome plays a crucial role in various biological processes. Hi-C technology is widely used to investigate chromosome structures by quantifying 3D proximity between genomic regions. While numerous computational tools exist for detecting differences in Hi-C data between conditions, a comprehensive review and benchmark comparing their effectiveness is lacking.
This study offers a comprehensive review and benchmark of 10 generic tools for differential analysis of Hi-C matrices at the interaction count level. The benchmark assesses the statistical methods, usability, and performance (in terms of precision and power) of these tools, using both real and simulated Hi-C data. Results reveal a striking variability in performance among the tools, highlighting the substantial impact of preprocessing filters and the difficulty all tools encounter in effectively controlling the false discovery rate across varying resolutions and chromosome sizes.
The complete benchmark is available at https://forgemia.inra.fr/scales/replication-chrocodiff using processed data deposited at https://doi.org/10.57745/LR0W9R.
基因组的三维组织在各种生物过程中起着至关重要的作用。Hi-C技术被广泛用于通过量化基因组区域之间的三维接近度来研究染色体结构。虽然存在许多用于检测不同条件下Hi-C数据差异的计算工具，但缺乏对它们有效性的全面综述和基准比较。
本研究对10种用于在相互作用计数水平上对Hi-C矩阵进行差异分析的通用工具进行了全面综述和基准测试。该基准使用真实和模拟的Hi-C数据评估了这些工具的统计方法、可用性和性能（在精度和功效方面）。结果显示这些工具之间的性能存在显著差异，突出了预处理过滤器的重大影响以及所有工具在不同分辨率和染色体大小下有效控制错误发现率方面遇到的困难。
