利用基因组和转录组数据进行全转录组因果网络推断

Whole-Transcriptome Causal Network Inference with Genomic and Transcriptomic Data.

作者信息

Wang Lingfei, Michoel Tom

机构信息

Division of Genetics and Genomics, The Roslin Institute, The University of Edinburgh, Midlothian, Scotland, UK.

Current address: Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway.

出版信息

Methods Mol Biol. 2019;1883:95-109. doi: 10.1007/978-1-4939-8882-2_4.

DOI:10.1007/978-1-4939-8882-2_4

PMID:30547397

Abstract

Reconstruction of causal gene networks can distinguish regulators from targets and reduce false positives by integrating genetic variations. Its recent developments in speed and accuracy have enabled whole-transcriptome causal network inference on a personal computer. Here, we demonstrate this technique with program Findr on 3000 genes from the Geuvadis dataset. Subsequent analysis reveals major hub genes in the reconstructed network.

摘要

因果基因网络的重建可以通过整合基因变异来区分调控因子和靶标，并减少假阳性。其在速度和准确性方面的最新进展使得在个人电脑上进行全转录组因果网络推断成为可能。在这里，我们使用程序Findr对来自Geuvadis数据集的3000个基因展示了这项技术。后续分析揭示了重建网络中的主要枢纽基因。