BICORN: An R package for integrative inference of de novo cis-regulatory modules
BICORN: An R package for integrative inference of de novo cis-regulatory modules
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DOI：
10.1038/s41598-020-63043-2
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发表时间：
2020-05-14
影响因子：
4.6
通讯作者：
Xuan, Jianhua
中科院分区：
文献类型：
--
作者：
Chen, Xi;Gu, Jinghua;Xuan, Jianhua
全基因组转录因子 (TF) 结合信号分析揭示了基于推断的顺式调控模块 (CRM) 的 TF 结合位点的共定位。 CRM 在理解特定条件下多个 TF 的合作方面发挥着关键作用。然而，CRM 的功能及其对附近基因转录的影响是高度动态且特定于环境的，因此很难表征。 BICORN（合作调节网络的贝叶斯推理）构建了分层贝叶斯模型，并根据特定细胞类型的 TF 基因结合事件和基因表达数据推断特定上下文的 CRM。 BICORN 根据与感兴趣基因相关的调控区域的输入 TF 结合自动搜索候选 CRM 列表。 BICORN 应用吉布斯采样迭代估计 CRM 的模型参数、TF 活性以及基因转录的相应调节，并将其建模为调节目标基因的功能性 CRM 的稀疏网络。 BICORN 包在 R（版本 3.4 或更高版本）中实现，并在 CRAN 服务器上公开提供：https://cran.r-project.org/web/packages/BICORN/index.html。
Genome-wide transcription factor (TF) binding signal analyses reveal co-localization of TF binding sites based on inferred cis-regulatory modules (CRMs). CRMs play a key role in understanding the cooperation of multiple TFs under specific conditions. However, the functions of CRMs and their effects on nearby gene transcription are highly dynamic and context-specific and therefore are challenging to characterize. BICORN (Bayesian Inference of COoperative Regulatory Network) builds a hierarchical Bayesian model and infers context-specific CRMs based on TF-gene binding events and gene expression data for a particular cell type. BICORN automatically searches for a list of candidate CRMs based on the input TF bindings at regulatory regions associated with genes of interest. Applying Gibbs sampling, BICORN iteratively estimates model parameters of CRMs, TF activities, and corresponding regulation on gene transcription, which it models as a sparse network of functional CRMs regulating target genes. The BICORN package is implemented in R (version 3.4 or later) and is publicly available on the CRAN server at https://cran.r-project.org/web/packages/BICORN/index.html.