tools-only Methylation-Based Detection - Usage Guide
Overview
Analyze cfDNA methylation patterns for cancer detection and tissue-of-origin analysis. Uses bisulfite sequencing or cfMeDIP-seq for methylation profiling.
Prerequisites
# MethylDackel
conda install -c bioconda methyldackel
# Dependencies
pip install pandas numpy scipy matplotlib
Quick Start
Tell your AI agent what you want to do:
- "Extract methylation from my bisulfite-seq cfDNA BAM"
- "Find differentially methylated regions between cancer and normal"
- "Perform tissue-of-origin deconvolution from cfDNA methylation"
- "Analyze MCED-style regions for cancer detection"
Example Prompts
Methylation Extraction
"Extract CpG methylation levels from my bisulfite BAM using MethylDackel."
"Calculate beta values from methylation calls."
DMR Analysis
"Find differentially methylated regions between my cancer and normal samples."
"Identify hypermethylated regions in cancer cfDNA."
Tissue Deconvolution
"Deconvolve tissue composition from cfDNA methylation using reference atlas."
What the Agent Will Do
- Extract methylation from bisulfite-seq BAM
- Calculate beta values for CpG sites
- Identify differentially methylated regions
- Perform tissue deconvolution if reference available
- Score cancer-specific methylation signatures
Tips
- MethylDackel is actively maintained and nf-core integrated
- cfMeDIP-seq is good for low input (>= 5ng)
- Bisulfite-seq gives single-base resolution but needs >= 10ng
- Tissue deconvolution requires reference methylomes
- Limitation: Often requires >= 10ng cfDNA input
Related Skills
- cfdna-preprocessing - Preprocess before methylation analysis
- fragment-analysis - Complement with fragmentomics
- methylation-analysis/bismark-alignment - General methylation processing