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.
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ATAC-seq peaks file: Genomic regions representing integrated (merged) open chromatin sites for two distinct experimental conditions:
- 1- ATAC_Peak_file
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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
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Processed DataFrames: Data for each condition, including ChIP-seq signal around ATAC-seq peaks, used to calculate the average tag counts for each peak.
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Summed Tags: Total tag counts for each condition, representing the overall acetylation signal intensity across the regions of interest.
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DESeq2 output: Differential acetylation results (fold change > 2, p-adjusted < 0.05) identifying significantly modified regions.
- 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
- Python 3.7+
- HOMER Suite: Ensure that HOMER is installed and accessible in your system's PATH.
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
Contact [email protected] in case of questions, and to request the data files instead of extracting them from the GEO archive