What We Learned From Big Data for Autophagy Research

Autophagy is the process by which cytoplasmic components are sequestered in autophagosomal vesicles and delivered to the lysosome for degradation. 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 organisation 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 (such as transcriptomics, proteomics, lipidomics and metabolomics) have been developed and carried out specifically focusing on autophagy, and generating a 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.

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Citation Report https://scite.ai/reports/10.3389/fcell.2018.00092
DOI 10.3389/fcell.2018.00092
Date Last Updated 2019-06-18T02:00:01.244455
Evidence open (via page says license)
Funder code(s) Biotechnology and Biological Sciences Research Council (BB/L006324/1)
Journal Is Open Access true
Open Access Status gold
PDF URL https://www.frontiersin.org/articles/10.3389/fcell.2018.00092/pdf
Publisher URL https://doi.org/10.3389/fcell.2018.00092