PhIP-Seq Analysis pipeline from ONT reads

A Nextflow pipeline for Phage Immunoprecipitation Sequencing (PhIP-Seq) analysis from Oxford Nanopore Technologies (ONT) reads, producing enriched epitopes signal, pathogen exposure scores, IEDB epitope annotations, HL7 FHIR R4 reports, and interactive 3D protein structure visualizations. Full documentation

Installation

From the repo

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

# Clone the repository
git clone https://github.com/oucru-id/phip-seq-ont.git
cd phip-seq-ont

# Verify Nextflow installation
nextflow -v

Directory Structure

phip-seq-ont
├── main.nf                          # Main workflow entry point
├── nextflow.config                  # Configuration and parameters
├── workflows/
│   ├── alignment.nf                 # Read alignment and count collection
│   ├── statistics.nf                # Normalization and Z-score calculation
│   ├── output.nf                    # dataset export
│   ├── fhir_report.nf               # HL7 FHIR R4 bundle generation
│   ├── fdr.nf                       # Benjamini-Hochberg correction
│   ├── virusscore.nf                # Per-species exposure scoring
│   ├── iedb_annotation.nf           # IEDB Epitope annotation and novelty classification
│   ├── visualization.nf             # Interactive heatmaps
│   ├── streamlit.nf                 # 3D protein visualization dashboard
│   ├── neutralization_score.nf      # Neutralization scoring (in development)
│   ├── aggregate.nf                 # result aggregation
│   └── edgeR_BEER.nf                # Optional edgeR/BEER analysis
├── bin/
│   ├── calc_scores_nofilter.py      # Virus score calculation
│   ├── fit-predict-zscore.py        # Z-score calculation
│   ├── generate-fasta.py            # Peptide FASTA generation
│   ├── merge-counts-stats.py        # Count/stats merging into phippery dataset
│   ├── phipseq_to_fhir.py           # PhIP-Seq to FHIR converter
│   ├── replicate-counts.py          # Replicate sequence count aggregation
│   ├── run_BEER.Rscript             # BEER Bayesian enrichment (optional)
│   ├── run_edgeR.Rscript            # edgeR differential enrichment (optional)
│   ├── validate-peptide-table.py    # Peptide table validation
│   └── validate-sample-table.py    # Sample table validation
├── templates/                       # Shell script templates (alignment, SAM processing)
└── data/                            # dataset (sample table, peptide table, FASTQs)

Input Data

Sample Table

A CSV file with one row per sample

Column	Description
sample_ID	Sample identifier
fastq_filepath	Path to the sample's FASTQ file
control_status	empirical, beads_only (≥2 required), or library

Peptide Table

A CSV file describing the phage display library.

Column	Description
oligo	Oligonucleotide sequence
Organism	Virus/organism name
Species	Species-level grouping
peptide	Amino acid sequence
pdb_id	PDB identifier for 3D visualization and neutralizing scoring

Sequencing Data

• Format: FASTQ (.fastq.gz or .fastq)
• Platform: Oxford Nanopore Technologies (ONT)
• Set params.fastq_stream_func = 'zcat' for gzipped files or 'cat' for uncompressed

Other Input Files

File	Parameter	Description
IEDB database	params.iedb_database	TSV from iedb.org for epitope annotation
PDB structures	params.pdb_dir	Directory of .pdb/.cif files for 3D visualization and neutralizing scoring
Neutralization DBs	params.neutralization_sars_db etc.	TSV files for neutralization scoring

Usage

Run with Example Data

nextflow run main.nf

Run with Custom Data

nextflow run main.nf \
  --sample_table /path/to/sample_table.csv \
  --peptide_table /path/to/peptide_table.csv \
  --results /path/to/results/

Run with Docker

nextflow run main.nf -profile docker

Deploy Streamlit Dashboard

After the pipeline completes:

cd results/streamlit_app/
chmod +x deploy_streamlit.sh
./deploy_streamlit.sh

Prototype

Support

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