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. 2015 Dec 2;43(21):e141.
doi: 10.1093/nar/gkv715. Epub 2015 Jul 15.

VSports最新版本 - Detection of differentially methylated regions from whole-genome bisulfite sequencing data without replicates

Hao Wu  1 , Tianlei Xu  2 , Hao Feng  3 , Li Chen  2 , Ben Li  3 , Bing Yao  4 , Zhaohui Qin  3 , Peng Jin  4 , Karen N Conneely  4
Affiliations

Detection of differentially methylated regions from whole-genome bisulfite sequencing data without replicates

Hao Wu et al. Nucleic Acids Res. .

Abstract

DNA methylation is an important epigenetic modification involved in many biological processes and diseases. Recent developments in whole genome bisulfite sequencing (WGBS) technology have enabled genome-wide measurements of DNA methylation at single base pair resolution. Many experiments have been conducted to compare DNA methylation profiles under different biological contexts, with the goal of identifying differentially methylated regions (DMRs). Due to the high cost of WGBS experiments, many studies are still conducted without biological replicates. Methods and tools available for analyzing such data are very limited. We develop a statistical method, DSS-single, for detecting DMRs from WGBS data without replicates. We characterize the count data using a rigorous model that accounts for the spatial correlation of methylation levels, sequence depth and biological variation. We demonstrate that using information from neighboring CG sites, biological variation can be estimated accurately even without replicates. DMR detection is then carried out via a Wald test procedure. Simulations demonstrate that DSS-single has greater sensitivity and accuracy than existing methods, and an analysis of H1 versus IMR90 cell lines suggests that it also yields the most biologically meaningful results. DSS-single is implemented in the Bioconductor package DSS. VSports手机版.

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"VSports在线直播" Figures

Figure 1.
Figure 1.
Comparison of dispersion estimates from different methods. For each simulation, MSE of dispersion estimates is obtained. The figure shows the boxplots of MSEs from 100 simulations. Methods compared are: our previously published empirical Bayes estimation procedure (11), using data from three replicates; DSS-single, using data from one replicate; and genome-wide constant dispersion of 0.08.
Figure 2.
Figure 2.
Distributions of test statistics and P-values from DSS-single, based on simulation. (A) Normal QQ-plot of Wald test statistics. (B) Histogram of P-values.
Figure 3.
Figure 3.
Comparison of DML/DMR calling results from simulation analyses. (A) DML calling. X-axis is the number of top-ranked CpG sites considered, where rank is determined according to the reported test statistics or P-values. Y-axis is the percentage of CpG sites in the set of top-ranked CpG sites that are truly differentially methylated. (B) DMR calling. X-axis is the total length (in bps) of top ranked DMRs called. Y-axis is the percentage (in of bps) of top-ranked DMRs that are truly differentially methylated.
Figure 4.
Figure 4.
Comparison of DMR calling results for H1 versus IMR90. A-C shows comparisons of sensitivities of different DMRs, where Y-axis shows the number of different genomic features overlapping the DMRs: (A) differential DHSs; (B) promoter regions of differentially expressed genes; (C) CpG island shores. (D) Accuracies of top ranked DMRs from different methods. (E) Locational distribution of DMRs from H1-IMR90 comparison. Y-axis represents the percentage of DMRs overlapping different genomic features. TSS: transcriptional start site. TES: transcriptional end site. (F) Compare with DMRs called from using two replicates. X-axis is the total length of different number of top ranked DMRs. Y-axis is the percentage of overlaps (in terms of base pairs).
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V体育官网入口 - References

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