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In Advances in experimental medicine and biology

Autophagy is an important and dynamic biological process, and provides an ideal application scenario for bioinformatics to develop new data resources, algorithms, tools and computational or mathematic models for a better understanding of complex regulatory mechanisms in cells. In the past decade, great efforts have been taken on the development of numerous bioinformatics technologies in autophagy research, and a comprehensive summarization of these important studies will provide a timely reference for both biologists and bioinformaticians who are working in the field of autophagy. In this book chapter, we first introduce bioinformatics technologies that allow sequence analysis of autophagy genes. We briefly summarize the mainstream algorithms in sequence alignment for the identification of homologous autophagy genes and emphasize the computational identification of potential orthologs and paralogs, as well as the evolutionary analysis of autophagy gene families. Three methods for the recognition of autophagy-related sequence motifs are introduced: regular expression, position-specific scoring matrix (PSSM) and group-based prediction system (GPS). Second, we carefully summarize recent progress in the analysis of autophagy-related omics data. We discuss how two major types of computational methods, enrichment analysis and network analysis can be used to analyze omics data, including transcriptomics, non-coding RNAomics, epigenomics, proteomics, phosphoproteomics and protein lysine modification (PLM) omics data. Finally, we summarize several important autophagy-related data resources, including both autophagy gene databases and autophagy-related RNA databases. We anticipate that more useful bioinformatics technologies will be developed and play an ever-more-important role in the analysis of autophagy.

Xue Yu, Wang Dong, Peng Di