Enhancing gene regulatory network inference through data integration with markov random fields.
Enhancing gene regulatory network inference through data integration with markov random fields.
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DOI：
10.1038/srep41174
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发表时间：
2017-02-01
影响因子：
4.6
通讯作者：
Rhee SY
中科院分区：
文献类型：
--
作者：
Banf M;Rhee SY
基因调控网络将转录因子与它们的目标基因联系起来，代表了一张转录调控图。在用计算机破译基因调控网络方面已经取得了很大进展。然而，对大多数真核生物的基因调控网络推断仍然具有挑战性。为了提高基因调控网络推理的准确性，便于实验候选对象的选择，我们开发了一种称为GRACE(基因调控网络推理精度增强)的算法。GRACE利用生物学、先验和异质数据集成，以半监督方式使用马尔可夫随机场生成对真核生物的高置信度网络预测。Grace使用一种新的优化方案来整合监管证据和生物相关性。它特别适合于具有稀疏监管黄金标准数据的模型学习。与使用黑腹果蝇和拟南芥数据的最先进方法相比，我们展示了Grace产生高置信度调控网络的潜力。在拟南芥发育基因调控网络中，GRACE恢复了与细胞周期相关的调控机制，并进一步假设了几个新的调控环节，包括假定的由于细胞增殖修饰而导致的血管结构形成的控制机制。
A gene regulatory network links transcription factors to their target genes and represents a map of transcriptional regulation. Much progress has been made in deciphering gene regulatory networks computationally. However, gene regulatory network inference for most eukaryotic organisms remain challenging. To improve the accuracy of gene regulatory network inference and facilitate candidate selection for experimentation, we developed an algorithm called GRACE (Gene Regulatory network inference ACcuracy Enhancement). GRACE exploits biological a priori and heterogeneous data integration to generate high- confidence network predictions for eukaryotic organisms using Markov Random Fields in a semi-supervised fashion. GRACE uses a novel optimization scheme to integrate regulatory evidence and biological relevance. It is particularly suited for model learning with sparse regulatory gold standard data. We show GRACE’s potential to produce high confidence regulatory networks compared to state of the art approaches using Drosophila melanogaster and Arabidopsis thaliana data. In an A. thaliana developmental gene regulatory network, GRACE recovers cell cycle related regulatory mechanisms and further hypothesizes several novel regulatory links, including a putative control mechanism of vascular structure formation due to modifications in cell proliferation.