ResponseNet: revealing signaling and regulatory networks linking genetic and transcriptomic screening data.
ResponseNet: revealing signaling and regulatory networks linking genetic and transcriptomic screening data.
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DOI：
10.1093/nar/gkr359
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发表时间：
2011-07
影响因子：
14.9
通讯作者：
Yeger-Lotem E
中科院分区：
文献类型：
--
作者：
Lan A;Smoly IY;Rapaport G;Lindquist S;Fraenkel E;Yeger-Lotem E
细胞对刺激的反应通常是复杂的，既涉及调节过程，也涉及代谢过程。为确定这些过程的组成部分而进行的大规模实验工作通常包括基因筛选和转录图谱分析。我们之前已经确定，在酵母中，遗传筛选倾向于识别反应调节器，而转录图谱分析倾向于识别代谢过程的组成部分。ResponseNet是一种网络优化方法，它将这些检测的结果与已知分子相互作用的数据相结合。具体地说，ResponseNet确定了一个由信号和调控分子相互作用路径组成的高概率子网络，通过这些路径，假定的响应调节器可能会导致测量到的转录变化。在计算上，这是通过建立一个最小成本流优化问题并使用线性规划工具有效地解决它来实现的。ResponseNet Web服务器为应用ResponseNet提供了一个简单的界面。用户可以上传蛋白质和基因的加权列表，并获得连接他们数据的稀疏、加权的分子相互作用子网络。预测的子网络及其基因本体丰富分析以图形或文本的形式呈现。因此，ResponseNet网络服务器使研究人员能够有意义地整合他们的数据，并大大扩展他们对潜在细胞反应的理解。以前，研究人员仅限于对不同的大规模实验进行单独分析。响应网的网址是：http://bioinfo.bgu.ac.il/respnet.。
Cellular response to stimuli is typically complex and involves both regulatory and metabolic processes. Large-scale experimental efforts to identify components of these processes often comprise of genetic screening and transcriptomic profiling assays. We previously established that in yeast genetic screens tend to identify response regulators, while transcriptomic profiling assays tend to identify components of metabolic processes. ResponseNet is a network-optimization approach that integrates the results from these assays with data of known molecular interactions. Specifically, ResponseNet identifies a high-probability sub-network, composed of signaling and regulatory molecular interaction paths, through which putative response regulators may lead to the measured transcriptomic changes. Computationally, this is achieved by formulating a minimum-cost flow optimization problem and solving it efficiently using linear programming tools. The ResponseNet web server offers a simple interface for applying ResponseNet. Users can upload weighted lists of proteins and genes and obtain a sparse, weighted, molecular interaction sub-network connecting their data. The predicted sub-network and its gene ontology enrichment analysis are presented graphically or as text. Consequently, the ResponseNet web server enables researchers that were previously limited to separate analysis of their distinct, large-scale experiments, to meaningfully integrate their data and substantially expand their understanding of the underlying cellular response. ResponseNet is available at http://bioinfo.bgu.ac.il/respnet.
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DOI：
10.1126/science.1179050
发表时间：
2009-10-09
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Amit I;Garber M;Chevrier N;Leite AP;Donner Y;Eisenhaure T;Guttman M;Grenier JK;Li W;Zuk O;Schubert LA;Birditt B;Shay T;Goren A;Zhang X;Smith Z;Deering R;McDonald RC;Cabili M;Bernstein BE;Rinn JL;Meissner A;Root DE;Hacohen N;Regev A
通讯作者：
Regev A
影响因子：
56.9
作者：
Hillenmeyer, Maureen E.;Fung, Eula;Giaever, Guri
通讯作者：
Giaever, Guri
影响因子：
4.3
作者：
Su, Linhui Julie;Auluck, Pavan K.;Lindquist, Susan
通讯作者：
Lindquist, Susan
影响因子：
64.5
作者：
Shapira SD;Gat-Viks I;Shum BO;Dricot A;de Grace MM;Wu L;Gupta PB;Hao T;Silver SJ;Root DE;Hill DE;Regev A;Hacohen N
通讯作者：
Hacohen N
影响因子：
7.3
作者：
Huang SS;Fraenkel E
通讯作者：
Fraenkel E