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Additional file 1: Figure S1a. Hi-C million read counts (left) and read percentage (right) per cell line. Figure S2a. Venn diagram showing overlap of differentially expressed genes (abs(L2FC) > 0.32, p-value < 0.05) between three B-ALL cell lines upon knockdown, decreased and increased genes (left and right respectively). b. Volcano plots demonstrating differentially expressed genes (abs(L2FC) > 0.32, p-value < 0.05) highlighted by compartment switch or shift (A to more A, B to A, B to less B, or stable). c. Scatterplot demonstrating compartments colored by concordance score (percentage of genes with that compartment switch or shift that change in the same direction). Figure S3a. Rate of concordance calculations represent the amount of upregulated and downregulated genes (abs(L2FC) > 0.32, p-value < 0.05) that can be explained by compartment switches and shifts. Figure S4a. Barplot showing number of subcompartment calls with SNIPER for each cell line at NSD2 Low and NSD2 High. b. Barplots presenting fraction of Cscore compartment calls (A or B) overlapping SNIPER subcompartment calls (A1, A2, B1, B2, and B3) c. Barplot showing number of subcompartment calls with SNIPER for each cell line at NSD2 Low and NSD2 High excluding B1 subcompartment calls. d. Barplot presenting number of subcompartment switches from A to B and from B to A from NS2D Low to NSD2 High in each of the cell lines. e. Barplots presenting fraction of SNIPER subcompartment calls overlapping cscore compartment switches and shifts. Figure S5a. Correlation boxplots of SNIPER subcompartments and gene expression changes (abs(L2FC) > 0.32, p-value 0.05) for each cell line from NSD2 Low to NSD2 High. b. Volcano plots demonstrate differentially expressed genes (abs(L2FC) > 0.32, p-value < 0.05) highlighted by subcompartment switches and shifts. Figure S6a. PCA of ATAC-seq peaks for each cell line identifies three distinct clusters which are cell line-specific. b. Heat map representation of ATAC-seq results generated with DiffBind. c. Venn Diagram showing overlap of differentially accessible regions between three B-ALL cell lines decreased and increased accessibility (left and right respectively; (FDR < 0.01, abs(log2(fold change)) > 1). d. Example of B to A compartment shift shared by all three cell lines at the NEO1 locus showing concordance with gene expression and chromatin accessibility. e. Example of B to A compartment shift specific to RS4;11 cell line at the PRDM8 locus showing concordance with gene expression and chromatin accessibility. Figure S7a. Heatmap representation of mean intra-TAD activity per cell line. b. Bar plot showing number of TAD activity changes per cell line (abs(L2FC) > 0.25, FDR < 0.01). c. Venn diagrams demonstrating significant overlap of patient NSD2-High upregulated genes and cell line NSD2-High upregulated genes and insignificant overlap of patient NSD2-High downregulated genes and cell line NSD2-High upregulated genes (one-tailed Fisher’s exact test). The last row presents insignificant overlap of genes upregulated in 9 matched D/R patient pairs with no NSD2 mutation (NSD2 Low) and cell line NSD2-High upregulated genes with a B to A compartment switch or shift. d. Table presenting percentage of patient NSD2-High upregulated genes overlapping a compartment switch/shift from any of the three cell lines. Figure S8a. Table presents patient information associated with the three matched diagnosis-relapse pairs acquired from COG. Figure S9a. Rate of concordance calculations present the number of genes upregulated in expression, increased in H3K36me2 ChIP-seq peaks, and B to A compartment shifts and switches for the RCH, RS4;11, and SEM cell lines. b. Association bar plots showing fraction of differential H3K36me2 chip-seq peaks (decreased, increased, and stable) that overlap changes in intra-TAD activity (increased, decreased, and stable TADs).

Additional file 1. Supplementary Information.

Additional file 14. Review history.

Additional file1. Supplementary methods.

Additional file2. Supplementary figures.

Additional file 1: Fig. S1. The scatter plots between chronological age and epigenetic age based on four epigenetic clocks. Fig. S2. QQ plots of EAA GWAS based on four epigenetic clocks. Fig. S3. Distribution of intrinsic epigenetic age acceleration (IEAA) across five quintiles of polygenic risk score (PRS) for IEAA. Fig. S4. Molecular mechanism for associations between rs732314, chest/abdominal/pelvic-RT and EAA-Horvath. Table S1. SNPs (except the top one) significantly associated with EAA-Horvath and EAA-Hannum among survivors (SJLIFE1, SJLIFE2) and controls. Table S2. Single nucleotide polymorphisms (SNPs) with significant associations with IEAA-Horvath and EEAA-Hannum among survivors (SJLIFE1, SJLIFE2) and controls. Table S3. Differentially methylated regions (DMR) between the SJLIFE1 data set of survivors and controls overlapping with rs28366133 (+/- 500 kb) in HLA region. Table S4. Multivariable linear regression models for the two top SNPs. Table S5. Estimated SNP heritability of EAA based on each of four clock methods using single-trait LD Score Regression of the GWAS of the SJLIFE1 data set. Table S6. Estimated genetic correlation between EAA-Horvath and other traits using LD Score Regression of the GWAS of the SJLIFE1 data set (P

Additional file 1: Supplementary Figure S1. Minimal sample effect for calculating error rates for flow cells. Supplementary Figure S2. Flow cell-level error rate of all sequencers analyzed in this study. Supplementary Figure S3. Physical location pattern of tile-level error rates across flow cells in an outlier sequencer. Supplementary Figure S4. Prevalence of outlier tiles at flow cell level. Supplementary Figure S5. Positional pattern of problematic tiles. Supplementary Figure S6. Comparison of sequencers by using a common reference DNA library. Supplementary Figure S7. Effect of removing outlier tiles on the overall sequencing error rate. Supplementary Figure S8. Effect of removing outlier tiles evaluated by fold change. Supplementary Figure S9. Benchmarking SequencErr with FastQC. Supplementary Figure S10. Effect of DNA sequencing features on sequencer error rate. Supplementary Figure S11. Comparison of SequencErr with error correction methods. Supplementary Figure S12. Application of SequencErr on non-human dataset (SARS-CoV-2). Supplementary Note 1. Illustration of flowcell architecture. Supplementary Note 2. Manually checking read name information from NCBI SRA. Supplementary Note 3. Manually checking NCBI SRA database for the suitability of public datasets for our analysis. Supplementary Note 4. Flow cell layout of HiSeq. Supplementary Note 5. Flow cell layout of NextSeq. Supplementary Note 6. Flow cell layout of NovaSeq.

Additional file 16: Review history.

Additional file 1: Supplementary Methods. Bisulfite treatment, array hybridization, and scanning for DNA Methylation Profiling. Fig. S1. A flow diagram of the study population. Fig. S2. Q-Q plot showing the distribution of the observed versus expected association p-values. Fig. S3. Manhattan plot showing the treatment-specific association of DNA methylation. Fig. S4. The distributions of whole-array and significant treatment-associated CpG sites. Fig. S5. The distribution of beta-values for CpG mediators by groups of CHCs. Table S1. Pairwise correlations among eleven different treatments. Table S2. CpG hits associated with the specific treatment exposure (P

Supplementary Figures S1-S13. Figure S1. Comparison of mutant allele fraction (MAF) in diluted samples (y-axis) and undiluted cancer cell line (x-axis). Figure S2. Copy-number status of cell line COLO829 and ploidy of the 19 selected substitutions in this work. Figure S3. Quality metrics of sequenced datasets. Figure S4. Heatmap of error profiles across sequencing providers, sequencers, PCR enzymes, replicates, and dilutions. Figure S5. HiSeq error profile under CleanDeepSeq. Figure S6. Context dependency of C>T/G>A errors in HiSeq data under CleanDeepSeq. Figure S7. NovaSeq+Kapa error profile under CleanDeepSeq. Figure S8. Context dependency of C>T/G>A errors in NovaSeq+Kapa dataset under CleanDeepSeq. Figure S9. Context dependency of C>T/G>A errors in NovaSeq+Q5 dataset under CleanDeepSeq. Figure S10. False-postive introduced by â forced callingâ . Figure S11. Error profiles in downsampling of NovaSeq + Q5 dataset. Figure S12. Comparison of standard pileup and CleanDeepSeq by using deepSNV on dilution experiments. Figure S13. Comparison of standard pileup and CleanDeepSeq by using MuTect on dilution experiments. (DOCX 5524 kb)