KMA-cgMLST

flowchart TD
fastq[fastq]
scheme[scheme]
fastq --> fastp(fastp)
fastp -- trimmed_fastq --> kma_align(kma_align)
scheme --> kma_align
kma_align -- alignment_results --> kma_result_to_mlst(kma_result_to_mlst)
kma_result_to_mlst -- mlst --> count_called_alleles(count_called_alleles)

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Usage

Preparing your cgMLST scheme

First, prepare a single multi-fasta file containing all alleles. The pubmlst_client tool may be helpful for finding and downloading MLST schemes.

The fasta deflines of each allele should follow the format: locus_allele. eg:

>MYCO000001_1
CGATCGATGCTATACTAGG.....
>MYCO000001_2
CGATGCTTAGCGATCTACGT....

Index the fasta using kma:

kma index -i <your_scheme.fa> -o <your_scheme>

Running the pipeline

nextflow run BCCDC-PHL/kma-cgmlst \
  --fastq_input </path/to/fastqs> \
  [--min_identity <min_percent_identity>] \
  [--min_coverage <min_percent_coverage>] \
  --scheme </path/to/cgmlst_scheme> \
  --outdir </path/to/output_dir> 

The --min_identity and --min_coverage flags can be used to control the identity and coverage thresholds that are used to call an allele. They both default to 100% if the flags are omitted.

Alternatively, a samplesheet.csv file can be provided, with fields: ID,R1,R2:

ID,R1,R2
sample-01,/path/to/sample-01_R1.fastq.gz,/path/to/sample-01_R2.fastq.gz
sample-02,/path/to/sample-02_R1.fastq.gz,/path/to/sample-02_R2.fastq.gz
sample-03,/path/to/sample-03_R1.fastq.gz,/path/to/sample-03_R2.fastq.gz

When running the pipeline using samplesheet input, use the --samplesheet_input flag:

nextflow run BCCDC-PHL/kma-cgmlst \
  --samplesheet_input </path/to/samplesheet.csv> \
  --scheme </path/to/cgmlst_scheme> \
  --outdir </path/to/output_dir> 

If the --collect_outputs flag is added, several tabular outputs will be produced that include results for all samples included in the analysis. See Outputs below for more details.

Outputs

For each sample, the following outputs are produced:

.
├── SAMPLE-ID_YYYYMMDDHHmmss_provenance.yml
├── SAMPLE-ID_called_allele_count.csv
├── SAMPLE-ID_cgmlst.csv
├── SAMPLE-ID_fastp.csv [short read]
├── SAMPLE-ID_fastp.json [short read]
├── SAMPLE-ID_kma.csv
├── SAMPLE-ID_kma_mapstat.tsv
├── SAMPLE-ID_locus_qc.csv
└── SAMPLE-ID_nanoq.csv [long read]

Collected Outputs

If the --collect_outputs flag is used, the following additional outputs will be added to the top-level of the output directory (--outdir):

.
├── collected_called_allele_count.csv
├── collected_cgmlst.csv
└── collected_fastp.csv [short read]

The prefix of the filenames of the collected outputs can be controlled using the --collected_outputs_prefix flag.

For example, the following command:

nextflow run BCCDC-PHL/kma-cgmlst \
  --fastq_input </path/to/fastqs> \
  --scheme </path/to/cgmlst_scheme> \
  --collect_outputs \
  --collected_outputs_prefix "demo" \
  --outdir </path/to/output_dir> 

...results in the following filenames fort the collected outputs:

.
├── demo_called_allele_count.csv
├── demo_cgmlst.csv
└── demo_fastp.csv

Provenance

In the output directory for each sample, a provenance file will be written with the following format:

- pipeline_name: BCCDC-PHL/kma-cgmlst
  pipeline_version: 0.1.3
  nextflow_session_id: ee5b4986-6ada-4eab-a294-ed0cbb18427d
  nextflow_run_name: furious_murdock
  analysis_start_time: 2024-02-01T16:37:26.062501-08:00
- input_filename: SAMPLE-ID_S133_L001_R1_001.fastq.gz
  file_type: fastq-input
  sha256: 1b6a9a616ec3fd8432ff02f51d60fb6443617c29761b96234ede9c65efe06547
- input_filename: SAMPLE-ID_S133_L001_R2_001.fastq.gz
  file_type: fastq-input
  sha256: f6954b1a174fbead8a035ae7cdfda549fcc751be8847a330505df49de59bed96
- process_name: fastp
  tools:
    - tool_name: fastp
      tool_version: 0.20.1
      parameters:
        - parameter: --cut_tail
          value: null
- process_name: kma_align
  tools:
    - tool_name: kma
      tool_version: 1.3.5
      parameters:
        - parameter: -ef
          value: null
        - parameter: -cge
          value: null
        - parameter: -boot
          value: null
        - parameter: -1t1
          value: null
        - parameter: -mem_mode
          value: null
        - parameter: -t_db
          value: /path/to/scheme/used
        - parameter: -and
          value: null