Normalization sounds like a technical detail, but it is one of the biggest sources of analytical error. Many bioinformaticians simply normalize by total read count - the way they do for RNA-seq. But ATAC-seq is different. The number of usable reads in peaks can vary dramatically between samples, and normalizing by total reads often erases the real signal.
We once worked on a project where group A had higher chromatin accessibility than group B. But because group A also had higher background, total read count normalization made it appear less accessible. The fold changes were reversed, and the team spent weeks chasing an artifact.
Instead, we normalize based on stable genomic regions - such as promoters of housekeeping genes. If available, we use spike-in controls. Sometimes we try multiple methods and compare the results against known biology. Normalization is not just a math operation - it defines what signal survives, and what gets flattened.
Unexpected separation in PCA or loss of signal? We troubleshoot ATAC-seq batch effects and normalization choices to preserve biological interpretability without overcorrection. Request a free consultation →
Motif analysis often gives people false confidence. You run a few enrichment tools, get a list of transcription factors, and feel like you’ve uncovered the regulatory logic. But in practice, these motifs are often generic - AP-1, CTCF, SP1 - the usual suspects that appear in every dataset.
One project we reviewed claimed strong enrichment of a known TF. But that TF’s expression never changed, and its binding had no known connection to the tissue. It was just a background motif - not a regulatory driver. Yet the paper devoted an entire section to it.
We now approach motif analysis with more caution. We restrict to differential peaks, match background GC content carefully, and integrate with RNA-seq or ChIP-seq data. And most importantly, we do not overinterpret. It is better to report no convincing motif than to fabricate a story from noise.
Batch effects can look exactly like biology. If you process all treatment samples on one day and all control samples on another, any difference you see may be driven by that batch. We have seen PCA plots that show perfect separation - and the team gets excited - until we overlay the batch information and realize it's a technical artifact.
In one severe case, normalization removed most biological variation because it was confounded with sequencing batch. The final results looked flat, and the authors had no explanation. The reviewer asked tough questions - and the team had no answers.
What we do is plan ahead. We randomize samples across batches when possible. We visualize batch versus condition effects in PCA and clustering. If the effects are confounded, we interpret with caution. Statistical correction is helpful - but it cannot fix poor experimental design.
Need an extra set of expert eyes on your ATAC-seq project? Whether it’s design review, analysis audit, or figure validation, we help research teams avoid hidden traps and defend their findings. Request a free consultation →
After all the processing, you get a list of peaks. Now you want to assign meaning - usually by linking peaks to genes. Most tools assign the nearest gene, and teams build their functional analysis from there. But this shortcut can be very misleading.
Enhancers don’t always regulate the nearest gene. In fact, they often skip over nearby genes to target more distant ones. We’ve seen network analyses that assign regulatory function to irrelevant genes simply because of proximity. Reviewers are getting smarter - they question these assumptions, and they’re right to do so.
In our practice, we use enhancer–promoter maps when available - from Hi-C, ABC models, or known regulatory atlases. When in doubt, we mark the link as “unknown” rather than guessing. Poor annotation breaks the biological story. It is better to admit limits than to build on sand.
Every mistake described here comes from real experience. We have encountered them in client projects, peer-reviewed manuscripts, and sometimes even in our own early work. ATAC-seq offers powerful insights into chromatin regulation - but only if handled with proper care and deep understanding.
Being a good bioinformatician in this field is not about using the newest tools or most complex pipelines. It is about asking the right questions, checking assumptions, and staying skeptical of anything that looks too clean. Almost every failure we’ve seen could have been avoided - if only someone had paused to look more carefully.
If this series helps even one research team avoid a false discovery or a painful reviewer comment, then it was worth every word.
If you've read this far, you're already ahead of most teams doing ATAC-seq.
You’ve seen how easily small missteps — in quality control, peak comparison, normalization, or annotation - can distort results and weaken conclusions. But these aren’t just technical oversights. They shape what your reviewers see, what your figures show, and whether your story truly holds together.
We’ve helped many research teams catch these issues early, resolve complex discrepancies, and defend their ATAC-seq findings with confidence. Whether you're just starting out or heading into revision, we’re here to support your analysis and strengthen your conclusions.
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