DNA methylation analysis in plants: review of computational tools and future perspectives. (9th April 2019)

• Record Type: Journal Article
• Authors: Omony, Jimmy; Nussbaumer, Thomas; Gutzat, Ruben
• Is Part Of: Briefings in bioinformatics. Volume 21:Number 3(2020)
• Page Start: 906
• Page End: 918
• Publication Date: 2019-04-09
• Subjects: epigenomics -- epigenetics -- bisulfite sequencing -- DNA methylation -- plants -- differentially methylated regions; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbz039
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Genome-wide DNA methylation studies have quickly expanded due to advances in next-generation sequencing techniques along with a wealth of computational tools to analyze the data. Most of our knowledge about DNA methylation profiles, epigenetic heritability and the function of DNA methylation in plants derives from the model species Arabidopsis thaliana. There are increasingly many studies on DNA methylation in plants—uncovering methylation profiles and explaining variations in different plant tissues. Additionally, DNA methylation comparisons of different plant tissue types and dynamics during development processes are only slowly emerging but are crucial for understanding developmental and regulatory decisions. Translating this knowledge from plant model species to commercial crops could allow the establishment of new varieties with increased stress resilience and improved yield. In this review, we provide an overview of the most commonly applied bioinformatics tools for the analysis of DNA methylation data (particularly bisulfite sequencing data). The performances of a selection of the tools are analyzed for computational time and agreement in predicted methylated sites for A. thaliana, which has a smaller genome compared to the hexaploid bread wheat. The performance of the tools was benchmarked on five plant genomes. We give examples of applications of DNA methylation data analysis in crops (with a focus on cereals) and an outlook for future developments for DNA methylation status manipulations and data integration.