AREDCI: Assessing Reproducibility and Differential Chromatin Interactions for ChIA-PET Sequencing Data

Understanding the three-dimensional (3D) architecture of chromatin is pivotal for unraveling gene regulation and cellular processes. Currently, a wealth of data on chromatin interactions, such as ChIA-PET sequencing data, is increasingly accessible. However, challenges persist in comparative analyses of these chromatin interactions. Specifically, accurately identifying differential chromatin interactions (DCIs) remains challenging, yet it is crucial for studying gene expression differences during cellular differentiation. Additionally, assessing the inter-sample reproducibility at the sample level, which is a fundamental indicator of the reliability and consistency of replicate experiments, still lacks a computational method. In this work, we present AREDCI, a novel approach integrating data preprocessing, normalization, reproducibility assessment, and DCI identification. By leveraging multiple normalization techniques and developing a self-similarity-based reproducibility algorithm, AREDCI offers a robust evaluation of sample consistency. Additionally, AREDCI designs algorithms based on Kernel Density Estimation (KDE) and local contextual information to enhance the accuracy and reliability of DCI identification. Experimental evaluations demonstrate that AREDCI outperforms existing methods in both reproducibility assessment and DCI identification. Through experiments on simulated and real-world datasets, AREDCI exhibits commendable precision and recall, showcasing its effectiveness in analyzing chromatin interaction data. Notably, AREDCI successfully identifies significant DCIs during mouse cell differentiation, aligning with known biological processes and molecular functions.