DIADE, Univ Montpellier, IRD, CIRAD, Montpellier, France.
Institute of Plant Sciences, University of Bern, Bern, Switzerland.
Methods Mol Biol. 2022;2395:13-31. doi: 10.1007/978-1-0716-1816-5_2.
Over the last few decades, many genes have been functionally characterized and shown to be involved in various metabolic, developmental, and signaling pathways. However it still remains unclear how all these genes and pathways integrate into a unique regulatory network to coordinate the development and the growth, or the response to the environment. This is why unraveling the topology of gene regulatory networks (GRN) has become central to our understanding of all these processes. The recent advancement of high-throughput methods has provided enormous amount of -omics data. These data can now be exploited for rapid network reconstruction with statistical inference methods. We recently published a new GRN inference algorithm called TDCor which reconstructs GRN from time-series transcriptomic data. The algorithm has been released in the form of an R package. Here, I describe into details how to install and use the package.
在过去的几十年中，许多基因的功能已被确定，并被证明参与了各种代谢、发育和信号通路。然而，目前仍不清楚所有这些基因和途径如何整合到一个独特的调控网络中，以协调发育和生长，或对环境的反应。这就是为什么揭示基因调控网络 (GRN) 的拓扑结构对于我们理解所有这些过程变得至关重要。高通量方法的最新进展提供了大量的组学数据。现在可以利用这些数据，通过统计推断方法进行快速网络重建。我们最近发表了一种新的 GRN 推断算法，称为 TDCor，它可以从时间序列转录组数据中重建 GRN。该算法已以 R 包的形式发布。在这里，我将详细描述如何安装和使用该软件包。
