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A bioinformatic pipeline for analysis of M.EcoGII methylation footprint PacBio long-read sequence data

ABSTRACT: Recent studies have combined DNA methyltransferase footprinting of genomic DNA in nuclei with long-read sequencing, resulting in detailed chromatin maps for multi-kilobase stretches of genomic DNA from one cell. Nucleosome footprints and nucleosome-depleted regions can be identified, yielding unprecedented information concerning their degree of correlation within the same cell. The enzyme of choice is M.EcoGII, which methylates adenines in any sequence context, potentially resulting in very high resolution. However, in practice, the methylation efficiency (defined as the fraction of each genomic adenine that is methylated), is quite low, resulting in false footprints predicted by random unmethylated gaps between methylated adenines. We report PacBio long-read sequence data for budding yeast nuclei treated with M.EcoGII. We present a bioinformatic pipeline which accounts for methylation efficiency, as well as an observed bias against methylation as a function of increasing AT content. It also accounts for our observation that some methylation occurs within nucleosomes, breaking up their footprints. Comparison of long reads for each gene indicates the extent of chromatin heterogeneity within the cell population. Although the population average is consistent with that derived using other techniques, we observe a wide range of heterogeneity in nucleosome positions at the single-molecule level.

ORGANISM(S): Escherichia coli Saccharomyces cerevisiae

PROVIDER: GSE243114 | GEO | 2024/03/15

REPOSITORIES: GEO

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Project description:Probing epigenetic features on long molecules of DNA has tremendous potential to advance our understanding of the phased epigenome. In this study, we evaluate CpG methylation and chromatin accessibility simultaneously on long strands of DNA using GpC methyltransferase to exogenously label open chromatin, coupled with nanopore sequencing technology. We performed nanopore sequencing of Nucleosome Occupancy and Methylome (nanoNOMe) on four human cell lines (GM12878, MCF-10A, MCF-7, MDA-MB-231), and demonstrate the ability to directly measure methylation and chromatin accessibility in genomic features such as structural variations and repetitive elements. The long single-molecule resolution allows footprinting of protein and nucleosome binding and determining the combinatorial promoter epigenetic state on individual molecules. Long-read sequencing makes it possible to robustly assign reads to haplotypes, enabling allele-specific epigenetic analysis across the genome. We use existing SNV data on GM12878 to present the first fully phased human Probing epigenetic features on long molecules of DNA has tremendous potential to advance our understanding of the phased epigenome. We evaluate CpG methylation and chromatin accessibility simultaneously on long strands of DNA using GpC methyltransferase to exogenously label open chromatin, coupled with nanopore sequencing technology. We performed nanopore sequencing of Nucleosome Occupancy and Methylome (nanoNOMe) on four human cell lines (GM12878, MCF-10A, MCF-7, MDA-MB-231), and demonstrate the ability to directly measure methylation and chromatin accessibility in genomic features such as structural variations and repetitive elements. The long single-molecule resolution allows footprinting of protein and nucleosome binding and determining the combinatorial promoter epigenetic state on individual molecules. Long-read sequencing makes it possible to robustly assign reads to haplotypes, enabling allele-specific epigenetic analysis across the genome. We use existing SNV data on GM12878 to present the first fully phased human epigenome, consisting of chromosome-level allele-specific profiles of CpG methylation and chromatin accessibility.mosome-level allele-specific profiles of CpG methylation and chromatin accessibility.

Project description:We determined genome-wide nucleosome occupancy in mouse embryonic stem cells and their neural progenitor and embryonic fibroblast counterparts to assess features associated with nucleosome positioning during lineage commitment. Cell type and protein specific binding preferences of transcription factors to sites with either low (e.g. Myc, Klf4, Zfx) or high (e.g. Nanog, Oct4 and Sox2) nucleosome occupancy as well as complex patterns for CTCF were identified. Nucleosome depleted regions around transcription start and termination sites were broad and more pronounced for active genes, with distinct patterns for promoters classified according to their CpG-content or histone methylation marks. Throughout the genome nucleosome occupancy was dependent on the presence of certain histone methylation or acetylation modifications. In addition, the average nucleosome-repeat length increased during differentiation by 5-7 base pairs, with local variations for specific genomic regions. Our results reveal regulatory mechanisms of cell differentiation acting through nucleosome repositioning. The Total RNA from ESCs, NPCs and MEFs was extracted by guanidinisothiocyanat/phenol extraction with the Trifast kit (Peqlab). Total RNA preparations were treated with DNase I, phenol/chloroform extracted and precipitated before further processing. RNAs were depleted of 5S, 5.8S, 18S and 28S rRNAs using the Human/Mouse/Rat Ribo-Zero rRNA Removal Kit (Epicentre) according to the manufacturer’s protocol. After rRNA depletion, RNAs were fragmented with a kit from Ambion. Libraries for Solexa sequencing were generated according to the standard Illumina protocol that comprised first strand cDNA synthesis, second strand cDNA synthesis, end repair, addition of a single A base, and adapter ligation. Sequencing was performed on the Illumina GAIIx (replicate 1) and Illumina HiSeq 2000 (replicate 2) platforms at the sequencing core facilities of the BioQuant in Heidelberg, Germany. RNA reads were aligned with TopHat. Further expression analysis was with the Genomatix software suite (Genomatix, Munich, Germany) and the Eldorado gene annotation. For each transcript a normalized expression value was calculated from the read distribution that accounts for the length differences using the program DEseq for the analysis of differential expression.

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A bioinformatic pipeline for analysis of M.EcoGII methylation footprint PacBio long-read sequence data

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