Project Proposal: ATAC-Seq Analysis

Project Title: ATACseq Individual Project

Background Information: ATAC-seq (Assay for Transposase-Accessible Chromatin using sequencing) is a powerful method used to assess the open chromatin regions across the genome, providing insights into regulatory elements that control gene expression. This project involves the analysis of two ATAC-seq replicates from a human source, which present unique challenges due to the absence of control samples.

Objective: To conduct a comprehensive analysis of the ATAC-seq data from two human replicates to map accessible chromatin, identify regulatory elements, and analyze transcription factor binding sites and gene enrichment.

Methods and Workflow:

1. Sample Preparation and Quality Control

   • Pre-alignment QC: Use FastQC to assess base quality, GC content, sequence length distribution, and check for sequencing adapters.
   • Adapter Trimming: Utilize Trimmomatic for removing adapters and trimming low-quality bases.

2. Alignment

   • Tool: Bowtie2
   • Procedure: Align reads to the human reference genome hg38 using Bowtie2 with the -X 2000 flag to manage maximum insert size, focusing on achieving a unique mapping rate of over 80%.

3. Post-Alignment Processing

   • Mitochondrial DNA Removal: Use SAMtools to filter out reads aligning to the mitochondrial genome to reduce noise.
   • Read Shifting: Adjust reads using Deeptools to correct for the tagmentation bias, aligning reads more accurately with the transposase cutting sites.

4. Quality Control Post-Alignment

   • Fragment Size Distribution: Employ ATACSeqQC to analyze and plot fragment distribution sizes to confirm the expected nucleosome patterning of less than 100bp for nucleosome-free regions and around 200bp, 400bp, and 600bp for nucleosome patterns.

5. Peak Calling

   • Tool: MACS3
   • Procedure: Call peaks using default ATAC-seq settings in MACS3. Develop a strategy with Bedtools for defining reproducible peaks across replicates and filter out peaks located in ENCODE blacklisted regions.

6. Data Analysis and Integration

   • Motif Analysis: Use MEME Suite or HOMER to identify transcription factor binding motifs within the reproducible peaks.
   • Gene Enrichment Analysis: Apply tools like GREAT or GSEA to perform enrichment analysis based on the genes associated with identified peaks.

7. Visualization and Reporting

   • Coverage Plots: Create signal coverage plots centered on TSS for nucleosome-free and nucleosome-bound regions using Deeptools.
   • Fragment Length Distribution: Generate plots to display the distribution of fragment lengths for each sample.
   • Chromatin Accessibility: Produce figures displaying the proportions of chromatin regions marked as peaks (e.g., Promoter, Intergenic, Intron, Exon).

8. Deliverables:

   • Quality control reports and alignment statistics.
   • Tables and plots of fragment length distribution and peak analysis results.
   • A single BED file containing the reproducible peaks identified.
   • Tables and figures summarizing motif and gene enrichment analyses.
   • Figures illustrating the proportions of chromatin accessibility.

Conclusion: This proposal outlines a structured approach to ATAC-seq analysis, from quality control through to advanced genomic analyses, ensuring comprehensive insights into chromatin dynamics and gene regulation. This systematic exploration will leverage cutting-edge bioinformatics tools and techniques to deliver robust and insightful results.

References: Yan, F., Powell, D.R., Curtis, D.J. et al. From reads to insight: a hitchhiker’s guide to ATAC-seq data analysis. Genome Biol 21, 22 (2020). https://doi.org/10.1186/s13059-020-1929-3 Andrews S. Babraham Bioinformatics - FastQC A Quality Control tool for High Throughput Sequence Data. https://www.bioinformatics.babraham.ac.uk/projects/fastqc/
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