ChIP-Seq Pipeline

Overview

The ChIP-Seq pipeline is based on nf-core/chipseq v2.0.0, a bioinformatics best-practice analysis pipeline for ChIP-seq data. This pipeline performs quality control, alignment, peak calling, and differential binding analysis for ChIP-seq experiments.

Pipeline Features

• Multiple alignment options: BWA, Chromap, Bowtie2, STAR
• Comprehensive quality control: FastQC reports and MultiQC summaries
• Peak calling: MACS2 for identifying enriched regions
• Peak annotation: HOMER for genomic feature annotation
• Differential binding analysis: Compare binding between conditions
• Reproducible analysis: Docker/Singularity containers ensure consistency

Pipeline Workflow

1. Raw read QC - FastQC assessment of sequencing quality
2. Adapter trimming - Trim Galore! removes adapter sequences
3. Alignment - Map reads to reference genome
4. Duplicate marking - Identify and mark PCR duplicates
5. Filtering and QC - Remove low-quality alignments
6. Peak calling - MACS2 identifies enriched regions
7. Peak annotation - HOMER annotates peaks with genomic features
8. Differential analysis - Compare binding across conditions

Input Requirements

Sample Sheet Format

The pipeline requires a CSV sample sheet with the following columns:

Example sample sheet:

sample,fastq_1,fastq_2,antibody,control
CONTROL_1,control_1.fastq.gz,,input,
CONTROL_2,control_2.fastq.gz,,input,
TREATMENT_1,treatment_1_R1.fastq.gz,treatment_1_R2.fastq.gz,h3k27ac,CONTROL_1
TREATMENT_2,treatment_2_R1.fastq.gz,treatment_2_R2.fastq.gz,h3k27ac,CONTROL_2

Key Parameters

Running the Pipeline

Basic execution command:

nextflow run nf-core/chipseq \
  --input samplesheet.csv \
  --outdir results \
  --genome GRCh37 \
  -profile docker

Resume an interrupted run:

nextflow run nf-core/chipseq \
  --input samplesheet.csv \
  --outdir results \
  --genome GRCh37 \
  -profile docker \
  -resume

Output Files

The pipeline generates the following key outputs:

• Quality Control

  • FastQC reports for raw and processed reads
  • MultiQC report summarizing all QC metrics

• Alignments

  • BAM files with aligned reads
  • BAM index files (.bai)
  • Alignment statistics

• Peak Files

  • Narrowpeak/broadpeak files from MACS2
  • Summit files indicating peak centers
  • Peak annotation files from HOMER

• Coverage Tracks

  • BigWig files for genome browser visualization
  • Normalized coverage tracks

• Differential Binding

  • Differential peak analysis results
  • Plots and statistics for differential binding

Tips and Best Practices

Experimental Design

• Control samples are critical: Always include appropriate input or IgG controls
• Biological replicates: Include at least 2-3 biological replicates per condition
• Sequencing depth: Aim for 20-50 million reads for transcription factors, 50-100 million for histone marks
• Antibody validation: Ensure antibody specificity through western blot or ChIP-qPCR

Sample Preparation

• Cross-linking optimization: Use 1% formaldehyde for most proteins, consider dual cross-linking for larger complexes
• Fragmentation consistency: Aim for 200-300bp fragments for transcription factors, 200-500bp for histone marks
• Library complexity: Check library complexity metrics to ensure sufficient unique molecules

Parameter Optimization

• Peak calling mode: Use narrow for transcription factors and sharp histone marks (H3K4me3, H3K27ac)
• Broad peak calling: Use broad mode for spreading marks like H3K36me3, H3K27me3
• Effective genome size: Use appropriate size for your organism (e.g., 2.7e9 for human, 1.87e9 for mouse)
• Duplicate removal: Keep duplicates for low complexity samples or single-end data

Quality Assessment

• FRiP score: Fraction of Reads in Peaks should be >5% for acceptable quality
• NSC and RSC: Check strand cross-correlation metrics for library quality
• Peak reproducibility: Use IDR (Irreproducible Discovery Rate) for replicate concordance
• Signal-to-noise: Review fingerprint plots in MultiQC report

Troubleshooting

Common Issues

Issue: Low number of peaks detected

• Solution: Check antibody quality and ChIP efficiency
• Reduce MACS2 q-value threshold (try 0.05 or 0.1)
• Verify sufficient sequencing depth for your target
• Check fragment size distribution is appropriate

Issue: High background or noisy data

• Solution: Increase stringency in peak calling (lower q-value)
• Check for PCR duplicate rates - high rates indicate low complexity
• Verify proper input/IgG control subtraction
• Consider additional filtering based on fold enrichment

Issue: Peaks not overlapping between replicates

• Solution: Check if replicates cluster together in PCA/correlation analysis
• Use IDR to identify reproducible peaks
• Consider batch effects in sample processing
• Verify consistent antibody lot numbers

Issue: Memory errors during alignment

• Solution: Reduce number of parallel alignment jobs
• Use --max_memory and --max_cpus to limit resources
• Process samples in smaller batches

Issue: No differential binding detected

• Solution: Check if samples separate by condition in PCA plots
• Verify sufficient peak overlap between samples
• Consider using a more lenient FDR threshold
• Ensure peak calling was performed consistently

Issue: Adapter contamination in results

• Solution: Check FastQC reports for adapter content
• Adjust Trim Galore parameters for more aggressive trimming
• Verify correct adapter sequences are specified

Additional Resources

• Full documentation: nf-core/chipseq documentation
• Pipeline source code: GitHub repository
• Support: Join the #chipseq channel on nf-core Slack
• Citation: 10.5281/zenodo.3240506