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ATAC-Seq and RNA-Seq Integrated Analysis Workflow: Full Document Explanation

This document provides a comprehensive explanation of the multi-omics workflow illustrated in the original slides (Process.pdf). It describes how ATAC-Seq and RNA-Seq datasets were processed and integrated to identify putative regulatory genes associated with EHMT2 knockdown (KD). The explanation is organized page-by-page according to the source document.

Page 1 --- Overview of Sample Grouping and ATAC-Seq Processing

Sample Grouping and Key Comparisons

The top-left section summarizes the experimental design, including control and KD groups, each with multiple biological replicates (B and C).
The red-highlighted comparisons indicate the specific contrasts used for downstream ATAC-Seq differential accessibility analysis. These comparisons form the basis for identifying KD-induced chromatin accessibility changes.

ATAC-Seq Analytical Workflow

The lower portion of Page 1 outlines the pipeline for identifying putative target genes using two analytical strategies.

Method 1: Annotation First, Then Intersection

  1. MACS2 is used for peak calling on each biological replicate.
  2. Peaks from each replicate are directly annotated to the human genome (GRCh38.p14).
  3. Differentially Accessible Areas (DAAs) are defined by intersecting annotated peaks between replicates B and C.
  4. Genes linked to these intersected peaks are identified as DAA-associated genes.

This method typically yields a larger number of genes.

Method 2: Intersection First, Then Annotation

  1. MACS2 peak calling is performed on each replicate.
  2. Shared peak regions between replicates B and C are identified.
  3. These consensus peak regions are then annotated.
  4. Genes associated with these regions are extracted as DAA-associated genes.

This method identifies fewer but more reproducible peaks across replicates.

In summary, Method 1 is more inclusive, while Method 2 is more stringent.

Page 2 --- DAA Identification: Open vs. Closed Chromatin Regions

Two Venn diagrams compare Method 1 and Method 2 results.

Figure A --- Regions Open After KD (KD vs. Control)

These regions exhibit increased chromatin accessibility following EHMT2 KD.
The overlap between methods indicates that many regions are consistently detected, though Method 1 has greater sensitivity.

Figure B --- Regions Closed After KD (Control vs. KD)

These represent genomic regions with reduced accessibility after EHMT2 KD.
Again, Method 1 produces a larger gene set, whereas Method 2 yields a refined subset of reproducible accessibility losses.

Page 3 --- Multi-Omics Integration: Open Chromatin and Upregulated Genes (OpenUP)

Page 3 integrates ATAC-Seq open-region results with RNA-Seq differential expression from KD1 and KD2.

RNA-Seq significance thresholds:\

  • |log₂FC| > 1\
  • p < 0.05

Figure A --- Intersection Analysis

  • ATAC-Seq: Intersection of open-region results from Methods 1 and 2\
  • RNA-Seq: Intersection of significantly upregulated genes in KD1 and KD2

This identifies genes that show both structural chromatin opening and transcriptional upregulation, representing strong candidates activated by KD.

Figure B --- Union Analysis

  • ATAC-Seq: Union of open regions from both methods\
  • RNA-Seq: Union of upregulated DEGs from both KD experiments

This produces a broader set of candidate genes, capturing genes supported by either dataset.

Page 4 --- Multi-Omics Integration: Closed Chromatin and Downregulated Genes (CloseDOWN)

This page mirrors the logic of Page 3 but focuses on genes whose chromatin and expression are both repressed.

Figure A --- Intersection Analysis

  • ATAC-Seq: Intersection of Method 1 and Method 2 for closed regions\
  • RNA-Seq: Intersection of significantly downregulated genes in KD1 and KD2

These genes show consistent reductions in both chromatin accessibility and expression.

Figure B --- Union Analysis

  • ATAC-Seq: Union of closed regions from both methods\
  • RNA-Seq: Union of downregulated DEGs

This yields a more inclusive list of potentially EHMT2-regulated repressed genes.

Page 5 --- Pearson Correlation Heatmaps with EHMT2

The final page displays two Pearson correlation heatmaps constructed using RNA-Seq expression values from an EHMT2 tsv file.

Figure A --- Genes Open in ATAC-Seq and Upregulated in RNA-Seq

This heatmap shows correlations between EHMT2 and the 11 genes identified in the union analysis on Page 3B.
The correlation patterns help infer potential regulatory interactions---positive or negative---associated with EHMT2 KD.

Figure B --- Genes Closed in ATAC-Seq and Downregulated in RNA-Seq

This heatmap presents correlations between EHMT2 and the 8 genes identified in Page 4B.
These genes may reflect EHMT2-dependent chromatin silencing effects.

Overall Summary

Across all pages, the workflow integrates structural (ATAC-Seq) and transcriptional (RNA-Seq) changes to systematically identify EHMT2-related regulatory genes. Key features include:

  1. Two complementary ATAC-Seq pipelines providing sensitive versus stringent detection of differential chromatin accessibility.\
  2. Integration with RNA-Seq to identify genes coherently regulated at both chromatin and expression levels.\
  3. Pearson correlation analysis to evaluate co-regulation with EHMT2 and strengthen candidate gene prioritization.

This multi-omics strategy enables robust identification of potential EHMT2-regulated genes and biological pathways influenced by knockdown effects.