Wigwams: identifying gene modules co-regulated across multiple biological conditions.
Wigwams: identifying gene modules co-regulated across multiple biological conditions.
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WIGWAMS：识别在多种生物条件下共同调节的基因模块。
DOI：
10.1093/bioinformatics/btt728
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发表时间：
2014-04-01
期刊：
影响因子：
--
通讯作者：
Denby KJ
中科院分区：
文献类型：
--
作者：
Polanski K;Rhodes J;Hill C;Zhang P;Jenkins DJ;Kiddle SJ;Jironkin A;Beynon J;Buchanan-Wollaston V;Ott S;Denby KJ
动机：识别共调控基因的模块是解剖生物过程中的调控电路的关键第一步。共调节基因可能通过显示紧密的共表达来揭示自身，例如跨多个时间序列数据集的表达谱的高度相关性。然而，上调或下调的基因的数量通常很大，使得难以区分由共调节引起的依赖性共表达和独立共表达。此外，共调节基因的模块可能仅在时间序列的子集上显示紧密的共表达，即显示条件依赖性调节。结果如下：Wigwams是一种简单而有效的方法，用于识别在多个时间序列的基因表达数据中显示出共调控证据的基因模块。Wigwams分析每个时间序列（条件）内基因表达模式的相似性，并直接测试这些在不同条件下的依赖性或独立性。在每个条件子集中每个基因的表达模式作为调节多个基因的条件依赖性调节机制的潜在特征进行统计学测试。Wigwams不需要特定的时间点，可以处理不同时间尺度上的数据集。可以考虑相对于对照条件的差异表达。输出是简洁和非冗余的，使基因网络重建能够集中在那些基因模块和条件的组合，显示共享的调控机制的证据。Wigwams使用六个拟南芥时间序列表达数据集运行，产生一组跨越不同条件组合的生物学显著模块。可用性和实现：Wigwams的Matlab实现，包括图形用户界面和文档，可在warwick.ac.uk/wigwams上获得。联系方式：k. j.登比@ warwick.ac.uk补充数据：补充数据可在在线生物信息学上获取。
Motivation: Identification of modules of co-regulated genes is a crucial first step towards dissecting the regulatory circuitry underlying biological processes. Co-regulated genes are likely to reveal themselves by showing tight co-expression, e.g. high correlation of expression profiles across multiple time series datasets. However, numbers of up- or downregulated genes are often large, making it difficult to discriminate between dependent co-expression resulting from co-regulation and independent co-expression. Furthermore, modules of co-regulated genes may only show tight co-expression across a subset of the time series, i.e. show condition-dependent regulation. Results: Wigwams is a simple and efficient method to identify gene modules showing evidence for co-regulation in multiple time series of gene expression data. Wigwams analyzes similarities of gene expression patterns within each time series (condition) and directly tests the dependence or independence of these across different conditions. The expression pattern of each gene in each subset of conditions is tested statistically as a potential signature of a condition-dependent regulatory mechanism regulating multiple genes. Wigwams does not require particular time points and can process datasets that are on different time scales. Differential expression relative to control conditions can be taken into account. The output is succinct and non-redundant, enabling gene network reconstruction to be focused on those gene modules and combinations of conditions that show evidence for shared regulatory mechanisms. Wigwams was run using six Arabidopsis time series expression datasets, producing a set of biologically significant modules spanning different combinations of conditions. Availability and implementation: A Matlab implementation of Wigwams, complete with graphical user interfaces and documentation, is available at: warwick.ac.uk/wigwams. Contact: k.j.denby@warwick.ac.uk Supplementary Data: Supplementary data are available at Bioinformatics online.
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DOI：
10.1093/bioinformatics/btt248
发表时间：
2013-07-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Brown P;Baxter L;Hickman R;Beynon J;Moore JD;Ott S
通讯作者：
Ott S
影响因子：
9.5
作者：
Kim, Yongsoo;Han, Seungmin;Hwang, Daehee
通讯作者：
Hwang, Daehee
TransFac及其模块移植：真核生物中的转录基因调节。
影响因子：
14.9
作者：
Matys V;Kel-Margoulis OV;Fricke E;Liebich I;Land S;Barre-Dirrie A;Reuter I;Chekmenev D;Krull M;Hornischer K;Voss N;Stegmaier P;Lewicki-Potapov B;Saxel H;Kel AE;Wingender E
通讯作者：
Wingender E
影响因子：
--
作者：
Maere, Steven;Van Dijck, Patrick;Kuiper, Martin
通讯作者：
Kuiper, Martin
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y