Unique and assay specific features of NOMe-, ATAC- and DNase I-seq data

Abstract

Chromatin accessibility maps are important for the functional interpretation of the genome. Here, we systematically analysed assay specific differences between DNase I-seq, ATAC-seq and NOMe-seq in a side by side experimental and bioinformatic setup. We observe that most prominent nucleosome depleted regions (NDRs, e.g. in promoters) are roboustly called by all three or at least two assays. However, we also find a high proportion of assay specific NDRs that are often ‘called’ by only one of the assays. We show evidence that these assay specific NDRs are indeed genuine open chromatin sites and contribute important information for accurate gene expression prediction. While technically ATAC-seq and DNase I-seq provide a superb high NDR calling rate for relatively low sequencing costs in comparison to NOMe-seq, NOMe-seq singles out for its genome-wide coverage allowing to not only detect NDRs but also endogenous DNA methylation and as we show here genome wide segmentation into heterochromatic B domains and local phasing of nucleosomes outside of NDRs. In summary, our comparisons strongly suggest to consider assay specific differences for the experimental design and for generalized and comparative functional interpretations.

Chromatin accessibility determines intron retention in a cell type-specific manner

“…Enrichment for GCH methylated loci might be an indication of euchromatin formation, which is usually decorated with certain histone modifications and characterised by nucleosome depletion and reduced CpG methylation levels (42). High GCH methylation levels in retained introns, that we observed in our analysis, are indicative of NFRs, regions of possible nucleosome eviction that are characterised by a high density of methylated GCH sites and unmethylated CpG sites (43). Interestingly, You et al showed that a loss of nucleosome depleted regions accompanied by nucleosome occupancy precedes changes in endogenous CpG methylation in OCT4 and NANOG genes in embryonic carcinoma cell line NCCIT (44).…”

Widespread effects of DNA methylation and intra-motif dependencies revealed by novel transcription factor binding models

“…(4) A novel reference genome is generated by denoting occurrences of methylated cytosines with the letter M and occurrences of guanines opposite of a methylated cytosine with the letter H. (5) In-vivo transcription factor binding site information are obtained using peak calls from TF -ChIP-seq data. (6) TF binding data is used for motif discovery with LSlim models on the methylation aware reference genomes; (7) resulting in methylation aware TF motif representations.…”

“…Previous and this work have shown that many TFs show sensitivity to the status of CpG methylation. Interestingly, it was also shown that enzymes such as DNase1 and the Tn5 transposase, the two most often used enzymes for the measurement of open-chromatin, show differences in DNA cutting or insertion with respect to CpG methylation (7, 67). Thus in genome-wide analysis of such data, neglecting the status of DNA methylation may be harmful in two ways.…”

“…It was shown that TFs usually bind to accessible chromatin (2) and therefore a variety of computational methods (3) has been developed to combine chromatin accessibility data (e.g. DNase1-seq, ATAC-seq, NOMe-seq) with TF motif information as encoded in Position Weight Matrices (PWMs) (4–7) to elucidate the tissue-specific binding profiles of TFs. Recently, LS lim -models, which capture intra-motif dependencies, have been successfully applied to overcome the nucleotide independence assumption of PWMs (8).…”

Tumor suppressor p53: from engaging DNA to target gene regulation

“…How chromatin accessibility and modification state differ across cell types or after drug treatments and their influence on p53 occupancy requires more investigation. Future experiments utilizing ATAC-seq or NoMe-seq (nucleosome occupancy and methylome sequencing) in these cells may provide more information to identify cell type- and fate-specific features ( 213 , 214 ). Together, these data suggest that p53 recruitment may be affected by ‘availability’ of p53REs defined by tissue-specific regulatory elements expanding the repertoire of genes that can be affected by p53 binding events, as has been shown for p63 ( 12 ).…”