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)
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>
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.
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]
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
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