WMDS.net: a network control framework for identifying key players in transcriptome programs.
WMDS.net: a network control framework for identifying key players in transcriptome programs.
复制标题
WMDS.net：用于识别转录组程序中关键参与者的网络控制框架
DOI：
10.1093/bioinformatics/btad071
复制
发表时间：
2023-02-14
期刊：
影响因子：
--
通讯作者：
中科院分区：
文献类型：
--
作者：
哺乳动物细胞可以转录重编程为其他细胞表型。在细胞表型基础的转录网络中，这种复杂转换的可控性是固有的生物学特征。这种网络可控性可以通过操作几个关键调节器来解释，以引导转录程序从一种状态到另一种状态。找到转录程序中的关键调控因子可以为细胞表型的网络状态转换提供关键见解。结果为了应对这一挑战，我们提出将转录共表达网络中的关键调控因子识别为能够完全控制网络状态转换的驱动节点的最小支配集（MDS）。基于结构可控性理论，我们建立了一个加权MDS网络模型（WMDS.net）来寻找差分基因共表达网络的驱动节点。WMDS.net的权重将网络中节点的度和两种生理状态之间基因共表达差异的显著性整合到转录网络的节点可控性的度量中。为了证实其有效性，我们将WMDS.net应用于癌症基因组图谱中RNA-seq数据集中的癌症驱动基因的发现。WMDS.net在各种癌症数据集中功能强大，并且在精确度和召回率之间具有更好的平衡，优于其他顶级工具。可用性和实施https://github.com/chaofen123/WMDS.net。补充信息补充数据可在Bioinformatics在线获得。
Abstract Motivation Mammalian cells can be transcriptionally reprogramed to other cellular phenotypes. Controllability of such complex transitions in transcriptional networks underlying cellular phenotypes is an inherent biological characteristic. This network controllability can be interpreted by operating a few key regulators to guide the transcriptional program from one state to another. Finding the key regulators in the transcriptional program can provide key insights into the network state transition underlying cellular phenotypes. Results To address this challenge, here, we proposed to identify the key regulators in the transcriptional co-expression network as a minimum dominating set (MDS) of driver nodes that can fully control the network state transition. Based on the theory of structural controllability, we developed a weighted MDS network model (WMDS.net) to find the driver nodes of differential gene co-expression networks. The weight of WMDS.net integrates the degree of nodes in the network and the significance of gene co-expression difference between two physiological states into the measurement of node controllability of the transcriptional network. To confirm its validity, we applied WMDS.net to the discovery of cancer driver genes in RNA-seq datasets from The Cancer Genome Atlas. WMDS.net is powerful among various cancer datasets and outperformed the other top-tier tools with a better balance between precision and recall. Availability and implementation https://github.com/chaofen123/WMDS.net. Supplementary information Supplementary data are available at Bioinformatics online.
登录
查看更多内容
影响因子：
14.9
作者：
Chung IF;Chen CY;Su SC;Li CY;Wu KJ;Wang HW;Cheng WC
通讯作者：
Cheng WC
影响因子：
14.9
作者：
Gonzalez-Perez A;Lopez-Bigas N
通讯作者：
Lopez-Bigas N
影响因子：
56.9
作者：
Golub, TR;Slonim, DK;Lander, ES
通讯作者：
Lander, ES
影响因子：
12.3
作者：
Gustafsson M;Nestor CE;Zhang H;Barabási AL;Baranzini S;Brunak S;Chung KF;Federoff HJ;Gavin AC;Meehan RR;Picotti P;Pujana MÀ;Rajewsky N;Smith KG;Sterk PJ;Villoslada P;Benson M
通讯作者：
Benson M
影响因子：
12.3
作者：
Bashashati A;Haffari G;Ding J;Ha G;Lui K;Rosner J;Huntsman DG;Caldas C;Aparicio SA;Shah SP
通讯作者：
Shah SP