DNA methylation analysis in plants: review of computational tools and future perspectives

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.

Keywords: DNA methylation; bisulfite sequencing; differentially methylated regions; epigenetics; epigenomics; plants.

Figure 1

Selection of epigenomics tools. (A and B) Results of the calculation user times for four common tools, Bismark, BSMap, BS-Seeker3 and segemehl. We used data for A. thaliana and chromosome 1A in bread wheat (T. aestivum). n.a, values not available. (C and D) Overlap of detected sites in the two reference genomes for the four mapping tools.

Figure 2

Precision and sensitivity analysis. Precision and sensitivity analysis for the A. thaliana data based on read mapping of simulated reads using the tool by Sherman (https://www.bioinformatics.babraham.ac.uk/projects/sherman)—with the parameters (CG = 24, CH = 8, e = 0.5). (A) There is a large difference in the sensitivity of the four tools. BS-Seeker3 was the least sensitive (sensitivity averaging ~48%)—Bismark was the most sensitive (sensitivity, ~99.9%). The sensitivity values for BSMap and segemehl averaged ~97% and 90%, respectively. (B) For bread wheat (T. aestiuum), BSMap appears to be marginally less precise and less sensitive than segemehl. There is consistency in the precision and sensitivity values for the subgenomes A, B and D in chromosome 1 of T. aestivum. Overall, the results from both (A) and (B) are in agreement. Notably, BS-Seeker3 has a wide range of precision compared to the other three tools. Each data point represents the precision-sensitivity value based on a simulation run for an individual tool. The precision and sensitivity values for Bismark, BSMap, BS-Seeker3 and segemehl averaged ~(99%, 99%), (94%, 82%), (86%, 38%) and (97%, 87%), respectively. Five simulation runs were performed for each tool—one for each of the A. thaliana chromosomes. The elliptical rings around each set of data points represent the confidence bounds.

Figure 3

Memory footprint analysis for the four tools—benchmarked on five genomes. (A) Barplots showing variation in attained memory footprint between the tools benchmarked on different genomes. (B−E) Correlation analysis of genome size and memory footprint analysis. A benchmark of the four tools, (B) BSMap, (C) BS-Seeker3, (D) Bismark and (E) segemehl. The genome sizes are all significantly correlated to the memory footprint analysis (P < 0.05). Dotted line, fitted regression line; Dots, data points.