mlst-nf

A nextflow pipeline for running mlst on a set of assemblies.

flowchart TD
  assembly --> quast(quast)
  quast --> assembly_qc
  assembly --> mlst(mlst)
  mlst --> mlst.json
  mlst --> parse_alleles(parse_alleles)
  parse_alleles --> alleles.csv
  parse_alleles --> sequence_type.csv

Loading

Usage

nextflow run BCCDC-PHL/mlst-nf \
  --assembly_input </path/to/assemblies> \
  --outdir </path/to/outdir>

The pipeline also supports a 'samplesheet input' mode. Pass a samplesheet.csv file with the headers ID, ASSEMBLY:

nextflow run BCCDC-PHL/mlst-nf \
  --samplesheet_input </path/to/samplesheet.csv> \
  --outdir </path/to/outdir>

Outputs

Outputs for each sample will be written to a separate directory under the output directory, named using the sample ID.

The following output files are produced for each sample.

sample-01
├── sample-01_20211202154752_provenance.yml
├── sample-01_alleles.csv
├── sample-01_mlst.json
└── sample-01_sequence_type.csv

The mlst.json output is generated directly by the mlst tool. It has the following format:

[
   {
      "scheme" : "sepidermidis",
      "alleles" : {
         "mutS" : "1",
         "yqiL" : "1",
         "tpiA" : "1",
         "pyrR" : "2",
         "gtr" : "2",
         "aroE" : "1",
         "arcC" : "16"
      },
      "sequence_type" : "184",
      "filename" : "test/example.gbk.gz",
      "id" : "test/example.gbk.gz"
   }
]

The alleles.csv file is generated based on the .json output, and includes a couple of boolean (True/False) fields to indicate whether the allele is a perfect match, or if it is a novel allele, based on the presence of ? or ~ characters in the allele calls, as described here.

The per-locus score field is computed based on the rules described here.

The fields in in the alleles.csv output are:

sample_id
scheme
locus
allele
perfect_match
novel_allele
score

The sequence_type.csv file includes an overall sequence type ID based on the allele calls for each locus, and the overall score, which is simply the sum of the per-locus scores for the sample.

sample_id
scheme
sequence_type
score

Provenance

Each analysis will create a provenance.yml file for each sample. The filename of the provenance.yml file includes a timestamp with format YYYYMMDDHHMMSS to ensure that a unique file will be produced if a sample is re-analyzed and outputs are stored to the same directory.

- pipeline_name: BCCDC-PHL/mlst-nf
  pipeline_version: 0.1.4
  nextflow_session_id: f18b89aa-06f7-41e4-b016-3519dfd5a5cb
  nextflow_run_name: sharp_bhaskara
  timestamp_analysis_start: 2024-02-20T22:59:37.862710
- input_filename: NC-000913.3.fa
  input_path: /home/runner/work/mlst-nf/mlst-nf/.github/data/assemblies/NC-000913.3.fa
  sha256: 6b195feda4c66140f6762742eb8b30c2652f02b45878b174f5b00ef85ecc95d7
- process_name: mlst
  tools:
    - tool_name: mlst
      tool_version: 2.16.1
      parameters:
      - parameter: minid
        value: 95
      - parameter: mincov
        value: 10
      - parameter: minscore
        value: 50
- process_name: quast
  tools:
    - tool_name: quast
      tool_version: 5.0.2
      parameters:
        - parameter: --space-efficient
          value: null
        - parameter: --fast
          value: null
        - parameter: --min-contig
          value: 0