GOTHiC, a probabilistic model to resolve complex biases and to identify real interactions in Hi-C data

Hi-C is one of the main methods for investigating spatial co-localisation of DNA in the nucleus. However, the raw sequencing data obtained from Hi-C experiments suffer from large biases and spurious contacts, making it difficult to identify true interactions. Existing methods use complex models to account for biases and do not provide a significance threshold for detecting interactions. Here we introduce a simple binomial probabilistic model that resolves complex biases and distinguishes between true and false interactions. The model corrects biases of known and unknown origin and yields a p-value for each interaction, providing a reliable threshold based on significance. We demonstrate this experimentally by testing the method against a random ligation dataset. Our method outperforms previous methods and provides a statistical framework for further data analysis, such as comparisons of Hi-C interactions between different conditions. GOTHiC is available as a BioConductor package (http://www.bioconductor.org/packages/release/bioc/html/GOTHiC.html).

Hi-C是研究细胞核内DNA空间共定位的主流方法之一。然而，Hi-C实验获取的原始测序数据存在显著偏差与伪接触信号，使得真实DNA互作的识别难度显著增加。现有方法多采用复杂模型校正偏差，但未提供用于检测互作的显著性阈值。本研究提出一种简单的二项概率模型（binomial probabilistic model），可解决复杂偏差问题并区分真实与虚假互作。该模型能够校正已知及未知来源的偏差，并为每一组互作计算P值，基于显著性水平提供可靠的阈值标准。我们通过随机连接数据集（random ligation dataset）对该方法进行验证，实验结果证实其性能优于既往同类工具，并为后续数据分析（如不同实验条件下Hi-C互作的比较分析）提供了标准化统计框架。GOTHiC可作为BioConductor包使用，详情参见：http://www.bioconductor.org/packages/release/bioc/html/GOTHiC.html。