mobile-gene-regions

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mobile-gene-regions

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Analysing the genomic context of mobile genes

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# Visualizing and quantifying structural diversity around mobile AMR genes

__Liam P. Shaw__

> [!WARNING]
> 21 July 2025: since this repository was written in late 2023, pangraph has undergone a major migration between v0 and v1. This repository was written to    work with pangraph v0.7.0 and has not been updated for the newer versions of pangraph. A test version for pangraph v1.2.0 is available on the `pangraph_1.2.0` branch. This has not been validated, but seems to work on the GES-1 test dataset with all aspects apart from plotting positional entropies, which have     been removed temporarily from the Snakefile.

This repository contains a pipeline to analyse the flanking regions of genes using [pangraph](https://github.com/neherlab/pangraph).

It was developed for the analysis of mobile AMR genes, but in principle can be used for any input gene.

For more details, see the paper:

Visualizing and quantifying structural diversity around mobile AMR genes  
Liam P. Shaw and Richard A. Neher, *biorxiv* (2023)  
doi: [10.1101/2023.08.07.551646](https://doi.org/10.1101/2023.08.07.551646)

### Installation

The pipeline requires [pangraph](https://github.com/neherlab/pangraph) to be installed first. Currently it uses the `feat/unbalanced-tree` branch which can be installed with:

```
git clone -b feat/unbalanced-tree https://github.com/neherlab/pangraph.git && cd pangraph
```

Then follow the installation instructions from the pangraph github as normal. Note that because pangraph was written in a specific version of Julia, you'll currently need to install **Julia v1.7.2** which you can download [here](https://julialang.org/downloads/oldreleases/).

Then you will need to clone this repository:

```
git clone https://github.com/liampshaw/mobile-gene-regions.git
```

Other dependencies can be installed with conda or mamba (recommended: mamba). From inside this directory (`cd mobile-gene-regions`):

```
mamba env create -f flanking-regions.yaml # Note that snakemake v7 is required, not v8
conda activate flanking-regions
```

There are also R packages required for some plots. (If you don't have R, you can download it [here](https://www.r-project.org/).) These R packages can be installed with a helper script in this repository:

```
# cd mobile-gene-regions
Rscript scripts/install_libraries.R
```

If you have followed all of these steps, you should have the pipeline installed and ready to run.

### Test

You can test whether it's working with data in the `test_data` directory (33 sequences containing GES-1 variants), running the following command from within the cloned repository:

```
python analyze-flanking-regions.py \
	--contigs test_data/GES-1_contigs.fa \
	--gene_fasta test_data/GES-1.fa \ 
	--output test_data_output \
	--gff test_data/GES-1_annotations.gff \
	--focal_gene_name GES-1	
```

If this runs well, you should be able to inspect the plots in `test_data_output/plots`. If it doesn't, the installation may not have worked.

### Usage

Basic usage (run from within this directory):

```
python analyze-flanking-regions.py \
        --contigs contig_fasta.fa \
        --gene_fasta focal_gene.fa \
        --focal_gene_name gene_name \
        --output_dir output
```

The underlying pipeline has flexible parameters such as the size of the flanking region, the number of single nucleotide variants (SNVs) allowed in the focal gene, and pangraph parameters:

```
options:
  -h, --help            show this help message and exit
  --contigs CONTIGS     fasta with contigs containing central/focal gene
  --gene_fasta GENE_FASTA
                        fasta with nucleotide sequence of focal gene
  --focal_gene_name FOCAL_GENE_NAME
                        name of focal gene (NOTE: if using gffs, must match name of protein
                        product e.g. IMP-4 not blaIMP-4)
# Optional parameters
  --flanking_region FLANKING_REGION
                        size of flanking region (N.B. currently symmetrical
                        upstream/downstream). Default: 5000
  --output_dir OUTPUT_DIR
                        output directory. Default: output
  --force               whether to overwrite existing input files. Default: False
  --gff GFF             file with gff annotations for contigs. Default: none
  --panx_export         whether to export panX output from pangraph. Default: False
  --bandage             whether to run Bandage on pangraph. Default: False
  --snv_threshold SNV_THRESHOLD
                        Nucleotide-level difference threshold for focal gene (1 SNV = 1
                        diff, Xbp indel = X diffs). Default: 25
  --gene_length_threshold GENE_LENGTH_THRESHOLD
                        Lateral coverage required of focal gene (0-1). Default: 0.99
  --pangraph_polish     whether to polish the pangraph. Default: False
  --pangraph_aligner {minimap2,mmseqs}
                        aligner to use for building pangraph. Default: minimap2
  --pangraph_seed PANGRAPH_SEED
                        random seed for pangraph (for reproducibility). Default: 0
  --pangraph_alpha PANGRAPH_ALPHA
                        value of alpha parameter for pangraph. Default: 100
  --pangraph_beta PANGRAPH_BETA
                        value of beta parameter for pangraph. Default: 10
  --pangraph_dist_backend {native,mash}
                        distance backend for calculation of pangraph. Default: native
  --pangraph_minblocklength PANGRAPH_MINBLOCKLENGTH
                        minimum block length for pangraph. Default: 100
  --pangraph_edgeminlength PANGRAPH_EDGEMINLENGTH
                        minimum edge length for pangraph when exporting gfa. Default: 0
  --breakpoint_minimap2
                        whether to also calculate breakpoint distances using minimap2.
                        Default: False                        
```

`analyze-flanking-regions.py` is just a helper script that creates a config file and calls the pipeline for a single gene. However, the pipeline is written in [snakemake](https://snakemake.readthedocs.io/en/stable/index.html), so as to potentially enable analysis of multiple genes in a single command once the input files have been formatted correctly and put in the `input` directory.

Outputs for a given gene are saved in `{output_dir}/{focal_gene_name}`. They include output data files as well as interactive html plots (generated with [altair](https://altair-viz.github.io/) in Python). N.B. In the manuscript we talk about 'uninterrupted shared distance' - this is referred to as 'breakpoint distance' in the code and documentation, but it is the same quantity.

A tutorial on how to prepare data and use the pipeline to explore flanking regions is available in `tutorial/Tutorial.md`.

### Beta-lactamases example

The analysis presented in the paper for twelve beta-lactamases uses the version of the pipeline in `beta-lactamases`. The plots generated can be seen at [https://liampshaw.github.io/flanking-regions/](https://liampshaw.github.io/flanking-regions/).

mobile-gene-regions

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