Additional file 2: of Sparse conserved under-methylated CpGs are associated with high-order chromatin structure

A.docx file containing Tables S1–S4. (DOCX 282 kb

Additional file 3: Table S5. of Sparse conserved under-methylated CpGs are associated with high-order chromatin structure

is an.xls file containing all datasets (31 WGBS, 16 ChIP-seq, two RNA-seq, and two ChIA-PET) used in this study. (XLS 36 kb

Width Based Quantitation of Chromatographic Peaks: Principles and Principal Characteristics

Height- and area-based quantitation reduce two-dimensional data to a single value. For a calibration set, there is a single height- or area-based quantitation equation. High-speed high-resolution data acquisition now permits rapid measurement of the width of a peak (<i>W</i>_<i>h</i>), at any height <i>h</i> (a fixed height, not a fixed fraction of the peak maximum) leading to any number of calibration curves. We propose a width-based quantitation (WBQ) paradigm complementing height or area based approaches. When the analyte response across the measurement range is not strictly linear, WBQ can offer superior overall performance (lower root-mean-square relative error over the entire range) compared to area- or height-based linear regression methods, rivaling weighted linear regression, provided that response is uniform near the height used for width measurement. To express concentration as an explicit function of width, chromatographic peaks are modeled as two different independent generalized Gaussian distribution functions, representing, respectively, the leading/trailing halves of the peak. The simple generalized equation can be expressed as <i>W</i>_<i>h</i> = <i>p</i>(ln <i>h̅</i>)^<i>q</i>, where <i>h̅</i> is <i>h</i>_max/<i>h</i>, <i>h</i>_max being the peak amplitude, and <i>p</i> and <i>q</i> being constants. This fits actual chromatographic peaks well, allowing explicit expressions for <i>W</i>_<i>h</i>. We consider the optimum height for quantitation. The width-concentration relationship is given as ln <i>C</i> = <i>aW</i>_<i>h</i>^<i>n</i> + <i>b</i>, where <i>a</i>, <i>b</i>, and <i>n</i> are constants. WBQ ultimately performs quantitation by projecting <i>h</i>_max from the width, provided that width is measured at a fixed height in the linear response domain. A companion paper discusses several other utilitarian attributes of width measurement

Additional file 1 of Partial erosion on under-methylated regions and chromatin reprogramming contribute to oncogene activation in IDH mutant gliomas

Additional file 1. Fig. S1: The refUMRs provide a better definition of hypermethylated regions in IDH mutant gliomas. Fig. S2: Partial hyper possess distinct features compared with flanking UMRs. Fig. S3: Chromatin features of fhUMRs and phUMRs. Fig. S4: GO and KEGG functional annotation of phUMR and fhUMR related genes. Fig. S5: Transcriptional tendency of phUMR and fhUMR genes in DKFZ. Fig. S6: Cell type enrichment analysis of phUMRs related genes. Fig. S7: Heatmaps of methylation signals at up- and down-regulated phUMR/fhUMR genes. Fig. S8: Histone modification changes in differentially expressed genes within phUMR or fhUMR. Fig. S9: Local histone modification changes on partial Hyper and flanking UMR in phUMRs. Fig. S10: Partial methylation erosion on the promoter of glioma related genes. Fig. S11: Local changes of average histone modification signals at partial Hyper, flanking UMR and downstream regions of up-regulated oncogenes

Additional file 3 of Partial erosion on under-methylated regions and chromatin reprogramming contribute to oncogene activation in IDH mutant gliomas

Additional file 3. Table S2: The genomic context of phUMRs and fhUMRs

Additional file 2 of Partial erosion on under-methylated regions and chromatin reprogramming contribute to oncogene activation in IDH mutant gliomas

Additional file 2. Table S1: Sample information of WGBS data

Additional file 2: Table S1. of DNA epigenome editing using CRISPR-Cas SunTag-directed DNMT3A

List of primers used in this study. (XLSX 17 kb

DNA Methylation Patterns Can Estimate Nonequivalent Outcomes of Breast Cancer with the Same Receptor Subtypes

<div><p>Breast cancer has various molecular subtypes and displays high heterogeneity. Aberrant DNA methylation is involved in tumor origin, development and progression. Moreover, distinct DNA methylation patterns are associated with specific breast cancer subtypes. We explored DNA methylation patterns in association with gene expression to assess their impact on the prognosis of breast cancer based on Infinium 450K arrays (training set) from The Cancer Genome Atlas (TCGA). The DNA methylation patterns of 12 featured genes that had a high correlation with gene expression were identified through univariate and multivariable Cox proportional hazards models and used to define the methylation risk score (MRS). An improved ability to distinguish the power of the DNA methylation pattern from the 12 featured genes (p = 0.00103) was observed compared with the average methylation levels (p = 0.956) or gene expression (p = 0.909). Furthermore, MRS provided a good prognostic value for breast cancers even when the patients had the same receptor status. We found that ER-, PR- or Her2- samples with high-MRS had the worst 5-year survival rate and overall survival time. An independent test set including 28 patients with death as an outcome was used to test the validity of the MRS of the 12 featured genes; this analysis obtained a prognostic value equivalent to the training set. The predict power was validated through two independent datasets from the GEO database. The DNA methylation pattern is a powerful predictor of breast cancer survival, and can predict outcomes of the same breast cancer molecular subtypes.</p></div

The Identification of Specific Methylation Patterns across Different Cancers

<div><p>Abnormal DNA methylation is known as playing an important role in the tumorgenesis. It is helpful for distinguishing the specificity of diagnosis and therapeutic targets for cancers based on characteristics of DNA methylation patterns across cancers. High throughput DNA methylation analysis provides the possibility to comprehensively filter the epigenetics diversity across various cancers. We integrated whole-genome methylation data detected in 798 samples from seven cancers. The hierarchical clustering revealed the existence of cancer-specific methylation pattern. Then we identified 331 differentially methylated genes across these cancers, most of which (266) were specifically differential methylation in unique cancer. A DNA methylation correlation network (DMCN) was built based on the methylation correlation between these genes. It was shown the hubs in the DMCN were inclined to cancer-specific genes in seven cancers. Further survival analysis using the part of genes in the DMCN revealed high-risk group and low-risk group were distinguished by seven biomarkers (<i>PCDHB15, WBSCR17, IGF1, GYPC, CYGB, ACTG2</i>, and <i>PRRT1</i>) in breast cancer and eight biomarkers (<i>ZBTB32, OR51B4, CCL8, TMEFF2, SALL3, GPSM1, MAGEA8</i>, and <i>SALL1</i>) in colon cancer, respectively. At last, a protein-protein interaction network was introduced to verify the biological function of differentially methylated genes. It was shown that <i>MAP3K14, PTN, ACVR1</i> and <i>HCK</i> sharing different DNA methylation and gene expression across cancers were relatively high degree distribution in PPI network. The study suggested that not only the identified cancer-specific genes provided reference for individual treatment but also the relationship across cancers could be explained by differential DNA methylation.</p></div

Additional file 3: Table S2. of DNA epigenome editing using CRISPR-Cas SunTag-directed DNMT3A

Differentially methylated regions identified in RRBS. (XLSX 630 kb