scNOMe-seq Analysis Pipeline

Overview

The scNOMe-seq (single-cell Nucleosome, Methylation and Expression) analysis pipeline consists of two main modules for detecting nucleosome-depleted regions (NDRs) from single-cell bisulfite sequencing data.

source: https://www.illumina.com/science/sequencing-method-explorer/kits-and-arrays/nome-seq.html

Requirements

• Software: conda (see installing Miniconda3)
• Dataset: genome assembly and annotation from UCSC

Note: refer to config/config.yaml for details

Setup

Clone the repository

git clone https://github.com/sherryxuePKU/scNOMe-seq.git
cd scNOMe-seq

Create a Conda environment

conda install -c bioconda snakemake

conda env create -n scNOMeSeq -f base.yaml
conda env create -n py2 -f py2.yaml

Directory Structure

scNOMe_xxh/
├── config/
│   ├── batches.xls     # Pseudobulks to run
│   ├── cells.xls       # Single cells to run
│   └── config.yaml
├── envs/               # config for conda environment
├── resources/          # setting for HPC
├── workflow/
│   ├── S01_cell_preprocess.smk    # Single-cell preprocessing module
│   ├── S02_NDR_analysis.smk       # NDR detection module
│   ├── scripts/
│   └── rules/
└── run_pipeline.sh

Analysis Workflow

Step 1: Single-Cell Preprocessing (S01_cell_preprocess.smk)

Setting: Update the config/cells.xls according to your needs (one cell for each row).

Purpose: Process raw data of individual single cells through trimming, aligment, methylation extraction and quality control

Components:

• Trimming: Remove adapters and low-quality bases using Trim Galore
• Alignment: Map reads to reference genome using Bismark
• Methylation Extraction: Extract CpG and GpC methylation using methylpy
• Statistics: Generate alignment and methylation statistics

# run locally
snakemake -s workflow/S01_cell_preprocess.smk

# run in SLURM HPC
bash run_pipeline.sh workflow/S01_cell_preprocess.smk

Step 2: Quality Control Assessment

Purpose: Filter cells based on bisulfite conversion efficiency and in vitro methylation efficiency

Quality Metrics:

• CT Conversion Rate: Unmethylated WCG ratio of spike-in (such as lambda DNA)
• In vitro M.CviPI Conversion Rate: Methylated GCH ratio of spike-in

Setting: Update the config/batches.xls according to your needs (cells passing quality control only).

Step 3: Pseudobulk NDR Detection (S02_NDR_analysis.smk)
Purpose: Combine high-quality cells into pseudobulk and detect nucleosome-depleted regions

Components:

• Cell Aggregation: Merge cells passing QC into pseudobulk
• Methylation Integration: Extract CpG and GpC methylation
• NDR Calling: Identify regions with low GpC methylation (indicating nucleosome depletion)

# run locally
snakemake -s workflow/S02_NDR_analysis.smk

# run in SLURM HPC
bash run_pipeline.sh workflow/S02_NDR_analysis.smk

Reference

NOMe-seq: Kelly TK, Liu Y, Lay FD, Liang G, Berman BP, Jones PA. Genome-wide mapping of nucleosome positioning and DNA methylation within individual DNA molecules. Genome Res. 2012;22(12):2497-2506. doi:10.1101/gr.143008.112

scCOOL-seq: Li L, Guo F, Gao Y, et al. Single-cell multi-omics sequencing of human early embryos. Nat Cell Biol. 2018;20(7):847-858. doi:10.1038/s41556-018-0123-2