This workflow processes ATAC-seq data from raw FASTQ files to accessibility peaks, with optional differential analysis and transcription factor footprinting.
# CLI tools
conda install -c bioconda fastp bowtie2 samtools macs3 deeptools bedtools tobias
# R packages
BiocManager::install(c('DiffBind', 'ChIPseeker'))Tell your AI agent what you want to do:
- "Run the ATAC-seq pipeline on my samples"
- "Call accessibility peaks from my ATAC-seq data"
- "Find differential accessibility between treatment and control"
"Process my ATAC-seq FASTQ files through peak calling"
"Run ATAC-seq analysis on human samples"
"I have paired-end ATAC-seq, align and call peaks"
"Calculate TSS enrichment for my ATAC-seq"
"Find differential peaks between conditions"
"Run TF footprinting with TOBIAS"
| Input | Format | Description |
|---|---|---|
| FASTQ files | .fastq.gz | Paired-end reads |
| Reference | FASTA | Reference genome + Bowtie2 index |
| Motifs (optional) | JASPAR | For footprinting analysis |
- Quality Control - Trim Nextera adapters
- Alignment - Map reads with Bowtie2
- BAM Processing - Remove chrM, shift for Tn5, deduplicate
- Peak Calling - Call accessible regions with MACS3
- QC - TSS enrichment, FRiP, fragment sizes
- Differential - Compare accessibility between conditions
- Footprinting - Infer TF binding from accessibility patterns
| Aspect | ATAC-seq | ChIP-seq |
|---|---|---|
| Adapters | Nextera | TruSeq |
| Control | None needed | Input required |
| Tn5 shift | Yes (+4/-5 bp) | No |
| chrM | High, remove | Low |
| Peak type | Narrow | Narrow or broad |
- Mitochondrial: Expect 20-50% chrM reads; always filter
- Tn5 shift: Essential for accurate footprinting
- TSS enrichment: Good library shows >5 enrichment
- Fragment sizes: Should show nucleosome-free and nucleosome peaks
- Footprinting: Requires high depth (>50M reads)