...
首页> 外文期刊>BMC Bioinformatics >TRaCE+: Ensemble inference of gene regulatory networks from transcriptional expression profiles of gene knock-out experiments
【24h】

TRaCE+: Ensemble inference of gene regulatory networks from transcriptional expression profiles of gene knock-out experiments

机译:TRaCE +:通过基因敲除实验的转录表达谱综合推断基因调控网络

获取原文
   

获取外文期刊封面封底 >>

       

摘要

Background The inference of gene regulatory networks (GRNs) from transcriptional expression profiles is challenging, predominantly due to its underdetermined nature. One important consequence of underdetermination is the existence of many possible solutions to this inference. Our previously proposed ensemble inference algorithm TRaCE addressed this issue by inferring an ensemble of network directed graphs (digraphs) using differential gene expressions from gene knock-out (KO) experiments. However, TRaCE could not deal with the mode of the transcriptional regulations (activation or repression), an important feature of GRNs. Results In this work, we developed a new algorithm called TRaCE+ for the inference of an ensemble of signed GRN digraphs from transcriptional expression data of gene KO experiments. The sign of the edges indicates whether the regulation is an activation (positive) or a repression (negative). TRaCE+ generates the upper and lower bounds of the ensemble, which define uncertain regulatory interactions that could not be verified by the data. As demonstrated in the case studies using Escherichia coli GRN and 100-gene gold-standard GRNs from DREAM 4 network inference challenge, by accounting for regulatory signs, TRaCE+ could extract more information from the KO data than TRaCE, leading to fewer uncertain edges. Importantly, iterating TRaCE+ with an optimal design of gene KOs could resolve the underdetermined issue of GRN inference in much fewer KO experiments than using TRaCE. Conclusions TRaCE+ expands the applications of ensemble GRN inference strategy by accounting for the mode of the gene regulatory interactions. In comparison to TRaCE, TRaCE+ enables a better utilization of gene KO data, thereby reducing the cost of tackling underdetermined GRN inference. TRaCE+ subroutines for MATLAB are freely available at the following website: http://www.cabsel.ethz.ch/tools/trace.html .
机译:背景技术从转录表达谱推断基因调控网络(GRN)具有挑战性,主要是由于其不确定性。确定不足的一个重要结果是,存在许多可能的解决方案来支持这一推断。我们先前提出的集合推理算法TRaCE通过使用基因敲除(KO)实验中的差异基因表达来推断一组网络有向图(图)来解决此问题。但是,TRaCE无法处理转录调控的模式(激活或抑制),这是GRN的重要特征。结果在这项工作中,我们开发了一种称为TRaCE +的新算法,用于根据基因KO实验的转录表达数据推断出一组带符号的GRN有向图。边缘的符号指示调节是激活(正)还是抑制(负)。 TRaCE +生成集合的上限和下限,这定义了无法通过数据验证的不确定的监管相互作用。正如使用DREAM 4网络推论挑战的大肠杆菌GRN和100基因金标准GRN的案例研究所证明的那样,通过考虑监管迹象,与TRaCE相比,TRaCE +可以从KO数据中提取更多信息,从而减少了不确定边缘。重要的是,用基因KO的最佳设计来迭代TRaCE +可以比使用TRaCE少得多的KO实验来解决GRN推论中不确定的问题。结论TRaCE +通过考虑基因调控相互作用的方式,扩展了集合GRN推断策略的应用。与TRaCE相比,TRaCE +能够更好地利用基因KO数据,从而降低了处理不确定的GRN推断的成本。可在以下网站上免费获得MATLAB的TRaCE +子例程:http://www.cabsel.ethz.ch/tools/trace.html。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号