Stuck in the Peaks? Troubleshooting ATAC-seq, CUT&Tag, CUT&RUN, ChIP-seq, and More

Introduction

Many researchers searching for ATAC-seq analysis help, CUT&Tag troubleshooting, or ChIP-seq QC support find this practical guide useful for solving peak calling problems, replicate mismatch, and quality control challenges.

These days, more and more labs are using chromatin profiling assays - like ATAC-seq, CUT&Tag, CUT&RUN, ChIP-seq, and their advanced forms such as scATAC-seq, scChIP-seq, reChIP, and Co-ChIP - to study gene regulation. These techniques are powerful, but the analysis often becomes the bottleneck.

Even when the sequencing data looks OK, the results may not match expectation. Some researchers find that peaks are missing, or show up in strange locations. Others have trouble with differential analysis, or are not sure how to connect peaks to gene expression meaningfully.

In this article, we summarize several common problems we’ve seen when helping collaborators or clients analyze chromatin accessibility and histone modification data. We group the discussion by type of challenge, not just by assay, to avoid repeating the same advice in different places.

1. Chromatin Accessibility Assays: ATAC-seq and scATAC-seq

ATAC-seq gives a global view of open chromatin regions. Single-cell ATAC-seq (scATAC-seq) further allows cell-type-level resolution. But both share specific technical and analysis pitfalls.

⚠️ Common Issues

(I) Strange fragment size distribution. Good ATAC-seq data shows peaks at ~50 bp (nucleosome-free), ~200 bp, and ~400 bp. If this is missing, maybe over-tagmentation or DNA degradation occurred.

(II) TSS enrichment is low. TSS enrichment score below 6 is a warning. This may reflect poor signal-to-noise or uneven fragmentation. Still, it depends on cell type.

(III) Peak calling is unstable. MACS2 is often used, but it assumes sharp peaks. Genrich and HMMRATAC may give better results for broader regions or clean nucleosome pattern—but can be sensitive to noise.

(IV) Differential analysis does not agree with biology. Some teams use DESeq2 or edgeR on peak counts. But results depend heavily on how peaks were defined, batch effect, and replicate quality.

(V) scATAC-seq has data sparsity. In single-cell ATAC, each cell may only have ~10k fragments. This leads to sparse peak matrix. Clustering and dimensionality reduction need careful tuning.

2. Targeted Enrichment Assays: CUT&Tag, CUT&RUN, ChIP-seq, reChIP, Co-ChIP

These assays enrich for specific chromatin features using antibodies. CUT&Tag and CUT&RUN have lower background than ChIP-seq, but the data is often sparse. ChIP-seq is more established, but more noisy. reChIP and Co-ChIP involve double enrichment, making signal even more fragile.

⚠️ Common Issues

(I) Sparse or uneven signal. Especially in CUT&Tag or CUT&RUN, the read counts may be very low in some regions. It is hard for some peak callers to handle this well.

(II) Peak calling tools give inconsistent results. SEACR is a popular choice for CUT&Tag, but may overcall weak signal. GoPeaks and MACS2 sometimes work better, but need proper tuning.

(III) Broad histone marks confuse analysis. Histone modifications like H3K27me3 show diffuse enrichment. Some tools assume narrow peaks and miss these signals.

(IV) Double IP methods (reChIP, Co-ChIP) suffer from low yield. Signal may be too weak for confident peak calling. Manual validation and IGV checking is necessary.

(V) Replicates show poor agreement. This may happen due to variable antibody efficiency, sample prep, or PCR bias.

3. Single-Cell Chromatin Profiling: scATAC-seq, scCUT&Tag, scChIP-seq

These assays give exciting possibilities, but analysis is much harder than bulk. Most tools adapted from bulk are not suitable without modification.

⚠️ Common Issues

(I) Data sparsity and dropout. Each cell has low read count. Most peaks are zero in most cells. Need to rely on latent space methods like LSI, TF-IDF, or NMF.

(II) Defining peaks is not straightforward. Global peak calling often misses cell-type-specific regions.

(III) Integration with scRNA-seq is hard. Joint embedding needs gene activity matrix or motif scores, which can be noisy. False correlation may arise if not careful.

(IV) Motif enrichment is unstable. Because of data sparsity, motif analysis in single-cell chromatin data is less reliable than in bulk.

4. Common Pitfalls Across All Assays

Closing Thoughts

We have worked with many epigenomic datasets from different labs. Even when the protocol is standard, the data often behaves differently. Each assay - whether ATAC-seq or reChIP—has its own style. Each tool also has its own assumptions.

It is not necessary to use every latest tool. But it is important to understand what each tool expects, and what it may fail to detect. Sometimes, the problem is not the data, but the wrong pipeline for that data.

We hope this summary provides a useful reference, especially for those facing analysis trouble after generating good data. A small adjustment in pipeline or interpretation can make a big difference.