Project description:Genome-wide mapping of histone modifications is critical to understanding transcriptional regulation. CUT&Tag is a new method for profiling histone modifications, offering improved sensitivity and decreased cost compared with ChIP-seq. Here, we present GoPeaks, a peak calling method specifically designed for histone modification CUT&Tag data. We compare the performance of GoPeaks against commonly used peak calling algorithms to detect histone modifications that display a range of peak profiles and are frequently used in epigenetic studies. We find that GoPeaks robustly detects genome-wide histone modifications and, notably, identifies a substantial number of H3K27ac peaks with improved sensitivity compared to other standard algorithms.
Project description: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.
Project description:We developed scNanoSeq-CUT&Tag, a streamlined method by adapting a modified CUT&Tag protocol to Oxford Nanopore sequencing platform for efficient chromatin modification profiling at single-cell resolution. We firstly tested the performance of scNanoSeq-CUT&Tag on six human cell lines: K562, 293T, GM12878, HG002, H9, HFF1 and adult mouse blood cells, it showed that scNanoSeq-CUT&Tag can accurately distinguish different cell types in vitro and in vivo. Moreover, scNanoSeq-CUT&Tag enables to effectively map the allele-specific epigenomic modifications in the human genome andallows to analyze co-occupancy of histone modifications. Taking advantage of long-read sequencing,scNanoSeq-CUT&Tag can sensitively detect epigenomic state of repetitive elements. In addition, by applying scNanoSeq-CUT&Tag to testicular cells of adult mouse B6D2F1, we demonstrated that scNanoSeq-CUT&Tag maps dynamic epigenetic state changes during mouse spermatogenesis. Finally, we exploited the epigenetic changes of human leukemia cell line K562 during DNA demethylation, it showed that NanoSeq-CUT&Tag can capture H3K27ac signals changes along DNA demethylation. Overall, we prove that scNanoSeq-CUT&Tag is a valuable tool for efficiently probing chromatin state changes within individual cells.
Project description:To understand how Pou4f1 functions in RGC lineage specification and subtype formation, we performed “Cleavage Under Targets & Tagmentation” (CUT&Tag) analysis using a rabbit anti-Pou4f1 antibody and embryonic 16.5 (E16.5) retinal cells to generate barcoded PCR libraries that are enriched for Pou4f1-mediated binding. In parallel, rabbit IgG was used as a negative control for peak calling analysis, and rabbit anti-H3K9ac antibody was used to mark active enhancers and promoters.
Project description:High-resolution methods such as 4C and Capture-C enable the study of chromatin loops such as those formed between promoters and enhancers or CTCF/cohesin binding sites. An important aspect of 4C/CapC analyses is the identification of robust peaks in the data for the identification of chromatin loops. Here we present an R package for the analysis of 4C/CapC data. We generated 4C data for 10 viewpoints in 2 tissues in triplicate to test our methods. We developed a non-parametric peak caller based on rank-products. Sampling analysis shows that not read depth but template quality is the most important determinant of success in 4C experiments. By performing peak calling on single experiments we show that the peak calling results are similar to the replicate experiments, but that false positive rates are significantly reduced by performing replicates.