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Abbasova, L.

Publications and source records attributed to Abbasova, L..

2 recordsLinked to original sources

EpiCompare: R package for the comparison and quality control of epigenomic peak files

SummaryEpiCompare combines a variety of downstream analysis tools to compare, quality control and benchmark different epigenomic datasets. The package requires minimal input from users, can be run with just one line of code and provides all results of the analysis in a single interactive HTML report. EpiCompare thus enables downstream analysis of multiple epigenomic datasets in a simple, effective and user-friendly manner. Availability and ImplementationEpiCompare is available on Bioconductor ([≥] v3.15): https://bioconductor.org/packages/release/bioc/html/EpiCompare.html All source code is publically available via GitHub: https://github.com/neurogenomics/EpiCompare Documentation website https://neurogenomics.github.io/EpiCompare EpiCompare DockerHub repository: https://hub.docker.com/repository/docker/neurogenomicslab/epicompare

bioinformatics↗

CUT&Tag recovers up to half of ENCODE ChIP-seq peaks

Techniques for genome-wide epigenetic profiling have been undergoing accelerated development toward recovery of high-quality data from bulk and single cell samples. DNA-protein interactions have traditionally been profiled via chromatin immunoprecipitation followed by next generation sequencing (ChIP-seq), which has become the gold standard for studying histone modifications or transcription factor binding. Cleavage Under Targets & Tagmentation (CUT&Tag) is a rapidly expanding new technique that enables profiling of such interactions in situ at high sensitivity and is adaptable to single cell applications. However, thorough evaluation and benchmarking against established ChIP-seq datasets are lacking. Here, we comprehensively benchmarked CUT&Tag for H3K27ac and H3K27me3 against published ChIP-seq profiles from ENCODE in K562 cells. Combining multiple new and published CUT&Tag datasets, there was an average recall of 54% known ENCODE peaks for both histone modifications. To optimize data analysis steps, we tested peak callers MACS2 and SEACR and identified optimal peak calling parameters. Considering both precision and recall of known ENCODE peaks, the peak callers were comparable in their performance, although peaks produced by MACS2 match ENCODE peak width distributions more closely. We found that reducing PCR cycles during library preparation lowered duplication rates at the expense of ENCODE peak recovery. Despite the moderate ENCODE peak recovery, peaks identified by CUT&Tag represent the strongest ENCODE peaks and show the same functional and biological enrichments as ChIP-seq peaks identified by ENCODE. Our workflow systematically evaluates the merits of methodological adjustments, providing a benchmarking framework for the experimental design and analysis of CUT&Tag studies, and will facilitate future efforts to apply CUT&Tag in human tissues and single cells.

genomics↗