MamPipe - Mammalian Whole Genome Sequencing Pipeline

A comprehensive Nextflow pipeline for mammalian whole genome sequencing (WGS) analysis using Oxford Nanopore long-read data. This pipeline can run locally or on Google Cloud Batch for scalable compute.

Pipeline Overview

MamPipe performs end-to-end genome assembly and quality assessment:

1. Quality Control - NanoPlot visualization of read quality
2. Read Filtering - Chopper for quality and length filtering
3. Assembly - Flye for de novo genome assembly
4. Polishing - Medaka for consensus polishing
5. Quality Assessment - BUSCO for completeness evaluation
6. Duplicate Purging - Purge_dups (conditional, based on BUSCO duplication score)
7. Scaffolding - RagTag for reference-guided scaffolding
8. Final QC - BUSCO assessment of final assembly

Optional Modules

• Variant Calling (vcf.nf) - BWA alignment, GATK variant calling, and filtering

Requirements

Local Execution

• Nextflow (≥22.04)
• Conda or Mamba
• 16+ GB RAM recommended
• Multi-core CPU (4+ cores recommended)

Google Cloud Batch Execution

• Google Cloud Project with Batch API enabled
• Google Cloud Storage bucket
• Docker image (provided Dockerfile)

Installation

1. Clone the repository:

git clone https://github.com/genecodAlan/mampipe.git
cd mampipe

2. Install Nextflow:

curl -s https://get.nextflow.io | bash

3. Ensure Conda/Mamba is installed for environment management

Usage

Local Execution

nextflow run main.nf \
  --reads 'data/*.fastq' \
  --ref 'refSeq/*.fna' \
  --outdir 'results' \
  --busco_threshold 6.0 \
  --lineage 'mammalia_odb10'

Google Cloud Batch Execution

1. Build and push the Docker image:

cd Github_Upload
docker build -t gcr.io/YOUR_PROJECT/mampipe:latest .
docker push gcr.io/YOUR_PROJECT/mampipe:latest

2. Configure your Google Cloud settings in nextflow.config:

process {
    executor = 'google-batch'
    container = 'gcr.io/YOUR_PROJECT/mampipe:latest'
    workDir = 'gs://YOUR_BUCKET/work'
}

google {
    project = 'YOUR_PROJECT_ID'
    region = 'us-central1'
    batch {
        spot = true
        bootDiskSize = '50 GB'
    }
}

3. Run the pipeline:

nextflow run batch_main.nf -c nextflow.config

Parameters

Parameter	Default	Description
--reads	data/*.fastq	Path to input FASTQ files
--ref	refSeq/*.fna	Path to reference genome
--outdir	WGS_results	Output directory
--busco_threshold	6.0	BUSCO duplication threshold (%)
--lineage	mammalia_odb10	BUSCO lineage dataset
--threads	8	Number of threads for most processes
--threads_assembly	4	Threads for Flye assembly
--threads_polish	4	Threads for Medaka polishing
--medaka_model	r1041_e82_400bps_fast_g632	Medaka model for your sequencing chemistry
--assembly_input	results/polished/...	Path to existing assembly (Post_Assembly.nf only)

Pipeline Workflows

Main Assembly Pipeline (main.nf / batch_main.nf)

Complete genome assembly from raw reads to scaffolded assembly

Post-Assembly Processing (Post_Assembly.nf / batch_Post_assembly.nf)

BUSCO assessment, conditional purging, and scaffolding for existing assemblies

Variant Calling (vcf.nf)

SNP/indel calling from aligned reads

Output Structure

results/
├── read_plots/              # NanoPlot QC reports
├── chopped/                 # Filtered reads
├── assembly/                # Flye assembly output
├── polished/                # Medaka polished assembly
├── busco_precheck/          # Initial BUSCO results
├── purged_output/           # Purge_dups output (if triggered)
├── scaffolded/              # RagTag scaffolded assembly
└── FINAL_BUSCO/             # Final quality assessment

Tools & Versions

• NanoPlot - Read quality visualization
• Chopper - Read filtering
• Flye - Genome assembly
• Medaka - Assembly polishing
• BUSCO - Completeness assessment
• Purge_dups - Haplotig purging
• RagTag - Reference-guided scaffolding
• minimap2 - Read alignment
• GATK4 - Variant calling (optional)

Citation

If you use this pipeline, please cite the individual tools used in your analysis.

License

MIT License

Contact

For issues and questions, please open an issue on GitHub.