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

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

Differentially methylated regions identified in RRBS. (XLSX 630 kb

Confounding by Repetitive Elements and CpG Islands Does Not Explain the Association between Hypomethylation and Genomic Instability

Confounding by Repetitive Elements and CpG Islands Does Not Explain the Association between Hypomethylation and Genomic Instabilit

Association between methylation deserts and human-specific structural rearrangements.

<p>(A) Locations of human-specific structural rearrangements (black), 100 Kbp windows with methylation index value 0 (violet), 100 Kbp windows with lowest 1% sperm methylation at 15× coverage (green) and 2.5× coverage (red) for three representative chromosomes. (See <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002692#pgen.1002692.s018" target="_blank">Figure S18</a> for a whole genome view). (B) Cumulative sperm methylation distribution and the Kolmogorov-Smirnov statistics for 100 Kbp windows containing rearrangements (solid line) and the rest of the windows (dashed line) at 15× coverage (red) and at 2.5× coverage (red). (C) Simulation test of extent of hypomethylation in the regions flanking human-specific structural rearrangements. Distribution of methylation levels for 10 Kbp regions sampled at increasing distances (from 10 Kbp to 100 Kbp) from the 522 human specific structural rearrangements is compared to the distribution of methylation levels of randomly picked segments with matching sizes within the same chromosome (100 random samplings for each rearrangement). The same analysis is performed for methylomes at 15× coverage (green) and 2.5× coverage (red). <i>D_max</i> and significance <i>p</i>-value were determined using the Kolmogorov-Smirnov test.</p

Structural mutability assessed by structural heterozygosity.

<p>(A) Under the infinite allele model, assuming structural mutations are neutral and at drift-mutation equilibrium, mutation rates are proportional to heterozygosity rates. (B) Comparison of average CNV heterozygosity rates (data from four studies) within (black for methylomes at 15× coverage, gray for methylomes at 2.5× coverage) and outside (white) methylation deserts. Error bars represent standard deviation of CNV heterozygosity rates in corresponding regions.</p

Major patterns of CNVs in relation to LCRs (arrows with same texture indicates paralogous LCRs).

<p>(A) CNVs involving whole regions between DP-LCRs. (B) Scattered CNVs (CNVs covering <40% of the distance between LCRs) between DP-LCRs. (C) CNVs involving whole regions between non-paralogous LCRs. (D) Scattered CNVs between non-paralogous LCRs. (E) Complex patterns of CNVs extending over various LCR groups and intervening regions. (F) CNVs overlapping LCRs. (G–H) Contingency tables summarizing the counts of CNVs observed between LCRs, corresponding to A, B, C and D. The CNVs between paralogous LCRs tend to involve the whole region (as illustrated in A, corresponding to counts in top left cells in G and H), a signature of NAHR involving paralogous LCRs.</p

Enrichment of various regulatory features in methylation deserts detected using permutation test or chi-square test. Enrichments for an expanded set of regulatory features are included in Table S6.

<p>Enrichment of various regulatory features in methylation deserts detected using permutation test or chi-square test. Enrichments for an expanded set of regulatory features are included in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002692#pgen.1002692.s029" target="_blank">Table S6</a>.</p

Predictive power of methylation and other genomic factors for CNV counts.

<p>Predictive power of methylation, CpG island content, and repetitive element content (LINE, SINE, LTR, and Satellites) was measured using Akaike information criterion (AIC). For all five datasets, negative binomial regression was performed using all six factors and all six combinations of five factors (one factor being removed at a time). The <i>y</i>-axis represents the predictive power of a factor, as measured by the improvement of the AIC score based on all six factors relative to the AIC score without the factor. Note that this method measures predictive power of a factor after correction for any potential confounding due to other factors. (The detailed calculations and input data are in Supporting Information.)</p

Statistical risk analysis of structural mutability due to hypomethylation and DP–LCRs.

<p>(A) Venn diagram of 100 Kbp windows classified into one or more of the following three categories: (i) windows containing human-specific structural rearrangements; (ii) windows within methylation deserts (windows with lowest 1% methylation at 2.5× or 15× coverage); and (iii) windows containing regions between DP-LCRs. Numbers within the circle areas indicate fraction (per mil) of the genome occupied by the specific groups of windows. (B) Statistical relative risk (RR) and statistical attributable risk (AR) of structural instability for hypomethylation and DP-LCRs (the first row corresponds to A).</p

Additional file 2: of DNMT3A and TET1 cooperate to regulate promoter epigenetic landscapes in mouse embryonic stem cells

Table S1. List of oligonucleotides: guide sgRNAs and primers. (PDF 52 Kb