ABSTRACT: All available datasets of DEEP. There are several Reference Epigenomes available. For metadata check out www.deep.dkfz.de One reference epigenome represents a set of 6 ChIP-Seqs, WGBS, RNA-Seq, smallRNA-Seq and sometimes DNase1 and/or NOME-Seq.
Project description:mRNA, total RNA, small noncoding RNA, NOMe-Seq and DNase-Seq data from following samples (not every Sequencing Type for every sample):
01_HepG2_LiHG_Ct1
41_Hf01_LiHe_Ct
41_Hf02_LiHe_Ct
41_Hf03_LiHe_Ct
51_Hf03_BlCM_Ct
51_Hf04_BlCM_Ct
51_Hf03_BlEM_Ct
51_Hf04_BlEM_Ct
51_Hf03_BlTN_Ct
51_Hf04_BlTN_Ct
Metadata available at deep.dkfz.de
Project description:Reference datasets are often used to compare, interpret or validate experimental data and analytical methods. In the field of gene expression, a dozen reference datasets have been published. Typically, they consist of individual baseline or spike-in experiments carried out in a single laboratory and representing a particular set of conditions. For most organisms, however, few or no such reference datasets are publicly available. Here, we describe a new type of datasets highly representative for the spatial, temporal and response dimensions of gene expression. They result from integrating expression data from a large number of globally normalized and quality controlled public experiments and aggregating results by anatomical parts, stages of development, perturbations, drugs, diseases, neoplasms, and genotypes. The proposed datasets were created for human and several model organisms and are publicly available at www.expressiondata.org.
Project description:Data for paper: Epigenetic dynamics of monocyte to macrophage differentiation with Chip Seq, NOMe, mRNA, total RNA, noncoding RNA, whole genome bisulfite seq,
Project description:Since the introduction of next generation sequencing technologies, the field of epigenomics has evolved rapidly. However, most commonly used assays are enrichment-based methods and thus only semi-quantitative. Nucleosome occupancy and methylome sequencing (NOMe-seq) allows for quantitative inference of chromatin states with single locus resolution, but this requires high sequencing depth and is therefore prohibitively expensive to routinely apply to organisms with large genomes. To overcome this limitation, we introduce guidedNOMe-seq, where we combine NOMe profiling with large scale sgRNA synthesis and Cas9-mediated region-of-interest (ROI) enrichment. To facilitate quantitative comparisons between multiple samples, we additionally develop an R package to standardize differential analysis of NOMe-seq data. We extensively benchmark guidedNOMe-seq in a proof-of-concept study, dissecting the interplay of ChAHP and CTCF on chromatin. In summary we introduce a cost-effective, scalable, and customizable targeted enrichment extension to the existing NOMe-seq protocol allowing genome-scale quantification of nucleosome occupancy and transcription factor binding at single allele resolution.
Project description:Since the introduction of next generation sequencing technologies, the field of epigenomics has evolved rapidly. However, most commonly used assays are enrichment-based methods and thus only semi-quantitative. Nucleosome occupancy and methylome sequencing (NOMe-seq) allows for quantitative inference of chromatin states with single locus resolution, but this requires high sequencing depth and is therefore prohibitively expensive to routinely apply to organisms with large genomes. To overcome this limitation, we introduce guidedNOMe-seq, where we combine NOMe profiling with large scale sgRNA synthesis and Cas9-mediated region-of-interest (ROI) enrichment. To facilitate quantitative comparisons between multiple samples, we additionally develop an R package to standardize differential analysis of NOMe-seq data. We extensively benchmark guidedNOMe-seq in a proof-of-concept study, dissecting the interplay of ChAHP and CTCF on chromatin. In summary we introduce a cost-effective, scalable, and customizable targeted enrichment extension to the existing NOMe-seq protocol allowing genome-scale quantification of nucleosome occupancy and transcription factor binding at single allele resolution.
Project description:Reference datasets are often used to compare, interpret or validate experimental data and analytical methods. In the field of gene expression, a dozen reference datasets have been published. Typically, they consist of individual baseline or spike-in experiments carried out in a single laboratory and representing a particular set of conditions. For most organisms, however, few or no such reference datasets are publicly available. Here, we describe a new type of datasets highly representative for the spatial, temporal and response dimensions of gene expression. They result from integrating expression data from a large number of globally normalized and quality controlled public experiments and aggregating results by anatomical parts, stages of development, perturbations, drugs, diseases, neoplasms, and genotypes. The proposed datasets were created for human and several model organisms and are publicly available at www.expressiondata.org. PMI: 18 Samples were used with 2 different experimental factors: 1. 2 harvesting times (morning/afternoon) 2. 3 pos-harvesting times. 3 biological replicates were used.
