dna methylation microarrays detected by illumina humanmethylation bead chip  (Illumina Inc)

Journal: Bioinformatics Advances

Article Title: easyEWAS: a flexible and user-friendly R package for epigenome-wide association study

doi: 10.1093/bioadv/vbaf026

Figure Lengend Snippet: The workflow chart of the easyEWAS. Abbreviations: DNAm, DNA methylation; DMP, differentially methylated position; DMR, differentially methylated region; GLM, general linear model; LMM, linear mixed-effects model; CoxPH, Cox proportional hazards model; QQ: quantile–quantile; Boot.CI, bootstrap-derived CI; KEGG, Kyoto Encyclopedia of Genes and Genomes.

Article Snippet: In conclusion, easyEWAS is an R package that can be easily integrated into various DNA methylation microarrays detected by Illumina HumanMethylation Bead Chip, significantly enhancing the accessibility of EWAS.

Techniques: DNA Methylation Assay, Methylation, Derivative Assay

Journal: Bioinformatics Advances

Article Title: easyEWAS: a flexible and user-friendly R package for epigenome-wide association study

doi: 10.1093/bioadv/vbaf026

Figure Lengend Snippet: The function names and their corresponding functionalities within the easyEWAS package.

Article Snippet: In conclusion, easyEWAS is an R package that can be easily integrated into various DNA methylation microarrays detected by Illumina HumanMethylation Bead Chip, significantly enhancing the accessibility of EWAS.

Techniques: DNA Methylation Assay, Methylation, Biomarker Discovery

Journal: Life Science Alliance

Article Title: Aberrant DNA methylation distorts developmental trajectories in atypical teratoid/rhabdoid tumors

doi: 10.26508/lsa.202302088

Figure Lengend Snippet: (A) Illustration of our data analysis and integration approach. The number of samples in each cohort is shown on the left. Data were used to call differentially methylated regions (DMRs) and differentially expressed (DE) genes. Data from the Gene Transcription Regulation Database provided transcription factor DNA binding information. DNA methylation data from pluripotent stem cells and normal samples were used as references and to study normal neural cell differentiation. (B) Tumor types are separated into tumor subgroups (omitted from ) based on DNA methylation, when the 10,000 most variable regions measured in both i450k and RRBS data (see the Materials and Methods section) were used for the tSNE visualization. RRBS samples are positioned adjacent to i450k samples representing the tumor subgroups that matched their clinical diagnosis. (C) Venn diagrams showing the number of DMRs in each comparison. Tumor type–specific DMRs are marked into the intersecting areas. A higher number of DMRs were detected in the RRBS than in the i450k data. For i450k results, DMRs were filtered using DNA methylation data from normal brain samples. (D) For AT/RTs, larger numbers of hypermethylated than hypomethylated regions were detected in all the comparisons in both i450k and RRBS data. The numbers of DMRs and direction of DNA methylation change for each comparison in both datasets. (E) AT/RT subgroups showed the highest DNA methylation among the pooled DMRs when compared to other tumor types and normal control samples (CONTR). Average DNA methylation of the probes hitting each i450k DMR is visualized as tumor subgroup-wise violin plots. (F) k-Means clustering analysis revealed DMR clusters that are specifically hypermethylated in AT/RTs. The median DNA methylation of the DMRs in each cluster was used to summarize the DNA methylation patterns. None of the DMR clusters showed AT/RT subtype–specific DNA methylation patterns, but there were DNA methylation differences between tumor subtypes within both MBs and PLEXs. (G) Several topologically associating domains were influenced by large-scale DNA methylation differences, especially in the AT/RT-MB comparison. Karyoplot visualizes the topologically associating domains that harbor large-scale DNA methylation differences, that is, several DMRs that were predominantly either hyper- or hypomethylated in the comparison (see the Materials and Methods section). Color indicates a comparison in which a difference was observed.

Article Snippet: To study oncogenic epigenetic regulation in AT/RTs, we collected genome-wide DNA methylation Illumina microarray data (i450K) from 497 tumors and unmatched microarray expression data from 110 tumors; 89 normal brain DNA methylation samples were used as controls in i450k-based DNA methylation analysis.

Techniques: Methylation, Binding Assay, DNA Methylation Assay, Cell Differentiation, Biomarker Discovery, Comparison, Control

Journal: Life Science Alliance

Article Title: Aberrant DNA methylation distorts developmental trajectories in atypical teratoid/rhabdoid tumors

doi: 10.26508/lsa.202302088

Figure Lengend Snippet: (A) Most of the AT/RT-specific DMRs were hypermethylated (99% and 79% in i450K and RRBS data, respectively), whereas hypomethylated DMRs were more commonly observed in MBs (85% and 88% in i450K and RRBS data, respectively) and PLEXs (98% and 71% in i450K and RRBS data, respectively). Four-field plots for each tumor type show the number of hypermethylated and hypomethylated regions with respect to two other tumor types. Tumor-specific DMRs have the DNA methylation change in the same direction when compared to two other tumor types (e.g., hypermethylated in AT/RTs when compared to MBs and to PLEXs). (B) Most of the transcription factors and other transcriptional regulators (jointly referred to as TFs) are specifically enriched in AT/RT-hyper, MB-hyper, or PLEX-hypo DMRs and largely linked to neural differentiation, SWI/SNF, and PRC2. Upper part: upset plot showing the number of enriched TFs for regions that are hypermethylated or hypomethylated in an AT/RT-, MB-, and PLEX-specific manner. Some transcriptional regulators were enriched in several tumor-specific DMR groups. Lower part: the number of TFs in manually annotated function-related theme groups is shown for each upset plot column. The color of the heatmap shows the fraction of TFs in each theme (row). (C) Binding sites of neural TFs measured in brain tumors and other neural samples were enriched in AT/RT-hyper DMRs, whereas those measured in pluripotent stem cells were enriched in MB-hyper DMRs. GTRD TF binding data were categorized based on the measured sample type into the listed subsets (at the bottom of the plot), and the enrichment of TF binding sites in all the DMRs with AT/RT- or MB-specific DNA methylation was calculated for each of the GTRD subsets separately. Category “All*” means all the reported TF binding sites, so the full GTRD data. Results for the most relevant TFs are shown after organizing them into the theme groups listed in . The dot is not marked when a given TF or other regulator is not measured in a given GTRD subset. (D) PRC2 subunits rarely co-localize with neural TFs and other regulators in AT/RT-hypermethylated sites. Heatmap visualization of the enrichment P -value (one-sided Fisher’s exact test) for co-localization. All the adjusted P -values of 0.01 or higher are marked in white. Themes for each TF are annotated on the right-hand side of the heatmap. (E, F) In our CUT&RUN sequencing analysis, the DNA binding sites of NEUROD1 in the MB cell line overlapped regions that are hypermethylated in AT/RTs and hypomethylated in MBs (AT/RT versus MB-hyper), whereas no NEUROD1 binding sites in AT/RT samples overlapped with DMRs. Heatmaps showing the NEUROD1 binding sites located in different types of DMRs in i450K (E) and RRBS (F) data across the analyzed cell lines.

Article Snippet: To study oncogenic epigenetic regulation in AT/RTs, we collected genome-wide DNA methylation Illumina microarray data (i450K) from 497 tumors and unmatched microarray expression data from 110 tumors; 89 normal brain DNA methylation samples were used as controls in i450k-based DNA methylation analysis.

Techniques: DNA Methylation Assay, Binding Assay, Sequencing

Journal: Life Science Alliance

Article Title: Aberrant DNA methylation distorts developmental trajectories in atypical teratoid/rhabdoid tumors

doi: 10.26508/lsa.202302088

Figure Lengend Snippet: (A) Genomic annotations for cancer specific DMRs in all tumors for RRBS and i450k data. A higher proportion of MB-hypermethylated DMRs (7.6% and 32% in RRBS and i450k data, respectively) were located in CpG islands, when compared to MB-hypomethylated, AT/RT-hypermethylated, or PLEX-hypomethylated DMRs (1.9–5.9% and 11–12% in RRBS and i450k data, respectively). (B) Binding sites of neural differentiation factors NEUROG2, ASCL1, and PAX7 are more methylated in AT/RTs irrespective of the tumor subtype. Of the TFs linked to histone lysine methylation, EZH2 (involved in histone H3 lysine 27 trimethylation) behaved similarly, but a distinct DNA methylation pattern was observed for MIER1 and EHMT2 (involved in histone H3 lysine 9 methylation) binding sites. Violin plots visualizing the distribution of DNA methylation with selected TF binding sites in all i450k DMRs. The tumor subgroups are presented separately.

Article Snippet: To study oncogenic epigenetic regulation in AT/RTs, we collected genome-wide DNA methylation Illumina microarray data (i450K) from 497 tumors and unmatched microarray expression data from 110 tumors; 89 normal brain DNA methylation samples were used as controls in i450k-based DNA methylation analysis.

Techniques: Binding Assay, Methylation, DNA Methylation Assay

Journal: Life Science Alliance

Article Title: Aberrant DNA methylation distorts developmental trajectories in atypical teratoid/rhabdoid tumors

doi: 10.26508/lsa.202302088

Figure Lengend Snippet: (A) Heatmaps visualizing the expression of TFs associated with tumor type–specific hypomethylated DMRs in RNA-seq and microarray data. (B) Same as in (A) but for hypermethylated DMRs.

Article Snippet: To study oncogenic epigenetic regulation in AT/RTs, we collected genome-wide DNA methylation Illumina microarray data (i450K) from 497 tumors and unmatched microarray expression data from 110 tumors; 89 normal brain DNA methylation samples were used as controls in i450k-based DNA methylation analysis.

Techniques: Expressing, RNA Sequencing, Microarray

Journal: Life Science Alliance

Article Title: Aberrant DNA methylation distorts developmental trajectories in atypical teratoid/rhabdoid tumors

doi: 10.26508/lsa.202302088

Figure Lengend Snippet: (A) Pluripotent stem cells (PSCs), primary adult brain, and primary fetal brain (FB) are separated from tumor samples based on DNA methylation in tSNE visualization, when the 10,000 most variable regions in i450k data were used for visualization. (B) When using the same set of pooled DMRs as in , the median DNA methylation level of PSCs is most similar to AT/RTs. (C) AT/RT-hyper DMRs were mostly AT/RT-unique or PSC-like, whereas MB DMRs were MB-unique or FB-like. Very few MB-hypermethylated DMRs were associated with large-scale differences in DNA methylation. Tumor type–specific DMRs were categorized based on DNA methylation levels in PSC and FB samples. The bar plot on the left shows the number of DMRs in different categories. Annotations show whether DMRs are PSC-like (P), FB-like (F), or unique (different from PSCs and FB) and whether DNA methylation changes during cell differentiation from PSC to FB. The proportion of DMRs in large-scale DNA methylation differences within annotated DMR categories is shown in blue on the right. The number of DMRs is marked in the figure. (D) DMR category–related DNA binding patterns revealed transcriptional regulators (TFs) involved in tumor-unique, normal cell–like, and differentiation-related regulation of DNA methylation. PRC2 subunits were enriched in the AT/RT-unique DMRs, whereas neural TFs were enriched in both AT/RT-unique and PSC-like DMRs with varying enrichment patterns. TF binding site enrichment was calculated separately for each normal cell differentiation–related DMR category (bottom). TFs were organized into the themes listed in . The dot is not marked when a given TF is not measured in a given GTRD category.

Article Snippet: To study oncogenic epigenetic regulation in AT/RTs, we collected genome-wide DNA methylation Illumina microarray data (i450K) from 497 tumors and unmatched microarray expression data from 110 tumors; 89 normal brain DNA methylation samples were used as controls in i450k-based DNA methylation analysis.

Techniques: DNA Methylation Assay, Cell Differentiation, Binding Assay

Journal: Life Science Alliance

Article Title: Aberrant DNA methylation distorts developmental trajectories in atypical teratoid/rhabdoid tumors

doi: 10.26508/lsa.202302088

Figure Lengend Snippet: (A) Tumor type–specific DMRs were categorized based on DNA methylation levels in PSC and FB samples. The bar plot on the left shows the number of DMRs in different categories. Annotations show whether DMRs are PSC-like (P), FB-like (F), or unique (different from PSCs and FB) and whether DNA methylation changes during cell differentiation from PSCs. The proportion of DMRs in large-scale methylation differences within annotated DMR categories is shown in blue on the right. The number of DMRs is marked in the figure. This figure has all the possible groups (compared with ). (B) TF binding site enrichment was calculated separately for each DMR category (bottom). TFs were organized into the themes listed in . The dot is not marked when a given TF is not measured in a given GTRD category. This has all the groups shown in (A).

Article Snippet: To study oncogenic epigenetic regulation in AT/RTs, we collected genome-wide DNA methylation Illumina microarray data (i450K) from 497 tumors and unmatched microarray expression data from 110 tumors; 89 normal brain DNA methylation samples were used as controls in i450k-based DNA methylation analysis.

Techniques: DNA Methylation Assay, Cell Differentiation, Methylation, Binding Assay

Journal: Life Science Alliance

Article Title: Aberrant DNA methylation distorts developmental trajectories in atypical teratoid/rhabdoid tumors

doi: 10.26508/lsa.202302088

Figure Lengend Snippet: (A, B, C, D) Differential DNA methylation (DM) was associated with differential gene expression (DE). Gene expression and DNA methylation patterns were studied in four contexts: differential gene expression alone (A) and DE coupled with DM in the genomic neighborhood (±200 kb from the transcription start site [TSS] within the same topologically associating domain) (B), DE coupled with DM in gene-linked enhancer (C), and DE coupled with DM in the gene promoter (2 kb upstream and 500 bp downstream from the TSS) (D). Venn diagrams show the numbers of genes behaving similarly in both sequencing and array data. Differentially expressed genes associated with differential DNA methylation (B, C, D) are called DM-DE genes. Only cases where the sign of DM change was opposite to DE were included in the figure. (E) DM-DE genes in AT/RT comparisons show generally high DNA methylation among AT/RTs. Sample-wise heatmaps show the levels of DNA methylation (average methylation of variable sites) and gene expression. The rightmost heatmap summarizes in which comparison the DM-DE gene was detected, what was the direction of DNA methylation change (hyper/hypo), and the genomic location of the DMR. (F) Expression patterns of selected DM-DE genes. * P < 0.05, ** P < 0.01, *** P < 0.001. (G) Hypermethylated DMRs in relevant genes, which are hypermethylated and underexpressed in AT/RTs. CXXC5 and TCEA3 are AT/RT-specifically suppressed DM-DE genes, and NEUROG1 , EBF3 , and NEUROD2 are DM-DE genes in the AT/RT-MB comparison. Distal DMRs are connected to the TSS via an arch. Oncoprint indicates which relevant TFs have binding sites in these regions in selected GTRD categories. The color of the DMR indicates whether the DMR is PSC-like and whether it is demethylated during neural cell differentiation. The number in front of the DMR indicates the k-means cluster which DMR belongs to (see ). Gray DMRs were not included in TF binding and DMR cluster analysis as they were not AT/RT-specific.

Article Snippet: To study oncogenic epigenetic regulation in AT/RTs, we collected genome-wide DNA methylation Illumina microarray data (i450K) from 497 tumors and unmatched microarray expression data from 110 tumors; 89 normal brain DNA methylation samples were used as controls in i450k-based DNA methylation analysis.

Techniques: DNA Methylation Assay, Gene Expression, Sequencing, Methylation, Comparison, Expressing, Binding Assay, Cell Differentiation

Journal: Life Science Alliance

Article Title: Aberrant DNA methylation distorts developmental trajectories in atypical teratoid/rhabdoid tumors

doi: 10.26508/lsa.202302088

Figure Lengend Snippet: (A) Expression and methylation heatmaps for DM-DE genes using public microarray data. Expression on the left and methylation on the right. (B) Expression and methylation heatmaps for DM-DE genes using sequencing data (RNA-seq, RRBS). Expression on the left and methylation on the right.

Article Snippet: To study oncogenic epigenetic regulation in AT/RTs, we collected genome-wide DNA methylation Illumina microarray data (i450K) from 497 tumors and unmatched microarray expression data from 110 tumors; 89 normal brain DNA methylation samples were used as controls in i450k-based DNA methylation analysis.

Techniques: Expressing, Methylation, Microarray, Sequencing, RNA Sequencing

Journal: Life Science Alliance

Article Title: Aberrant DNA methylation distorts developmental trajectories in atypical teratoid/rhabdoid tumors

doi: 10.26508/lsa.202302088

Figure Lengend Snippet: (A) Expression and methylation heatmaps for tumor-specific DM-DE genes using microarray data. Expression on the left and methylation on the right. (B) Same as in A but with sequencing data (RNA-seq and RRBS). Expression on the left and methylation on the right.

Article Snippet: To study oncogenic epigenetic regulation in AT/RTs, we collected genome-wide DNA methylation Illumina microarray data (i450K) from 497 tumors and unmatched microarray expression data from 110 tumors; 89 normal brain DNA methylation samples were used as controls in i450k-based DNA methylation analysis.

Techniques: Expressing, Methylation, Microarray, Sequencing, RNA Sequencing

Journal: Life Science Alliance

Article Title: Aberrant DNA methylation distorts developmental trajectories in atypical teratoid/rhabdoid tumors

doi: 10.26508/lsa.202302088

Figure Lengend Snippet: (A) Expression of genes presented in from microarray data (GEO accession GSE42658 ). (B) Expression of AT/RT-unique DMRs with EZH2 binding site target genes from RNA-seq data. (C) NEUROG/NEUROD target genes from microarray data (GEO accession GSE42658 ). (D) NEUROG/NEUROD target genes from RNA-seq data.

Article Snippet: To study oncogenic epigenetic regulation in AT/RTs, we collected genome-wide DNA methylation Illumina microarray data (i450K) from 497 tumors and unmatched microarray expression data from 110 tumors; 89 normal brain DNA methylation samples were used as controls in i450k-based DNA methylation analysis.

Techniques: Expressing, Microarray, Binding Assay, RNA Sequencing

Journal: Nature Communications

Article Title: DNA methylation profiling to determine the primary sites of metastatic cancers using formalin-fixed paraffin-embedded tissues

doi: 10.1038/s41467-023-41015-0

Figure Lengend Snippet: a – d RRBS libraries were generated using the paired FF and FFPE tissues from 19 patients with primary cancer. a Size distributions for the two library types. FFPE-RRBS (red line), FF-RRBS (blue line). b Mean coverage of CpGs within CGIs for the two library types. FFPE-RRBS (red rectangle), FF-RRBS (blue rectangle). c Coverage uniformity of CpGs within CGIs. FFPE-RRBS (red line), FF-RRBS (blue line). d Comparison of the mean correlation of different DNA methylation metrics for paired (yellow rectangle) and unpaired (green rectangle) sample groups ( n = 38). DNA methylation of CGIs was assessed using different methods, including beta value, CHALM, MHL, and PDR, as described in the Methods section. e – h RRBS libraries were constructed using the FFPE tissues from the primary and matched metastatic tissues of cancer patients ( n = 8). e Size distributions for the FFPE-RRBS libraries derived from the primary (blue line) and matched metastatic (red line) tissues. f Mean coverage of the FFPE-RRBS libraries constructed using the primary (blue rectangle) and matched metastatic (red rectangle) tissues. g Coverage uniformity for the RRBS libraries of two tissue groups. Primary tissues (blue line); metastatic tissues (red line). h Comparison of the mean correlation of different DNA methylation metrics for paired (yellow rectangle) and unpaired (green rectangle) sample groups ( n = 16). DNA methylation of CGIs was evaluated using different methods, including beta value, CHALM, MHL, and PDR, as described in the Methods section. In the box plots of b , d , f , and g , the center line, box limits and whiskers represent the median, upper and lower quartiles, and 1.5-fold interquartile range, respectively. Asterisks ∗ and ∗∗∗ indicate a significant difference at P < 0.05 and P < 0.001, respectively, as determined by the two-sided Wilcoxon rank sum test. Source data are available in a supplementary file.

Article Snippet: The DNA methylation microarray (Illumina 450 K) data of the ten most common cancers used in this study are available in the TCGA database.

Techniques: Generated, Comparison, DNA Methylation Assay, Construct, Derivative Assay