A network-based computational framework to predict and differentiate functions for gene isoforms using exon-level expression data.
A network-based computational framework to predict and differentiate functions for gene isoforms using exon-level expression data.
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DOI：
10.1016/j.ymeth.2020.06.005
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发表时间：
2021-05
期刊：
影响因子：
4.8
通讯作者：
Au, Kin Fai
中科院分区：
文献类型：
--
作者：
Wang, Dingjie;Zou, Xiufen;Au, Kin Fai
选择性剪接对转录本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异构体已被证明在不同的背景下发挥特定的生物学功能。除了基因水平表达之外，高通量测序的进步还提供了以高分辨率估计异构体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。在本研究中，我们提出了一种新颖的基于网络的分析框架，用于从外显子级 RNA-Seq 数据预测异构体特异性功能。特别是，基于外显子级表达数据，我们首先提出了一个统一的框架，称为Iso-Net，来集成两种新的数学方法（称为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：样本量非常小且两种亚型的外显子数量相差很大。此外，通过定义相关的定量指标（例如Jaccard相关系数）并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，并进一步发现它们在同一基因内的不同功能。我们应用 Iso-Net 通过来自三种不同细胞系的外显子水平 RNA-Seq 数据来研究人类骨髓分化中几个重要转录因子的基因亚型。 Iso-Net 是开源的，可以从 https://github.com/Dingjie-Wang/Iso-Net 免费获取。
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. 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. Iso-Net is open source and freely available from https://github.com/Dingjie-Wang/Iso-Net.
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