PEAKDIFF

This Python-based bioinformatics pipeline integrates ATAC-seq and H3K27ac ChIP-seq data to perform differential analysis between two distinct experimental conditions. It processes and visualizes ChIP-seq signal intensities around distal ATAC-seq peaks of specific size windows and identifies regions of differential acetylation using DESeq2 (with thresholds of FC > 2 and p-adj < 0.05). The pipeline generates scatter plots that visualize the average signal within each peak across both conditions and uses color coding to categorize peaks based on their condition-specific classifications.

Data Input:

• ATAC-seq peaks file: Genomic regions representing integrated (merged) open chromatin sites for two distinct experimental conditions:

  • 1- ATAC_Peak_file

• H3K27ac ChIP-seq signal tag directories: Acetylation tag counts captured as two distinct H3k27ac ChIP-seq tag directories (2 reps per condition) for the two experimental conditions.

  • 2- Healthy H3K27ac ChIP rep1
  • 3- Healthy H3K27ac ChIP rep2
  • 4- MASH H3K27ac ChIP rep1
  • 5- MASH H3K27ac ChIP rep2

Data Output:

• Processed DataFrames: Data for each condition, including ChIP-seq signal around ATAC-seq peaks, used to calculate the average tag counts for each peak.

• Summed Tags: Total tag counts for each condition, representing the overall acetylation signal intensity across the regions of interest.

• DESeq2 output: Differential acetylation results (fold change > 2, p-adjusted < 0.05) identifying significantly modified regions.

Graphs Generated:

• Scatter Plot of H3K27ac Signal: Shows average tag counts for each condition, comparing acetylation levels around ATAC-seq peaks.
  • Color-coding: Regions are colored based on differential acetylation: red/orange for increased acetylation during NASH, blue/purple for decreased acetylation, and green for overlapping with KC signature enhancers.
• Histogram of H3K27ac Signal: Shows the density of H3K27ac signal as a function of distance from peak center, accumulated over all peaks for Condition1 and Condition2

System requirements

• Python 3.7+
• HOMER Suite: Ensure that HOMER is installed and accessible in your system's PATH.

Notebook

PEAKDIFF Notebook

Data Source

The data used in this example is from a 2020 study, Seidman et al, looking at epigenomic changes in the immune system cells (Kupffer) as they undergo a diet induced transformation.

Full datase on GEO: GSE128338

Questions

Contact [email protected] in case of questions, and to request the data files instead of extracting them from the GEO archive