Alternative splicing enables a gene spliced into different isoforms and hence protein variants. Identifying individual functions of these isoforms help deciphering the functional diversity of proteins. Although much efforts have been made for automatic gene function prediction, few efforts have been moved toward computational isoform function prediction, mainly due to the unavailable (or scanty) functional annotations of isoforms. Existing efforts directly combine multiple RNA-seq datasets without account of the important tissue specificity of alternative splicing. To bridge this gap, we introduce a novel approach called TS-Isofun to predict the functions of isoforms by integrating multiple functional association networks with respect to tissue specificity. TS-Isofun first constructs tissue-specific isoform functional association networks using multiple RNA-seq datasets from tissue-wise. Next, TS-Isofun assigns weights to these networks and models the tissue specificity by selectively integrating them with adaptive weights. It then introduces a joint matrix factorization-based data fusion model to leverage the integrated network, gene-level data and functional annotations of genes to infer the functions of isoforms. To achieve coherent weight assignment and isoform function prediction, TS-Isofun jointly optimizes the weights of individual networks and the isoform function prediction in a unified objective function. Experimental results show that TS-Isofun significantly outperforms state-of-the-art methods and the account of tissue specificity contributes to more accurate isoform function prediction.

中文翻译：

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基于组织特异性的异构体功能预测

选择性剪接使基因能够剪接成不同的同种型，从而剪接成不同的蛋白质变体。识别这些亚型的个体功能有助于破译蛋白质的功能多样性。尽管已经为自动基因功能预测做出了很多努力，但很少有努力转向计算同种型功能预测，这主要是由于同种型的功能注释不可用（或很少）。现有的努力直接结合了多个 RNA-seq 数据集，而没有考虑选择性剪接的重要组织特异性。为了弥合这一差距，我们引入了一种称为 TS-Isofun 的新方法，通过整合与组织特异性相关的多个功能关联网络来预测异构体的功能。TS-Isofunc 首先使用来自组织的多个 RNA-seq 数据集构建组织特异性异构体功能关联网络。接下来，TS-Isofun 为这些网络分配权重，并通过选择性地将它们与自适应权重集成来模拟组织特异性。然后，它引入了一个基于联合矩阵分解的数据融合模型，以利用集成网络、基因级数据和基因的功能注释来推断异构体的功能。为了实现连贯的权重分配和异构函数预测，TS-Isofun 在统一的目标函数中联合优化了单个网络的权重和异构函数预测。实验结果表明，TS-Isofun 显着优于最先进的方法，并且对组织特异性的考虑有助于更准确的异构体功能预测。