Additional file 1: of SMITE: an R/Bioconductor package that identifies network modules by integrating genomic and epigenomic information

Supplementary Methods. Figure S1 Proportions of RNA-seq reads from T. gondii-infected HFFs aligning to a composite hg19/Toxoplasma genome. Figure S2 Comparison of distance weighting effect on gene scores. Figure S3 Representation of simulations demonstrating the effects on high scoring genes of variation of weightings. Figure S4 Comparison of gene scores with reduced and full SMITE models. Figure S5 Examples of modules generated by full and reduced SMITE models. Figure S6 KS test results comparing SMITE and FEM module genes and a random sampling of 10,000 genes. Figure S7 Comparison of the performance of the full SMITE model with the FEM model. Table S1 Criteria for defining genomic contexts in HFFs. Table S2 Weighting criteria used for SMITE analysis of the T. gondii HFF dataset. Appendix 1 R code for analyzing T. gondii HFF dataset with SMITE. Appendix 2 R code for analyzing T. gondii HFF dataset with FEM. Supplementary references (PDF 5642 kb

Additional file 2: of SMITE: an R/Bioconductor package that identifies network modules by integrating genomic and epigenomic information

Supplementary Tables. Table S3 Gene symbol and score of the high scoring genes using three different methods: SMITE full model, SMITE reduced model, and FEM. Table S4 Modules discovered using FEM and genes composing the modules with their DNA methylation, expression, and overall statistics. Table S5 Modules discovered using the reduced model of SMITE (SMITE-R) with spin-glass. Table S6 Modules discovered using the full model of SMITE (SMITE-F) with spin-glass. Table S7 Pathways associated with the genes composing the modules discovered by FEM. Table S8 Pathways associated with the genes composing the modules discovered by the reduced model of SMITE (SMITE-R) using spin-glass. Table S9 Pathways associated with the genes composing the modules discovered by the full model of SMITE (SMITE-F) using spin-glass. Table S10 Quantifying the number of times pathways were found to be associated the modules discovered by either FEM, the reduced model of SMITE (SMITE-R) using spin-glass, or the full model of SMITE(SMITE-F) using spin-glass. Table S11 Genes composing the “summary network” found by either the reduced (SMITE-R) or full (SMITE-F) SMITE models using the Heinz algorithm. Table S12 Pathways associated with the genes composing the “summary network” discovered by the reduced model of SMITE(SMITE-R) using the Heinz algorithm. Table S13 Pathways associated with the genes composing the “summary network” discovered by the full model of SMITE (SMITE-F) using the Heinz algorithm. Table S14 Genes composing the “modules” found using no weights instead of weighting by distance. Table S15 Pathways associated with the genes in the modules identified without using distance weighting. (XLSX 269 kb

Additional file 3: Table S1. of Amnion as a surrogate tissue reporter of the effects of maternal preeclampsia on the fetus

Lists of p values of principal component analysis with known covariates. Table S2. A list of differentially expressed genes. Table S3. Lists of differentially methylated HpaII sites in each model. Table S4. A list of differentially methylated HpaII sites with previously reported Infinium HumanMethylation450 BeadChip differentially methylated probes. Table S5. A list of variably methylated HpaIIs. Table S6. A list of var-HpaII sites harboring genes. (XLSX 374 kb

Additional file 7: of Amnion as a surrogate tissue reporter of the effects of maternal preeclampsia on the fetus

Codes used in this study. (TXT 66.7 kb

Additional file 6: Figure S4. of Amnion as a surrogate tissue reporter of the effects of maternal preeclampsia on the fetus

Comparison of the DNA methylation distribution of variable HpaII sites. The distributions of the DNA methylation levels of variable methylation sites in severe PE-exposed (PE_S, green) (proteinuria grade ≥3 and systolic blood pressure ≥160 mmHg), less severe PE-exposed (PE_M, orange) (proteinuria grade ≤1 and systolic blood pressure ≥ 140 mmHg) and control (blue) were summarized graphically in violin plots. (PDF 895 kb

Additional file 2: Figure S1. of Amnion as a surrogate tissue reporter of the effects of maternal preeclampsia on the fetus

Cell type-specific DNA methylation. A) Bisulfite sequencing results of Hpa_1553647. Each row represents the sequence result from bisulfite PCR products. We show four amnion, four amniotic epithelial cell, and amniotic stromal cell results. The amniotic epithelial and stromal cells were isolated from four individuals. The blue arrow indicates the Hpa_1553647 position in the sequencing results. B) Bisulfite MassArray results of Hpa_210409 and Hpa_621984. The y-axis shows the % DNA methylation in amniotic epithelial and amniotic stromal cells (from four individuals). The p values were calculated by a t test. The error bars indicate the standard deviations. (PDF 317 kb

MOESM2 of RNA:DNA hybrids in the human genome have distinctive nucleotide characteristics, chromatin composition, and transcriptional relationships

Additional file 2. Supplemental Tables

Additional file 1: of Selective modulation of local linkages between active transcription and oxidative demethylation activity shapes cardiomyocyte-specific gene-body epigenetic status in mice

Figure S1. K-means 12 clustering summary. Figure S2. Strict gene length restriction in constitutive genes. Figure S3. Comparison of gene length and promoter features observed among the 12 cluster populations. Figure S4. Cell-type-specific gene body DNA hypomethylation in cardiomyocytes (validation of the HELP tagging method). Figure S5. Comparison gene body and promoter DNA methylation patterns. Figure S6. Validation of 5hmC enrichment by BGT-qPCR assay. Figure S7. Dynamic CTCF binding sites in promoter and gene body regions