Project description:Track normalization and averaging of H3K4me3 ChIP-seq data across various cell and tissue types from Mouse ENCODE.
| PRJNA298126 | ENA
Project description:Track normalization and averaging of epigenetic features across various cell and tissue types derived from Roadmap Epigenomic datasets
Project description:Data tracks from bisulfite sequencing (BS-seq) experiments were sorted by tissue or cell types and processed using an in-house algorithm that provides normalization functionality followed by generation of a track average. Re-analysis of Roadmap Epigenomics DNA methylation datasets using an in-house algorithm to create an average data track.
Project description:Data tracks from chromatin immunoprecipitation followed by high throughput sequencing (ChIP-seq) experiments were sorted by tissue or cell types and processed using an in-house algorithm that provides normalization functionality followed by generation of a track average. Re-analysis of Roadmap Epigenomics H3K4me3 ChIP-seq datasets using an in-house algorithm to create an average data track.
Project description:Data tracks from bisulfite sequencing (BS-seq) experiments were sorted by tissue or cell types and processed using an in-house algorithm that provides normalization functionality followed by generation of a track average.
Project description:Data tracks from chromatin immunoprecipitation followed by high throughput sequencing (ChIP-seq) experiments were sorted by tissue or cell types and processed using an in-house algorithm that provides normalization functionality followed by generation of a track average.
Project description:Genome wide DNA methylation profiling of 4 human tissues of various ages: 83 samples of normal adjacent resected kidney tissue .The Illumina Infinium 27k Human DNA methylation Beadchip v1.2 was used to obtain DNA methylation profiles across approximately 27,000 CpGs. Sentrix barcode provided for batch normalization
Project description:Long-term culture associated changes need to be considered for quality control of cell preparations – especially in cellular therapy. Here we describe a simple method to track cellular aging based on continuous DNA-methylation changes at six specific CpG sites. This epigenetic signature can be used as a biomarker for various cell types to predict the state of cellular aging with regard to the number of passages or days of in vitro culture. 8 samples of human dermal fibroblasts. 4 samples of mesenchymal stem cells (MSC) from human adipose tissue.
Project description:Illumina-based BeadChip arrays have revolutionized genome-wide DNA methylation profiling, pushing it into diagnostics. However, comprehensive quality assessment remains a challenge within a wide range of available tissue materials and sample preparation methods. This study tackles two critical issues: differentiating between biological effects and technical artefacts in suboptimal quality samples and the impact of the first sample on the Illumina-like normalization algorithm. We introduce three quality control scores based on global DNA methylation distribution (DB-Score), bin distance from copy number variation analysis (BIN-Score), and consistently methylated CpGs (CM-Score), that rely on biological features rather than internal array controls. These scores were explored and benchmarked across independent study cohorts. Additionally, we reveal deviations in beta values caused by different sample rankings with the Illumina-like normalization algorithm, verified these with whole-genome methylation sequencing data and showed effects on differential DNA methylation analysis. Our findings underscore the necessity of consistently utilizing a pre-defined normalization sample within the ranking process to boost the reproducibility of the Illumina-like normalization algorithm. In conclusion, our study delivers valuable insights, practical recommendations, and R functions designed to enhance the reproducibility and quality assurance of DNA methylation analysis, particularly for challenging sample types.
