Cefiderocol Omics Resistance Exploration - A. baumannii

Reproducible Nextflow pipeline for replicating the genomic and functional analysis of cefiderocol resistance in Acinetobacter baumannii from:

Morosi et al. (2024). Distinct chromosomal mutation associated with cefiderocol resistance in Acinetobacter baumannii: a combined bioinformatics and mass spectrometry approach to unveil and validate the in vivo-acquired chemoresistance. Front. Microbiol. 15:1480322. DOI: 10.3389/fmicb.2024.1480322

Overview

This pipeline automates the complete genomic analysis workflow for detecting cefiderocol resistance mechanisms in A. baumannii, including:

• ✅ Quality control and read trimming (Trim Galore)
• ✅ Genome assembly (SPAdes)
• ✅ Contamination detection (FCS-GX)
• ✅ Genome comparison (FastANI, pyANI)
• ✅ Sequence typing (MLST)
• ✅ Genome annotation (Prokka)
• ✅ Variant calling (Snippy)
• ✅ Variant annotation and summary reporting

Quick Start

Prerequisites

• Nextflow ≥ 25.10.0
• Docker OR Conda
• 8+ GB RAM (16+ GB recommended)
• ~50 GB disk space per sample

Installation

# Clone the repository
git clone https://github.com/yourusername/cefiderocol-resistance-pipeline.git
cd cefiderocol-resistance-pipeline

# Run the full setup (downloads genomes and prepares reads)
bash bin/setup.sh

Run Analysis

Using Docker (recommended for reproducibility)

nextflow run main.nf \
    --reads_dir ./reads \
    --reference_genome bin/test_data/reference.fasta \
    --outdir ./results \
    -profile docker

Using Conda

nextflow run main.nf \
    --reads_dir ./reads \
    --reference_genome bin/test_data/reference.fasta \
    --outdir ./results \
    -profile conda

Test Run

nextflow run main.nf -profile test,conda

Input Data

Read Files

Place paired-end FASTQ files in a single directory with the naming convention:

reads/
  ├── sample1_R1.fastq.gz
  ├── sample1_R2.fastq.gz
  ├── sample2_R1.fastq.gz
  └── sample2_R2.fastq.gz

Supported naming patterns:

• *_R1.fastq.gz / *_R2.fastq.gz
• *_1.fastq.gz / *_2.fastq.gz

Reference Genome

Provide a reference genome FASTA file (e.g., A. baumannii reference strain)

Example: Using reference from the paper

# Available from NCBI BioProject PRJNA1126554
# Or download from ENA
wget ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/022/468/295/GCA_022468295.1_ASM2246829v1/GCA_022468295.1_ASM2246829v1_genomic.fna.gz
gunzip GCA_022468295.1_ASM2246829v1_genomic.fna.gz

Output Structure

results/
├── 01_trimmed_reads/           # Quality-controlled reads
├── 02_assembly/                # Assembled genomes (contigs)
├── 03_contamination_detection/ # FCS-GX contamination report
├── 04_genome_comparison/       # ANI results (FastANI, pyANI)
│   ├── fastani/               # Pairwise ANI comparisons
│   └── pyani/                 # Whole-genome alignments
├── 05_sequence_typing/         # MLST results
├── 06_annotation/              # Prokka annotations (GFF, FAA)
├── 07_variant_calling/         # Snippy variants (VCF)
│   └── core/                  # Core SNP alignment
├── 08_variant_annotation/      # Annotated VCF and variant summaries
├── pipeline_report.html        # Execution report
├── pipeline_timeline.html      # Runtime statistics
└── pipeline_dag.html           # Workflow DAG

Key Outputs

Variant Annotation

The final output of the pipeline is the annotated variant summary:

• sample_id_annotated.vcf - Annotated VCF copy generated from Snippy output
• sample_id_variant_summary.txt - SNP/INDEL summary with per-variant listing

These files provide the functional variant summary used for downstream interpretation.

Parameters

# Assembly parameters
--spades_memory 32              # SPAdes RAM allocation (GB)
--spades_cpus 8                 # SPAdes CPU threads
--spades_kmers "21,33,55,77,99,127"  # k-mer sizes for assembly

# Quality filtering
--clip_r1 5                     # Clip 5bp from R1
--clip_r2 5                     # Clip 5bp from R2
--quality 20                    # Minimum base quality
--length 75                     # Minimum read length

# Variant calling
--snippy_minsnps 4              # Minimum SNPs for core alignment
--snippy_minqual 60             # Minimum variant quality
--snippy_mincov 10              # Minimum variant coverage

Configuration

Profiles

• docker - Run with Docker containers (reproducible)
• conda - Run with Conda environments
• singularity - Run with Singularity containers (HPC-friendly)
• test - Run with minimal resources on synthetic data

Resource Limits

Modify in nextflow.config:

process {
    cpus = 2
    memory = 4.GB
    
    withName: SPADES {
        cpus = 8
        memory = 32.GB
    }
}

Bioinformatics Methods

1. Read Processing

• Trim Galore: Quality trimming and adapter removal
• Parameters: quality ≥ 20, minimum length 75 bp

2. Genome Assembly

• SPAdes: De novo assembly with multiple k-mers
• Parameters: k-mer range 21-127, careful mode enabled

3. Contamination Detection

• FCS-GX: Detects non-target organism sequences

4. Comparative Genomics

• FastANI: Rapid pairwise ANI calculation
• pyANI: MUMmer-based whole-genome alignment

5. Variant Discovery

• Snippy: Variant calling against reference
• Snippy-core: Core SNP alignment across samples

6. Variant Annotation

• Variant Annotation Module: Summarizes Snippy variants and copies the annotated VCF
• Functional interpretation of SNP and INDEL calls

Citation

If you use this pipeline, please cite:

@article{Morosi2024,
  title={Distinct chromosomal mutation associated with cefiderocol resistance 
         in {A}cinetobacter baumannii: a combined bioinformatics and mass 
         spectrometry approach},
  author={Morosi, L. and Golzato, D. and Bussini, L. and others},
  journal={Frontiers in Microbiology},
  volume={15},
  pages={1480322},
  year={2024},
  doi={10.3389/fmicb.2024.1480322}
}

Also cite the tools:

• Nextflow: Di Tommaso et al. (2017), Nat. Biotechnol. 35:316-320
• SPAdes: Bankevich et al. (2012), J. Comput. Biol. 19:455-477
• Snippy: Seemann (2015), https://github.com/tseemann/snippy

Troubleshooting

Issue: "Cannot locate Docker image"

Solution: Ensure Docker daemon is running or switch to Conda profile

nextflow run main.nf -profile conda

Issue: Insufficient memory

Solution: Increase resource allocation

--spades_memory 64 --spades_cpus 16

Issue: Reads not found

Solution: Check file naming and path

# List files to verify naming
ls -la reads/*fastq.gz

Performance Notes

• Single isolate: ~30-45 minutes (with 8 CPU, 32 GB RAM)
• Multiple isolates: Scales linearly; supports HPC submission
• Disk space: ~20-30 GB per complete analysis

Advanced Usage

Run with resource requests (HPC)

nextflow run main.nf \
    --reads_dir ./reads \
    --reference_genome bin/test_data/reference.fasta \
    -profile singularity \
    -cluster slurm \
    --slurm_queue normal

Resume failed run

nextflow run main.nf -resume -profile docker

Generate execution DAG

nextflow run main.nf -profile test,conda -with-dag dag.pdf

Contributing

Contributions welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Submit a pull request

License

MIT License - See LICENSE file

Support

For issues, questions, or suggestions:

• Open an Issue
• Consult the Documentation
• Contact the paper authors

References

1. Paper: Morosi et al. Front. Microbiol. 2024. DOI: 10.3389/fmicb.2024.1480322
2. Related: McElheny et al. Microbiol. Spectr. 2021. DOI: 10.1128/Spectrum.01779-21
3. Review: Noinaj et al. Annu. Rev. Microbiol. 2010. DOI: 10.1146/annurev.micro.112408.134247

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Last Updated: April 2026
Nextflow Version: ≥ 25.10.0
Maintained by: Co-authors of Morosi et al. 2024