RNA-Seq expression analysis currently relies primarily upon exon expression data. The recognized role of introns during translation, and the presence of substantial RNA-Seq counts attributable to introns, provide the rationale for the simultaneous consideration of both exon and intron data. We describe here a method for the coordinated analysis of exon and intron data by investigating their relationship within individual genes and across samples, while taking into account changes in both variability and expression level. This coordinated analysis of exon and intron data offers strong evidence for significant differences that distinguish the profiles of the exon-only expression data from the combined exon and intron data. One advantage of our proposed method, called matched change characterization for exons and introns (MEI), is its straightforward applicability to existing archived data using small modifications to standard RNA-Seq pipelines. Using MEI, we demonstrate that when data are examined for changes in variability across control and case conditions, novel differential changes can be detected. Notably, when MEI criteria were employed in the analysis of an archived data set involving polyarthritic subjects, the number of differentially expressed genes was expanded by sevenfold. More importantly, the observed changes in exon and intron variability with statistically significant false discovery rates could be traced to specific immune pathway gene networks. The application of MEI analysis provides a strategy for incorporating the significance of exon and intron variability and further developing the role of using both exons and intron sequencing counts in studies of gene regulatory processes.

RNA-Seq 表达分析目前主要依赖于外显子表达数据。内含子在翻译过程中的公认作用，以及可归因于内含子的大量 RNA-Seq 计数的存在，为同时考虑外显子和内含子数据提供了基本原理。我们在这里描述了一种协调分析外显子和内含子数据的方法，通过研究它们在单个基因和跨样本中的关系，同时考虑到变异性和表达水平的变化。这种对外显子和内含子数据的协调分析为显着差异提供了强有力的证据，这些差异将仅外显子表达数据与组合外显子和内含子数据区分开来。我们提出的方法的一个优点，称为外显子和内含子的匹配变化表征（MEI），使用对标准 RNA-Seq 管道的小修改，它直接适用于现有的存档数据。使用 MEI，我们证明，当检查数据以了解控制和案例条件之间的可变性变化时，可以检测到新的差异变化。值得注意的是，当 MEI 标准用于分析涉及多关节炎受试者的存档数据集时，差异表达的基因数量增加了七倍。更重要的是，观察到的具有统计显着错误发现率的外显子和内含子变异性的变化可以追溯到特定的免疫通路基因网络。