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. 2016 Jan 8;44(1):106-16.
doi: 10.1093/nar/gkv1461. Epub 2015 Dec 15.

MethylAction: Detecting Differentially Methylated Regions That Distinguish Biological Subtypes

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Free PMC article

MethylAction: Detecting Differentially Methylated Regions That Distinguish Biological Subtypes

Jeffrey M Bhasin et al. Nucleic Acids Res. .
Free PMC article

Abstract

DNA methylation differences capture substantial information about the molecular and gene-regulatory states among biological subtypes. Enrichment-based next generation sequencing methods such as MBD-isolated genome sequencing (MiGS) and MeDIP-seq are appealing for studying DNA methylation genome-wide in order to distinguish between biological subtypes. However, current analytic tools do not provide optimal features for analyzing three-group or larger study designs. MethylAction addresses this need by detecting all possible patterns of statistically significant hyper- and hypo- methylation in comparisons involving any number of groups. Crucially, significance is established at the level of differentially methylated regions (DMRs), and bootstrapping determines false discovery rates (FDRs) associated with each pattern. We demonstrate this functionality in a four-group comparison among benign prostate and three clinical subtypes of prostate cancer and show that the bootstrap FDRs are highly useful in selecting the most robust patterns of DMRs. Compared to existing tools that are limited to two-group comparisons, MethylAction detects more DMRs with strong differential methylation measurements confirmed by whole genome bisulfite sequencing and offers a better balance between precision and recall in cross-cohort comparisons. MethylAction is available as an R package at http://jeffbhasin.github.io/methylaction.

Figures

Figure 1.
Figure 1.
Stages and component steps in MethylAction. Note that the window size, P-value cutoffs, and ‘frequent’ fraction are user-adjustable.
Figure 2.
Figure 2.
MethylAction detects differentially methylated regions (DMRs) that distinguish among benign prostatic tissue and three clinically relevant subgroups of prostate cancer. (A) Number of DMRs detected for all possible patterns of hyper- (black squares) and hypomethylation (white squares). The table is sorted by false discovery rates (FDRs) that are the result of 2500 bootstraps. Patterns with FDR < 10% are indicated with an asterisk. ‘Frequent’ DMRs require the methylation status of two thirds or more of the samples in a group to agree. (B) Heatmap of read count distributions for all ‘frequent’ DMRs detected, ordered by pattern as in (A). Patterns with FDR < 10% are indicated with numerals corresponding to those indicated in (A). Columns represent samples, and rows represent DMRs. Normalized read counts have been divided by the window size and square root-transformed for visualization purposes. (C) Example hypermethylation DMR that is shared between high grade and African low grade. The x-axis represents genomic coordinates, and the y-axis represents normalized read counts. The read counts are plotted as the mean ± standard error for 50 bp non-overlapping windows. The region of the DMR called by MethylAction is indicated by the box under the x-axis. (D) Example hypermethylation DMR that is specific to high grade. (E) Example hypermethylation DMR that is shared by European low grade, African low grade and high grade.
Figure 3.
Figure 3.
Comparison among DMRs detected by MethylAction, MEDIPS and diffReps between MeDIP-seq data for skin fibroblasts and skin keratinocytes. (A) Heatmap of read count distributions for all ‘frequent’ DMRs detected by MethylAction. Columns represent samples, and rows represent DMRs. Normalized read counts have been divided by the number of windows in the DMR and square root-transformed for visualization purposes. (B) Venn diagram of the number of outputted differential regions unique to each or in common among all of the three tools. The DMR sets from all three analysis results were reduced into a set of consensus regions to enable the comparison. Both the ‘frequent’ and the ‘other’ DMRs from MethylAction were used. (C) Boxplots showing the distribution of the difference in percent methylation as measured by whole genome bisulfite sequencing (WGBS) in one of the skin donors for shared DMRs and DMRs unique to each tool. Differences were computed as % methylation in keratinoctyes minus % methylation in fibroblasts. (D) Distributions of log2 fold changes in MeDIP-seq reads between fibroblasts and keratinocytes for consensus regions shared by all three tools or unique to each tool. (E) Comparison of total time elapsed (wall time) for a complete run of each tool. Values shown are the mean±SEM of four separate program executions. (F) Peak RAM usage (the maximum RAM usage over the course of program execution when sampled in 1 second increments) for each tool shown as mean±SEM of four separate program executions.
Figure 4.
Figure 4.
Precision and recall calculations for hypermethylation detection for each tool when comparing DMRs from enrichment sequencing cohorts to data from The Cancer Genome Atlas (TCGA). (A) Precision (fraction of ‘true’ hypermethylation events out of all hypermethylation called by the tool, where ‘true’ indicates agreement with TCGA) and recall (fraction of ‘true’ hypermethylation events out of all hypermethylation events called in the TCGA data) fractions for each tool between a prostate cancer MiGS cohort and the TCGA PRAD cohort. (B) Precision and recall fractions for each tool between a colon cancer MAP-seq cohort (40) and the TCGA COAD cohort.

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