Mal Chittabrata, Aftabuddin Md, Kundu Sudip
Department of Biophysics, Molecular Biology & Bioinformatics, University of Calcutta, 92, A,P,C, Road, Kolkata 700009, India.
BMC Res Notes. 2014 Dec 8;7:886. doi: 10.1186/1756-0500-7-886.
Analyzing the microarray data of different conditions, one can identify the conserved and condition-specific genes and gene modules, and thus can infer the underlying cellular activities. All the available tools based on Bioconductor and R packages differ in how they extract differential coexpression and at what level they study. There is a need for a user-friendly, flexible tool which can start analysis using raw or preprocessed microarray data and can report different levels of useful information.
We present a GUI software, No3CoGP: Non-Conserved and Conserved Coexpressed Gene Pairs which takes Affymetrix microarray data (.CEL files or log2 normalized.txt files) along with annotation file (.csv file), Chip Definition File (CDF file) and probe file as inputs, utilizes the concept of network density cut-off and Fisher's z-test to extract biologically relevant information. It can identify four possible types of gene pairs based on their coexpression relationships. These are (i) gene pair showing coexpression in one condition but not in the other, (ii) gene pair which is positively coexpressed in one condition but negatively coexpressed in the other condition, (iii) positively and (iv) negatively coexpressed in both the conditions. Further, it can generate modules of coexpressed genes.
Easy-to-use GUI interface enables researchers without knowledge in R language to use No3CoGP. Utilization of one or more CPU cores, depending on the availability, speeds up the program. The output files stored in the respective directories under the user-defined project offer the researchers to unravel condition-specific functionalities of gene, gene sets or modules.
通过分析不同条件下的微阵列数据,可以识别保守的和特定条件下的基因及基因模块,从而推断潜在的细胞活动。所有基于生物导体(Bioconductor)和R包的现有工具在提取差异共表达的方式以及研究层面上存在差异。需要一个用户友好、灵活的工具,它可以使用原始或预处理的微阵列数据开始分析,并能报告不同层面的有用信息。
我们展示了一个图形用户界面(GUI)软件,即No3CoGP:非保守和保守共表达基因对,它将Affymetrix微阵列数据(.CEL文件或log2标准化的.txt文件)以及注释文件(.csv文件)、芯片定义文件(CDF文件)和探针文件作为输入,利用网络密度截止和费舍尔z检验的概念来提取生物学相关信息。它可以根据基因对的共表达关系识别四种可能的类型。这些类型分别是:(i)在一种条件下共表达但在另一种条件下不共表达的基因对;(ii)在一种条件下正共表达但在另一种条件下负共表达的基因对;(iii)在两种条件下都正共表达的基因对;(iv)在两种条件下都负共表达的基因对。此外,它还可以生成共表达基因模块。
易于使用的图形用户界面使不具备R语言知识的研究人员也能使用No3CoGP。根据可用情况使用一个或多个CPU核心可加快程序运行速度。存储在用户定义项目下各自目录中的输出文件使研究人员能够揭示基因、基因集或模块的特定条件功能。