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DynaMod:动态功能模块化分析。

DynaMod: dynamic functional modularity analysis.

机构信息

Department of Computer Science, KAIST, Daejeon 305-701, South Korea.

出版信息

Nucleic Acids Res. 2010 Jul;38(Web Server issue):W103-8. doi: 10.1093/nar/gkq362. Epub 2010 May 11.

Abstract

A comprehensive analysis of enriched functional categories in differentially expressed genes is important to extract the underlying biological processes of genome-wide expression profiles. Moreover, identification of the network of significant functional modules in these dynamic processes is an interesting challenge. This study introduces DynaMod, a web-based application that identifies significant functional modules reflecting the change of modularity and differential expressions that are correlated with gene expression profiles under different conditions. DynaMod allows the inspection of a wide variety of functional modules such as the biological pathways, transcriptional factor-target gene groups, microRNA-target gene groups, protein complexes and hub networks involved in protein interactome. The statistical significance of dynamic functional modularity is scored based on Z-statistics from the average of mutual information (MI) changes of involved gene pairs under different conditions. Significantly correlated gene pairs among the functional modules are used to generate a correlated network of functional categories. In addition to these main goals, this scoring strategy supports better performance to detect significant genes in microarray analyses, as the scores of correlated genes show the superior characteristics of the significance analysis compared with those of individual genes. DynaMod also offers cross-comparison between different analysis outputs. DynaMod is freely accessible at http://piech.kaist.ac.kr/dynamod.

摘要

对差异表达基因中丰富的功能类别进行全面分析,对于提取全基因组表达谱的潜在生物学过程非常重要。此外,识别这些动态过程中显著功能模块的网络是一个有趣的挑战。本研究介绍了 DynaMod,这是一个基于网络的应用程序,用于识别反映模块性变化和与不同条件下基因表达谱相关的差异表达的显著功能模块。DynaMod 允许检查各种功能模块,如生物途径、转录因子-靶基因组、miRNA-靶基因组、蛋白质复合物和蛋白质互作网络中的枢纽网络。动态功能模块的统计显著性基于涉及基因对在不同条件下的互信息(MI)变化的平均值的 Z 统计进行评分。功能模块中显著相关的基因对用于生成功能类别相关网络。除了这些主要目标之外,这种评分策略还支持在微阵列分析中更好地检测显著基因,因为相关基因的评分显示了与单个基因相比,显著性分析的优越特征。DynaMod 还提供了不同分析输出之间的交叉比较。DynaMod 可在 http://piech.kaist.ac.kr/dynamod 免费获得。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3742/2896096/8f1399345335/gkq362f1.jpg

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