Gene coexpression network analysis as a source of functional annotation for rice genes.
Gene coexpression network analysis as a source of functional annotation for rice genes.
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DOI：
10.1371/journal.pone.0022196
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发表时间：
2011
期刊：
影响因子：
3.7
通讯作者：
Buell CR
中科院分区：
文献类型：
--
作者：
Childs KL;Davidson RM;Buell CR
随着大量公开的植物基因表达数据集的存在，许多研究小组已经进行了数据分析来构建基因共表达网络并对基因进行功能注释。通常，大量不相关或与条件无关的表达数据被用来构建基因网络。由明确的条件/处理组成的条件依赖性表达实验也已用于创建共表达网络，以帮助检查特定的生物过程。如果存在大量基因和连接，则源自条件依赖性或条件独立数据的基因网络可能难以解释。然而，存在算法来识别共表达网络中高度连接且生物学相关的基因的模块。在这项研究中，我们使用公开的水稻（Oryza sativa）基因表达数据，利用条件依赖和条件无关数据创建基因共表达网络，并使用加权基因共表达网络分析方法识别了这些网络中的基因模块。我们比较了分配给模块的基因数量和基因共表达模块的生物学可解释性，以评估条件依赖性和条件无关基因共表达网络的效用。为了给水稻基因提供功能注释，我们发现通过条件依赖性基因表达实验的共表达分析鉴定的基因模块比通过条件独立数据集分析鉴定的基因模块更有用。我们已将我们的结果纳入 MSU 水稻基因组注释项目数据库，作为 13,537 个基因的额外基于表达的注释，其中 2,980 个基因缺乏功能注释描述。这些结果为我们的数据库提供了两种新类型的功能注释。模块中的基因现在与构成这些模块的集体功能注释的基因组相关联。此外，表达实验的处理/条件下的基因表达模式包括第二种形式的有用注释。
With the existence of large publicly available plant gene expression data sets, many groups have undertaken data analyses to construct gene coexpression networks and functionally annotate genes. Often, a large compendium of unrelated or condition-independent expression data is used to construct gene networks. Condition-dependent expression experiments consisting of well-defined conditions/treatments have also been used to create coexpression networks to help examine particular biological processes. Gene networks derived from either condition-dependent or condition-independent data can be difficult to interpret if a large number of genes and connections are present. However, algorithms exist to identify modules of highly connected and biologically relevant genes within coexpression networks. In this study, we have used publicly available rice (Oryza sativa) gene expression data to create gene coexpression networks using both condition-dependent and condition-independent data and have identified gene modules within these networks using the Weighted Gene Coexpression Network Analysis method. We compared the number of genes assigned to modules and the biological interpretability of gene coexpression modules to assess the utility of condition-dependent and condition-independent gene coexpression networks. For the purpose of providing functional annotation to rice genes, we found that gene modules identified by coexpression analysis of condition-dependent gene expression experiments to be more useful than gene modules identified by analysis of a condition-independent data set. We have incorporated our results into the MSU Rice Genome Annotation Project database as additional expression-based annotation for 13,537 genes, 2,980 of which lack a functional annotation description. These results provide two new types of functional annotation for our database. Genes in modules are now associated with groups of genes that constitute a collective functional annotation of those modules. Additionally, the expression patterns of genes across the treatments/conditions of an expression experiment comprise a second form of useful annotation.
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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：
4.9
作者：
Hamada, Kazuki;Hongo, Kohei;Yano, Kentaro
通讯作者：
Yano, Kentaro
影响因子：
7.4
作者：
Jain, Mukesh;Nijhawan, Aashima;Khurana, Jitendra P.
通讯作者：
Khurana, Jitendra P.
影响因子：
3
作者：
Cosgrove EJ;Gardner TS;Kolaczyk ED
通讯作者：
Kolaczyk ED
从时间序列基因表达谱中识别红糖多孢菌的共表达转录单元模块及其组织
影响因子：
3.7
作者：
Chang X;Liu S;Yu YT;Li YX;Li YY
通讯作者：
Li YY