What We Learned From Big Data for Autophagy Research.
What We Learned From Big Data for Autophagy Research.
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DOI：
10.3389/fcell.2018.00092
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发表时间：
2018
影响因子：
5.5
通讯作者：
Nezis IP
中科院分区：
文献类型：
--
作者：
Jacomin AC;Gul L;Sudhakar P;Korcsmaros T;Nezis IP
自噬是细胞质成分在被递送到溶酶体以被降解之前被吞噬在双膜囊泡中的过程。有缺陷的自噬与大量的人类病理学有关。自噬机制的分子机制已被充分描述，并已被广泛研究。然而，理解自噬系统的全球组织及其与其他细胞过程的整合仍然是一个挑战。为此，研究人员在过去几年中开发了各种生物信息学和网络生物学方法。最近，大规模的多组学方法（如基因组学，转录组学，蛋白质组学，脂质组学和代谢组学）已经被开发和实施，特别关注自噬，并生成相关组分的多尺度数据。在这篇综述中，我们概述了各种生物系统中自噬过程的计算机调查和大数据分析的最新应用。
Autophagy is the process by which cytoplasmic components are engulfed in double-membraned vesicles before being delivered to the lysosome to be degraded. Defective autophagy has been linked to a vast array of human pathologies. The molecular mechanism of the autophagic machinery is well-described and has been extensively investigated. However, understanding the global organization of the autophagy system and its integration with other cellular processes remains a challenge. To this end, various bioinformatics and network biology approaches have been developed by researchers in the last few years. Recently, large-scale multi-omics approaches (like genomics, transcriptomics, proteomics, lipidomics, and metabolomics) have been developed and carried out specifically focusing on autophagy, and generating multi-scale data on the related components. In this review, we outline recent applications of in silico investigations and big data analyses of the autophagy process in various biological systems.