Identifying functional gene sets from hierarchically clustered expression data: map of abiotic stress regulated genes in Arabidopsis thaliana.
Identifying functional gene sets from hierarchically clustered expression data: map of abiotic stress regulated genes in Arabidopsis thaliana.
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DOI：
10.1093/nar/gkl694
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发表时间：
2006
影响因子：
14.9
通讯作者：
Holm L
中科院分区：
文献类型：
--
作者：
Kankainen M;Brader G;Törönen P;Palva ET;Holm L
我们提出了MultiGO，一个网络工具，用于从分层聚类的基因表达树中识别生物相关基因集（）。高通量基因表达测量技术，如微阵列，目前常用于监测数千个基因的表达。由于这些实验可以产生大量的数据，辅助数据分析和解释的计算方法是必不可少的。MultiGO是一种自动提取多个聚类的生物信息并确定其生物相关性的工具，因此有助于数据的解释。由于分析了整个表达树，因此MultiGO保证报告所有共享共同丰富生物功能的聚类，如Gene Ontology注释所定义。该工具还确定了一个合理的聚类集，它代表了受实验影响的关键生物功能。通过分析来自拟南芥的干旱、寒冷和脱落酸相关的表达数据集来证明性能。这项分析不仅确定了已知的生物学功能，而且还关注了与防御相关基因簇的不太确定的联系。因此，与手动选择基因列表的分析相比，对每个聚类的系统分析可以揭示意想不到的生物现象，并对感兴趣的实验产生更全面的生物学见解。
We present MultiGO, a web-enabled tool for the identification of biologically relevant gene sets from hierarchically clustered gene expression trees (). High-throughput gene expression measuring techniques, such as microarrays, are nowadays often used to monitor the expression of thousands of genes. Since these experiments can produce overwhelming amounts of data, computational methods that assist the data analysis and interpretation are essential. MultiGO is a tool that automatically extracts the biological information for multiple clusters and determines their biological relevance, and hence facilitates the interpretation of the data. Since the entire expression tree is analysed, MultiGO is guaranteed to report all clusters that share a common enriched biological function, as defined by Gene Ontology annotations. The tool also identifies a plausible cluster set, which represents the key biological functions affected by the experiment. The performance is demonstrated by analysing drought-, cold- and abscisic acid-related expression data sets from Arabidopsis thaliana. The analysis not only identified known biological functions, but also brought into focus the less established connections to defense-related gene clusters. Thus, in comparison to analyses of manually selected gene lists, the systematic analysis of every cluster can reveal unexpected biological phenomena and produce much more comprehensive biological insights to the experiment of interest.
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影响因子：
12.3
作者：
Gentleman RC;Carey VJ;Bates DM;Bolstad B;Dettling M;Dudoit S;Ellis B;Gautier L;Ge Y;Gentry J;Hornik K;Hothorn T;Huber W;Iacus S;Irizarry R;Leisch F;Li C;Maechler M;Rossini AJ;Sawitzki G;Smith C;Smyth G;Tierney L;Yang JY;Zhang J
通讯作者：
Zhang J
影响因子：
5.8
作者：
Al-Shahrour, F;Díaz-Uriarte, R;Dopazo, J
通讯作者：
Dopazo, J
影响因子：
30.8
作者：
Jenssen, TK;Lægreid, A;Hovig, E
通讯作者：
Hovig, E
影响因子：
7
作者：
Gibbons, FD;Roth, FP
通讯作者：
Roth, FP
影响因子：
5.8
作者：
Berriz, GF;King, OD;Roth, FP
通讯作者：
Roth, FP