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

Choi, Sera; Schilder, Brian M; Abbasova, Leyla; Murphy, Alan E; Skene, Nathan G.

doi:10.1101/2022.07.22.501149

Cited by 1 publication

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We observe that fewer PCR cycles reduced duplication rates, but at the expense of ENCODE recovery and capture. We provide benchmarking pipeline EpiCompare 18 to aid researchers in optimally analyzing and interpreting their CUT&Tag datasets. We hope that our systematic optimizations of CUT&Tag will help to facilitate its more widespread adaptation in the field and expedite its application in understanding the epigenetic causes and consequences of complex diseases.…”

Section: Discussionmentioning

confidence: 99%

“…We characterized the similarities and differences between CUT&Tag and ENCODE based on parameters including read- and peak-level correlation, regulatory element annotation, gene ontology enrichment, and transcription factor binding motif (TFBM) analysis. We provide a benchmarking pipeline EpiCompare 18 to help researchers optimally analyze and interpret CUT&Tag studies.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

CUT&Tag recovers up to half of ENCODE ChIP-seq peaks in modifications of H3K27

Abbasova

Schilder

et al. 2022

Preprint

View full text Add to dashboard Cite

Techniques for genome-wide epigenetic profiling have been undergoing rapid 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 promising new technique, which 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 still lacking. Here we comprehensively benchmarked CUT&Tag for H3K27ac and H3K27me3 against published ChIP-seq profiles from ENCODE in K562 cells. Across a total of 30 new and 6 published CUT&Tag datasets we found that no experiment recovers more than 50% of known ENCODE peaks, regardless of the histone mark. We tested peak callers MACS2 and SEACR, identifying optimal peak calling parameters. Balancing both precision and recall of known ENCODE peaks, SEACR without retention of duplicates showed the best performance. 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 and will facilitate future efforts to apply CUT&Tag in human tissues and single cells.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%