The emerging era of genomic data integration for analyzing splice isoform function.
The emerging era of genomic data integration for analyzing splice isoform function.
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DOI：
10.1016/j.tig.2014.05.005
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发表时间：
2014-08
影响因子：
11.4
通讯作者：
Guan, Yuanfang
中科院分区：
文献类型：
--
作者：
Li, Hong-Dong;Menon, Rajasree;Omenn, Gilbert S.;Guan, Yuanfang
人类绝大多数多外显子基因都经历了选择性剪接，这大大增加了蛋白质种类的功能多样性。预测在异构体水平的功能是必不可少的，以进一步了解发育异常和癌症，这往往表现出异常剪接和异构体表达失调。然而，异构体功能的测定是非常困难的，并且在功能基因组学领域中预测异构体功能的努力受到限制。现在，RNA的深度测序在转录水平上提供了前所未有的表达数据量。我们在这里描述了新兴的计算方法，这些方法整合了大规模的RNA-seq数据，用于预测选择性剪接异构体的功能，我们讨论了它们在发育和癌症生物学中的应用。我们概述了异构体功能预测的未来发展方向，强调了异构基因组数据集成和组织特异性，动态异构体水平网络建模的必要性，这将使该领域充分发挥其潜力。
The vast majority of multi-exon genes in humans undergo alternative splicing, which greatly increases the functional diversity of protein species. Predicting functions at the isoform level is essential to further our understanding of developmental abnormalities and cancers, which frequently exhibit aberrant splicing and dysregulation of isoform expression. However, determination of isoform function is very difficult, and efforts to predict isoform function have been limited in the functional genomics field. Now deep sequencing of RNA provides an unprecedented amount of expression data at the transcript level. We describe here emerging computational approaches that integrate such large-scale RNA-seq data for predicting functions of alternatively spliced isoforms, and we discuss their applications in developmental and cancer biology. We outline future directions for isoform function prediction, emphasizing the need for heterogeneous genomic data integration and tissue-specific, dynamic isoform-level network modeling, which will allow the field to realize its full potential.
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