gnomonicus 2.5.3

Python code to integrate results of tb-pipeline and provide an antibiogram, mutations and variants

gnomonicus

Python code to integrate results of tb-pipeline and provide an antibiogram, mutations and variations

Provides a library of functions for use within scripts, as well as a CLI tool for linking the functions together to produce output

Documentation

API reference for developers, and CLI instructions can be found here: https://oxfordmmm.github.io/gnomonicus/

Usage

usage: gnomonicus [-h] --vcf_file VCF_FILE --genome_object GENOME_OBJECT [--catalogue_file CATALOGUE_FILE] [--ignore_vcf_filter] [--progress] [--output_dir OUTPUT_DIR] [--json] [--fasta FASTA] [--minor_populations MINOR_POPULATIONS]

options:
  -h, --help            show this help message and exit
  --vcf_file VCF_FILE   the path to a single VCF file
  --genome_object GENOME_OBJECT
                        the path to a compressed gumpy Genome object or a genbank file
  --catalogue_file CATALOGUE_FILE
                        the path to the resistance catalogue
  --ignore_vcf_filter   whether to ignore the FILTER field in the vcf (e.g. necessary for some versions of Clockwork VCFs)
  --progress            whether to show progress using tqdm
  --output_dir OUTPUT_DIR
                        Directory to save output files to. Defaults to wherever the script is run from.
  --json                Flag to create a single JSON output as well as the CSVs
  --fasta FASTA         Use to output a FASTA file of the resultant genome. Specify either 'fixed' or 'variable' for fixed length and variable length FASTA respectively.
  --minor_populations MINOR_POPULATIONS
                        Path to a line separated file containing genome indices of minor populations.

Helper usage

As the main script can utilise pickled gumpy.Genome objects, there is a supplied helper script. This converts a Genbank file into a pickled gumpy.Genome for significant time saving. Due to the security implications of the pickle module, DO NOT SEND/RECEIVE PICKLES. This script should be used on a host VM before running nextflow to avoid reinstanciation. Supports gzip compression to reduce file size significantly (using the --compress flag).

usage: gbkToPkl FILENAME [--compress]

Install

Simple install using pip for the latest release

pip install gnomonicus

Install from source

git clone https://github.com/oxfordmmm/gnomonicus.git
cd gnomonicus
pip install -e .

Docker

A Docker image should be built on releases. To open a shell with gnomonicus installed:

docker run -it oxfordmmm/gnomonicus:latest

Updating

If a gnomonicus update changes the version of gumpy used, an OutdatedGumpyException will be thrown if using a pickled Genome object which was instantiated with the old version. This can be fixed by simply re-instantiating the Genome object by passing a genbank file once.

Notes

When generating mutations, in cases of synonymous amino acid mutation, the nucelotides changed are also included. This can lead to a mix of nucleotides and amino acids for coding genes, but these are excluded from generating effects unless specified in the catalogue. This means that the default rule of gene@*= --> S is still in place regardless of the introduced gene@*? which would otherwise take precedence. For example:

  'MUTATIONS': [
      {
          'MUTATION': 'F2F',
          'GENE': 'S',
          'GENE_POSITION': 2
      },
      {
          'MUTATION': 't6c',
          'GENE': 'S',
          'GENE_POSITION': 6
      },
  ],
  'EFFECTS': {
      'AAA': [
          {
              'GENE': 'S',
              'MUTATION': 'F2F',
              'PREDICTION': 'S'
          },
          {
              'PHENOTYPE': 'S'
          }
      ],

The nucelotide variation is included in the the MUTATIONS, but explictly removed from the EFFECTS unless it is specified within the catalogue. In order for this variation to be included, a line in the catalogue of S@F2F&S@t6c would have to be present.

User stories

1. As a bioinformatician, I want to be able to run gnomonicus on the command line, passing it (i) a GenBank file (or pickled gumpy.Genome object), (ii) a resistance catalogue and (iii) a VCF file, and get back pandas.DataFrames of the genetic variants, mutations, effects and predictions/antibiogram. The latter is for all the drugs described in the passed resistance catalogue.

2. As a GPAS developer, I want to be able to embed gnomonicus in a Docker image/NextFlow pipeline that consumes the outputs of tb-pipeline and emits a structured, well-designed JSON object describing the genetic variants, mutations, effects and predictions/antibiogram.

3. In general, I would also like the option to output fixed- and variable-length FASTA files (the latter takes into account insertions and deletions described in any input VCF file).

Unit testing

For speed, rather than use NC_000962.3 (i.e. H37Rv M. tuberculosis), we shall use SARS-CoV-2 and have created a fictious drug resistance catalogue, along with some vcf files and the expected outputs in tests/.

These can be run with pytest -vv