Hi-C experiments have been widely adopted to study chromatin spatial organization, which plays an essential role in genome function. We have recently identified frequently interacting regions (FIREs) and found that they are closely associated with cell-type-specific gene regulation. However, computational tools for detecting FIREs from Hi-C data are still lacking. In this work, we present FIREcaller, a stand-alone, user-friendly R package for detecting FIREs from Hi-C data. FIREcaller takes raw Hi-C contact matrices as input, performs within-sample and cross-sample normalization, and outputs continuous FIRE scores, dichotomous FIREs, and super-FIREs. Applying FIREcaller to Hi-C data from various human tissues, we demonstrate that FIREs and super-FIREs identified, in a tissue-specific manner, are closely related to gene regulation, are enriched for enhancer-promoter (E-P) interactions, tend to overlap with regions exhibiting epigenomic signatures of -regulatory roles, and aid the interpretation or GWAS variants. The FIREcaller package is implemented in R and freely available at .

中文翻译：

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FIREcaller：从 Hi-C 数据中检测频繁交互的区域


Hi-C 实验已被广泛用于研究染色质空间组织，这在基因组功能中发挥着重要作用。我们最近发现了频繁相互作用区域（FIRE），并发现它们与细胞类型特异性基因调控密切相关。然而，仍然缺乏从 Hi-C 数据中检测 FIRE 的计算工具。在这项工作中，我们提出了 FIREcaller，这是一个独立的、用户友好的 R 包，用于从 Hi-C 数据中检测 FIRE。 FIREcaller 以原始 Hi-C 接触矩阵作为输入，执行样本内和样本间归一化，并输出连续 FIRE 分数、二分 FIRE 和超级 FIRE。将 FIREcaller 应用到来自各种人体组织的 Hi-C 数据，我们证明以组织特异性方式识别的 FIRE 和超级 FIRE 与基因调控密切相关，增强子-启动子 (EP) 相互作用得到丰富，并且往往重叠具有具有调节作用的表观基因组特征的区域，并帮助解释或 GWAS 变异。 FIREcaller 包在 R 中实现，可在 上免费获得。