Co-expression tools for plant biology: opportunities for hypothesis generation and caveats

被引：359

作者：

Usadel, Bjoern ^{[2
]}

Obayashi, Takeshi ^{[3
]}

Mutwil, Marek ^{[2
]}

Giorgi, Federico M. ^{[2
]}

Bassel, George W. ^{[1
]}

Tanimoto, Mimi ^{[4
]}

Chow, Amanda ^{[1
]}

Steinhauser, Dirk ^{[2
]}

Persson, Staffan ^{[2
]}

Provart, Nicholas J. ^{[1
]}

机构：

[1] Univ Toronto, Ctr Anal Genome Evolut & Funct, Dept Cell & Syst Biol, Toronto, ON M5S 3B2, Canada

[2] Max Planck Inst Mol Plant Physiol, D-14476 Potsdam, Germany

[3] Univ Tokyo, Ctr Human Genome, Inst Med Sci, Minato Ku, Tokyo 1088639, Japan

[4] Univ Guelph, Dept Mol & Cellular Biol, Guelph, ON N1G 2W1, Canada

来源：

PLANT CELL AND ENVIRONMENT | 2009年 / 32卷 / 12期

关键词：

Arabidopsis; bioinformatics; correlation; databases; reverse genetics; GENE REGULATORY NETWORKS; MODEL DATA-ANALYSIS; MICROARRAY DATA; EXPRESSION DATA; SYSTEMS-BIOLOGY; ARABIDOPSIS-THALIANA; METABOLIC NETWORK; ARRAY RESOURCE; DATA SET; IDENTIFICATION;

D O I：

10.1111/j.1365-3040.2009.02040.x

中图分类号：

Q94 [植物学];

学科分类号：

071001 ;

摘要：

Gene co-expression analysis has emerged in the past 5 years as a powerful tool for gene function prediction. In essence, co-expression analysis asks the question 'what are the genes that are co-expressed, that is, those that show similar expression profiles across many experiments, with my gene of interest?'. Genes that are highly co-expressed may be involved in the biological process or processes of the query gene. This review describes the tools that are available for performing such analyses, how each of these perform, and also discusses statistical issues including how normalization of gene expression data can influence co-expression results, calculation of co-expression scores and P values, and the influence of data sets used for co-expression analysis. Finally, examples from the literature will be presented, wherein co-expression has been used to corroborate and discover various aspects of plant biology.

引用

页码：1633 / 1651

页数：19

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