BACKGROUND mRNA interaction with other mRNAs and other signaling molecules determine different biological pathways and functions. Gene co-expression network analysis methods have been widely used to identify correlation patterns between genes in various biological contexts (e.g., cancer, mouse genetics, yeast genetics). A challenge remains to identify an optimal partition of the networks where the individual modules (clusters) are neither too small to make any general inferences, nor too large to be biologically interpretable. Clustering thresholds for identification of modules are not systematically determined and depend on user-settable parameters requiring optimization. The absence of systematic threshold determination may result in suboptimal module identification and a large number of unassigned features. RESULTS In this study, we propose a new pipeline to perform gene co-expression network analysis. The proposed pipeline employs WGCNA, a software widely used to perform different aspects of gene co-expression network analysis, and Modularity Maximization algorithm, to analyze novel RNA-Seq data to understand the effects of low-dose 56Fe ion irradiation on the formation of hepatocellular carcinoma in mice. The network results, along with experimental validation, show that using WGCNA combined with Modularity Maximization, provides a more biologically interpretable network in our dataset, than that obtainable using WGCNA alone. The proposed pipeline showed better performance than the existing clustering algorithm in WGCNA, and identified a module that was biologically validated by a mitochondrial complex I assay. CONCLUSIONS We present a pipeline that can reduce the problem of parameter selection that occurs with the existing algorithm in WGCNA, for applicable RNA-Seq datasets. This may assist in the future discovery of novel mRNA interactions, and elucidation of their potential downstream molecular effects.

中文翻译：

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多种途径的有效鉴定：56Fe离子辐照小鼠肝脏的RNA-Seq分析。

背景技术mRNA与其他mRNA和其他信号分子的相互作用决定了不同的生物学途径和功能。基因共表达网络分析方法已被广泛用于鉴定各种生物学环境（例如，癌症，小鼠遗传学，酵母遗传学）中基因之间的相关模式。确定网络的最佳分区仍然是一个挑战，其中各个模块（集群）既不能太小而不能做出任何一般性推论，又不能太大而无法进行生物学解释。用于模块识别的聚类阈值尚未系统确定，而是取决于需要优化的用户可设置参数。缺少系统的阈值确定可能会导致模块识别不理想以及大量未分配的功能。结果在这项研究中，我们提出了一条新的管道来进行基因共表达网络分析。拟议中的管道使用了WGCNA（一种广泛用于执行基因共表达网络分析的不同方面的软件）和模块化最大化算法，以分析新颖的RNA-Seq数据，以了解低剂量56Fe离子辐照对肝细胞形成的影响小鼠癌。网络结果以及实验验证表明，与单独使用WGCNA相比，将WGCNA与Modularity Maximization结合使用可在我们的数据集中提供更具生物学意义的网络。拟议的管道显示出比WGCNA中现有的聚类算法更好的性能，并确定了通过线粒体复合体I分析进行生物学验证的模块。结论我们提出了一个管道，可以减少适用于RNA-Seq数据集的WGCNA中现有算法所发生的参数选择问题。这可能有助于将来发现新的mRNA相互作用，并阐明其潜在的下游分子效应。