Ming "Tommy" Tang

1/ You think you're mapping transcription factor binding sites, but your assay might be lying to you.
Here's what you may miss about ATAC-seq and DNase-seq.

2/
There are multiple ways to identify regulatory elements in the genome: ATAC-seq, DNase-seq, MNase-seq, FAIRE-seq.

Each uses a different enzyme or method to probe chromatin structure.
They're not interchangeable. And they're not equally accurate.

3/
ATAC-seq became the dominant method because it's fast, requires fewer cells, and the protocol is straightforward.

It uses Tn5 transposase to simultaneously cut and tag accessible DNA.
But here's the problem: Tn5 doesn't discriminate the way you think it does.

4/
Tn5 cuts BOTH: → Nucleosome linker DNA (the spaces between nucleosomes) → Open chromatin regions (where transcription factors actually bind)

You get a mixed signal: nucleosome positioning data + TF binding information, all jumbled together.

5/
DNase-seq seems cleaner at first glance.
DNase I preferentially cuts only the open chromatin regions where DNA is truly accessible.

You'd think this gives you a purer signal for TF binding sites.
But there's still a critical flaw.

6/
Every enzyme has sequence bias.

Tn5 prefers certain DNA sequences over others. So does DNase I.

When you're trying to identify the exact location of a transcription factor binding site (the "footprint"), these biases distort your results.

7/
The footprint problem is serious:
You see a pattern that looks like TF binding, but part of that pattern comes from the enzyme's cutting preferences, not actual protein-DNA interaction.

Without correcting for this, you're chasing artifacts.

8/
A 2013 Nature paper demonstrated how bad this can get: 
The authors showed that sequence-specific cutting biases create false motif footprints.

Translation: You might be "discovering" binding sites that don't actually exist.

9/
MNase-seq and FAIRE-seq have their own quirks:

MNase cuts linker DNA preferentially and has its own sequence bias. FAIRE-seq relies on crosslinking and phenol-chloroform extraction (messy, variable).

No method is perfect. Each reveals a different slice of chromatin architecture.

10/
So what do you do?
→ Understand what your assay actually measures → Use computational tools that correct for enzymatic bias → Integrate multiple methods when possible → Never trust a single experiment to tell the whole story

11/
Key takeaways:
• ATAC-seq is convenient but mixes nucleosome and accessibility signals
• DNase-seq is more specific but still has cutting bias

• Motif footprints can be artifacts if bias isn't corrected
• Different assays reveal different aspects of chromatin structure
• Always validate findings with orthogonal methods

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🧵 Bioinformaticians: Drowning in multiple projects? Here's why context switching is killing your productivity—and how to fix it. 

1/

 Too many projects = too little progress.

 When your attention is scattered, your output drops.

 Context switching is the silent killer.

2/

 Each project has its own biology:

 🧬 Different TFs

 🧪 Different cell lines

 ⚙️ Different pipelines

 Switching eats mental energy.

3/

 Example: Jumping from CUT&amp;RUN on H3K27me3 in mESCs to ChIP-seq on c-Myc in HeLa = full brain reboot.

4/

 You spend time recalling:

• QC parameters
• Genome version
• Where peaks are saved
• Which scripts and tools you used

5/

 And if the project isn't organized, you’ll waste even more time digging through old folders, scratch files, or rerunning commands.

6/

  Solution 1: Ruthless prioritization

 Pick ONE project each day. Focus. Finish. Ship it.

 Half-done science doesn’t help anyone. Multitasking is a trap.

7/

Solution 2: Use consistent folder structures

 Example:

project_name/
├── raw_data/
├── fastqc/
├── trimmed/
├── alignment/
├── results/
├── scripts/

8/

Solution 3: Add a README to each project

 Include:

• Data source
• Goals
• Key results
• Analysis steps
• TODOs
This saves hours when switching context or collaborating.
document as much as possible.
it may seem to be a waste of time, but it saves you time long term

9/

Solution 4: Keep a running log

 Markdown or plain text. Example:

## 2025-04-10
- Trimmed reads with fastp
- Aligned with bowtie2 (hg38)
- Called peaks with macs2

10/

  Key Takeaways:

• Switching projects costs time and focus
• Finish one before hopping to another
• Structure + notes = less rework

11/

 ✅ Action items:

• Choose 1 project to focus on this week
• Create folder structure &amp; README
• Start a daily or weekly log

12/

 Deep work leads to deep insights.

 Prioritize. Organize. Write it down.

 Make future-you thank today-you.

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