We analyzed the whole-genome sequences of 212 gastric adenocarcinoma tumors and matched normal samples collated from four different sources (Supplementary Data 1; Methods). All samples were uniformly processed using an accurate somatic mutation calling pipeline (Supplementary Fig. 1a–b). Briefly, we trained a random forest classifier that predicts high confidence somatic mutation calls (SNVs and indels) by combining the outputs of four independent mutation callers. This approach achieved >85% accuracy on an independent test data set of curated somatic mutations26. We excluded 20 low-quality samples with less than 400 mutation calls from the discovery cohort (Supplementary Fig. 1c). In addition, we removed five samples with strong enrichment of C>A substitutions, a sign of oxidative damage during DNA preparation27,28 (Supplementary Fig. 1c). Somatic mutations in coding (CDS) regions, immunoglobin loci, and poorly mappable regions were also removed from further analyses. After uniform processing, samples from the four cohorts showed comparable distributions of somatic mutation counts and similar mutation spectra (Fig. 1a and Supplementary Fig. 1a). The ICGC cohort had slightly fewer mutations per tumor, probably due to the lower sequencing depth of this cohort.Fig. 1Summary of the data. a Gastric tumor samples grouped by cohort and ordered by SNV count within each cohort. The panels show coverage, SNV count, indel count, mutation spectrum, molecular subtype, and Lauren’s classification of each sample. b Correlations between epigenetic features and somatic mutation rates in different tumor subtypes (error bars represent s.e.m of the correlation coefficient). c Principle component analysis of contributions of epigenetic features to the variance in the mutation rate of individual tumors. Colored stacked bars show the contribution of individual epigenetic features to the first two principal components