Motivation Alternative splicing makes significant contributions to functional diversity of transcripts and proteins. Many alternatively spliced gene isoforms have been shown to perform specific biological functions under different contexts. In addition to gene-level expression, the advances of high-throughput sequencing offer a chance to estimate isoform-specific exon expression with a high resolution, which is informative for studying splice variants with network analysis. RESULTS: In this study, we propose a novel network-based analysis framework to predict isoform-specific functions from exon-level RNA-Seq data. In particular, based on exon-level expression data, we firstly propose a unified framework, referred to as Iso-Net, to integrate two new mathematical methods (named MINet and RVNet) that infer co-expression networks at different data scenarios. We demonstrate the superior prediction accuracy of Iso-Net over the existing methods for most simulation data, especially in two extreme cases: sample size is very small and exon numbers of two isoforms are quite different. Furthermore, by defining relevant quantitative measures (e.g., Jaccard correlation coefficient) and combining differential co-expression network analysis and GO functional enrichment analysis, a co-expression network analysis framework is developed to predict functions of isoforms and further, to discover their distinct functions within the same gene. We apply Iso-Net to study gene isoforms for several important transcription factors in human myeloid differentiation with the exon-level RNA-Seq data from three different cell lines. Availability and Implementation Iso-Net is open source and freely available from https://github.com/Dingjie-Wang/Iso-Net.

中文翻译：

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基于网络的计算框架，使用外显子水平表达数据预测和区分基因亚型的功能

动机 选择性剪接对转录物和蛋白质的功能多样性做出了重大贡献。许多选择性剪接的基因同种型已被证明在不同的环境下执行特定的生物学功能。除了基因水平的表达，高通量测序的进步提供了以高分辨率估计同种型特异性外显子表达的机会，这对于通过网络分析研究剪接变体提供了信息。结果：在本研究中，我们提出了一种新的基于网络的分析框架，用于从外显子水平的 RNA-Seq 数据预测异构体特异性功能。特别是基于外显子级别的表达数据，我们首先提出了一个统一的框架，称为Iso-Net，集成两种新的数学方法（名为 MINet 和 RVNet），在不同的数据场景下推断共表达网络。对于大多数模拟数据，我们证明了 Iso-Net 优于现有方法的预测精度，特别是在两种极端情况下：样本量非常小，两种异构体的外显子数量完全不同。此外，通过定义相关的定量测量（如Jaccard相关系数），结合差异共表达网络分析和GO功能富集分析，开发了共表达网络分析框架来预测异构体的功能，并进一步发现它们的不同功能在同一个基因内。我们应用 Iso-Net 使用来自三种不同细胞系的外显子水平 RNA-Seq 数据研究人类骨髓分化中几种重要转录因子的基因亚型。可用性和实施​​ Iso-Net 是开源的，可从 https://github.com/Dingjie-Wang/Iso-Net 免费获得。