Normalizing Metagenomic Hi-C Data and Detecting Spurious Contacts Using Zero-Inflated Negative Binomial Regression
Normalizing Metagenomic Hi-C Data and Detecting Spurious Contacts Using Zero-Inflated Negative Binomial Regression
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DOI：
10.1089/cmb.2021.0439
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发表时间：
2022-01-12
影响因子：
1.7
通讯作者：
Sun, Fengzhu
中科院分区：
文献类型：
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作者：
Du, Yuxuan;Laperriere, Sarah M.;Sun, Fengzhu
高通量染色体构象捕获技术（High-throughput chromosome构象捕获，Hi-C）近年来被应用于天然微生物群落，显示出同时研究多个基因组的巨大潜力。一些外来因素可能会影响染色体接触，使Hi-C接触图的规范化对下游分析至关重要。然而，目前宏基因组Hi-C归一化方法的缺乏和对虚假物种间接触的无知削弱了数据的可解释性。本文报道了宏基因组Hi-C实验中的两种类型的偏差：显性偏差和隐性偏差，并引入了参数化模型HiCzin来纠正这两种类型的偏差并去除虚假的种间接触。我们证明了用HiCzin标准化的宏基因组Hi-C接触图可以降低偏差，提高检测虚假接触的能力，并且在宏基因组contig聚类中具有更好的性能。
High-throughput chromosome conformation capture (Hi-C) has recently been applied to natural microbial communities and revealed great potential to study multiple genomes simultaneously. Several extraneous factors may influence chromosomal contacts rendering the normalization of Hi-C contact maps essential for downstream analyses. However, the current paucity of metagenomic Hi-C normalization methods and the ignorance for spurious interspecies contacts weaken the interpretability of the data. Here, we report on two types of biases in metagenomic Hi-C experiments: explicit biases and implicit biases, and introduce HiCzin, a parametric model to correct both types of biases and remove spurious interspecies contacts. We demonstrate that the normalized metagenomic Hi-C contact maps by HiCzin result in lower biases, higher capability to detect spurious contacts, and better performance in metagenomic contig clustering.