徐炳祥 (2025-08-29 16:01):
#paper doi: 10.1093/bib/bbaf074 Briefings in Bioinformatics, 2025, A comprehensive review and benchmark of differential analysis tools for Hi-C data。Hi-C作为研究染色质空间构象的主流技术,其差异分析对研究染色质空间构象在基因表达调控过程中的作用至关重要,然而目前缺乏得到广泛认可的此类工具。本文对10种Hi-C数据差异分析工具进行了全面的综述与性能评估,重点关注它们在识别不同条件下染色质互作差异方面的统计方法、可用性和实际表现。研究通过半模拟数据和真实CTCF缺失实验进行测试,发现工具在预处理过滤、标准化步骤上存在显著差异,且多数工具难以有效控制错误发现率(FDR)。结果表明,diffHic 在整体性能上表现最佳,能较好地控制I类错误并保持较高的检测效能;而基于二维空间结构的工具(如FIND、Selfish)并未显示出预期优势。文章强调预处理步骤对结果影响巨大,并指出当前工具尚不支持复杂实验设计(如配对或重复测量数据),呼吁开发更稳健且灵活的分析方法。
A comprehensive review and benchmark of differential analysis tools for Hi-C data
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Abstract:
Abstract Motivation 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. Results 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. Availability The complete benchmark is available at https://forgemia.inra.fr/scales/replication-chrocodiff using processed data deposited at https://doi.org/10.57745/LR0W9R. Contact nathalie.vialaneix@inrae.fr
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