Gjoni Ketrin, Gunsalus Laura M, Kuang Shuzhen, McArthur Evonne, Pittman Maureen, Capra John A, Pollard Katherine S
Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA.
Department of Epidemiology & Biostatistics, University of California, San Francisco, CA, USA.
Nat Methods. 2025 Apr;22(4):824-833. doi: 10.1038/s41592-025-02630-5. Epub 2025 Mar 19.
Comparing chromatin contact maps is an essential step in quantifying how three-dimensional (3D) genome organization shapes development, evolution, and disease. However, methods often disagree, and no gold standard exists for comparing pairs of maps. Here, we evaluate 25 ways to compare contact maps using Micro-C and Hi-C data from two cell types and in silico-generated contact maps. We identify similarities and differences between the methods and quantify their robustness to common sources of biological and technical variation, including losses and gains of CTCF-binding sites, changes in contact intensity or patterns, and noise. We find that global comparison methods, such as mean squared error, are suitable for initial screening; however, biologically informed methods are necessary for identifying how maps diverge and for proposing specific functional hypotheses. We provide a reference guide, codebase, and thorough evaluation for rapidly comparing chromatin contact maps at scale to enable biological insights into 3D genome organization.
比较染色质接触图谱是量化三维（3D）基因组组织如何塑造发育、进化和疾病的关键步骤。然而，不同方法的结果往往不一致，并且在比较成对图谱时不存在金标准。在这里，我们使用来自两种细胞类型的Micro-C和Hi-C数据以及计算机生成的接触图谱，评估了25种比较接触图谱的方法。我们确定了这些方法之间的异同，并量化了它们对常见生物学和技术变异来源的稳健性，包括CTCF结合位点的增减、接触强度或模式的变化以及噪声。我们发现，全局比较方法，如均方误差，适用于初步筛选；然而，基于生物学知识的方法对于识别图谱如何不同以及提出特定的功能假设是必要的。我们提供了一个参考指南、代码库以及全面的评估，以便快速大规模地比较染色质接触图谱，从而深入了解3D基因组组织的生物学特性。
