Scatter plots illustrating the high correlation of methylation calls.

<p>A) Average methylation in 100 kB windows (n = 28,795) shown for data generated on HiSeq X and HiSeq 2500 systems. B) Methylation at individual CpG sites covered by more than 10 reads (n = 7.5 M) shown for the corresponding data sets.</p

Data quality of whole genome bisulfite sequencing on Illumina platforms - Fig 1

<p>Box plots of the mean per-base Q-scores for R1 (left) and R2 (right) across three HiSeq X RTA versions. The mean, standard deviation (SD), and mean overall difference in Q-scores between RTA 2.7.7 and the two older versions (RTA 2.7.1 and 2.7.5) are listed above the box plots for each base and RTA version.</p

Examples of average base call quality scores for whole genome bisulfite sequencing of libraries prepared from lymphoblastoid cell line NA10860.

<p>Per nucleotide quality scores (average for each sequencing cycle) for read 1 and read 2 separately. A-D) SPLAT libraries. E-H) TSDM libraries. Panels A,B,C and E,F,G show Q-scores obtained with HiSeq X RTA versions, the version numbers are noted in each panel. Panels D and H show corresponding data generated on the HiSeq 2500 platform. Q-boxplots for guanines exclusively in read 2 are plotted in the rightmost panels.</p

Correlation plots showing pairwise comparisons across a shared set of 3 M CpG sites covered by 10 reads or more in each dataset.

<p>A). Heatmap of the Pearson’s correlation coefficients for comparisons across all the library types, sequencing softwares, and cell types used in the study ordered by hierarchical clustering. B) The corresponding root mean square error (RMSE) values for the library/software comparisons in the same order as plotted in panel A.</p

Per library sequencing metrics.

<p>Per library sequencing metrics.</p

Global methylation levels computed from R1 and R2 separately.

<p>Global methylation levels computed from R1 and R2 separately.</p

Variation in global methylation rates depends less on RTA version than on library preparation method.

<p>A) Global methylation levels (average methylation level across the whole genome) for DNA samples NA10860 and REH are shown for the different library preparation methods and are colored according to RTA software. B) Boxplots showing the average methylation in 100 kB windows for the various libraries and RTA versions; the median values are denoted in the panel.</p

The ability of ASE and GTE analysis to detect significantly associated rSNPs at different MAF.

<p>Fractions of rSNPs are shown for different minor allele frequencies (MAF) with significant association signals according to a Bonferroni-corrected p-value of 0.05. Each data point underlying the curves represents the fraction of significant associations within a 1% MAF bin. Sliding 5% MAF window averages are plotted for different sample sizes analyzed by ASE and GTE. Both methods detect a lower fraction of low frequency rSNPs, compared to the fraction of all the SNPs at the same frequency (black line). The ASE method detects a higher fraction of the SNPs (solid lines) with a MAF <15% than GTE (dashed lines) regardless of sample size except for the largest GTE sample set.</p

Overlap of significantly associated rSNPs identified by ASE and GTE.

<p>The percentage of overlapping rSNPs detected by allele-specific expression (ASE) and genotype expression (GTE) analysis is plotted for varying numbers of samples. The top 9536 SNPs from the GTE analysis are compared with the top 38203 SNPs from the ASE analysis, which corresponds to a Bonferroni threshold of p = 0.05 for a GTE sample size of 395 and an ASE sample size of 188. The p-value cut-offs were adapted so that the same SNP top-list sizes were obtained at all sample sizes for both GTE (p-value of 1.17E-7, 1.06E-4, 1.93E-3, 6.12E-3 for n = 395, n = 188, n = 95, and n = 50 respectively) and ASE (p-value of 8.06E-8, 9.35E-5, 4.90E-3 for n = 188, n = 95, and n = 50 respectively). The vertical axes show the percentage of SNPs in the top-lists detected by both GTE and ASE analysis and the horizontal axes show the number of samples analyzed using GTE and ASE, respectively. The percentage overlap is calculated by dividing the number of overlaps with the number of top SNPs in the GTE analysis. In (A), each line shows the effect on the number of overlapping SNPs detected by ASE analysis of a specific sample size when the sample size in GTE analysis was increased. In (B), each line shows the effect on the number of overlapping rSNPs detected by GTE analysis of a specific sample size when the samples size in ASE analysis is increased.</p