FastMatch IRIDA Workflow

This workflow takes query and reference JSON-formatted MLST profiles and reports query-reference pairs that are sufficiently within a specified distance of each other.

A brief overview of the usage of this pipeline is given below. Further documentation can be found in the docs directory.

Input

The input to the pipeline is a standard sample sheet (passed as --input samplesheet.csv) that looks like:

| sample | fastmatchcategory | mlstalleles | metadata1 | metadata2 | metadata3 | metadata4 | metadata5 | metadata6 | metadata7 | metadata8 | metadata9 | metadata10 | metadata11 | metadata12 | metadata13 | metadata14 | metadata15 | metadata16 | | ------- | ------------------ | ----------------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ----------- | ----------- | ----------- | ----------- | ----------- | ----------- | ----------- | | SampleA | query | sampleA.mlst.json | meta1 | meta2 | meta3 | meta4 | meta5 | meta6 | meta7 | meta8 | meta9 | meta10 | meta11 | meta12 | meta13 | meta14 | meta15 | meta16 | | SampleB | reference | sampleB.mlst.json | meta1 | meta2 | meta3 | meta4 | meta5 | meta6 | meta7 | meta8 | meta9 | meta10 | meta11 | meta12 | meta13 | meta14 | meta15 | meta16 |

Note that each sample must be defined as a query or reference. Samples designated with query will have their distance calculated to every sample in the sample sheet (query and reference samples), whereas reference-reference sample pairings do not have their distances calculated or reported.

The structure of this file is defined in assets/schema_input.json. Validation of the sample sheet is performed by nf-validation. Details on the columns can be found in the Full Samplesheet documentation.

Irida Next Optional Sample Name Configuration

fastmatchirida accepts the IRIDA Next format for samplesheets which can contain an additional column: sample_name

sample_name: An optional column, that overrides sample for outputs (filenames and sample names) and reference assembly identification.

sample_name allows more flexibility in naming output files or sample identification. Unlike sample, sample_name is not required to contain unique values. Nextflow requires unique sample names, and therefore in the instance of repeat sample_names, sample will be suffixed to any sample_name. Non-alphanumeric characters (excluding _,-,.) will be replaced with "_".

Parameters

The main parameters are --threshold, --input as defined above and --output for specifying the output results directory. You may wish to provide -profile singularity to specify the use of singularity containers and -r [branch] to specify which GitHub branch you would like to run.

• --threshold the minimum alleles for filtering matches (default: 50)

Metadata

In order to customize metadata headers, the parameters --metadata_1_header through --metadata_16_header may be specified. These parameters are used to re-name the headers in the final metadata table from the defaults (e.g., rename metadata_1 to country).

LOCIDEX

When large samplesheets are provided to LOCIDEX, they are split-up, into batches (default batch size: 100), to allow for LOCIDEX_MERGE to be run in parallel. To modify the size of batches use the parameter --batch_size n

Distance Threshold

A distance threshold parameter may be used to constrain the maximum distances between reported sample pairs in the final reports. This can be accomplished by specifying --threshold DISTANCE, where DISTANCE is a non-negative integer when using Hamming distances or a float between [0.0, 100.0] when using scaled distances. See below for more information on these distance methods.

Distance Methods

The distance measurement used can be one of two methods: Hamming or scaled.

Hamming Distances

Hamming distances are integers representing the number of differing loci between two sequences and will range between [0, n], where n is the total number of loci. When using Hamming distances, you must specify --pd_distm hamming.

Scaled Distances

Scaled distances are floats representing the percentage of differing loci between two sequences and will range between [0.0, 100.0]. When using scaled distances, you must specify --pd_distm scaled.

profile_dists

The following can be used to adjust parameters for the profile_dists tool.

• --pd_distm: The distance method/unit, either hamming or scaled. For hamming distances, the distance values will be a non-negative integer. For scaled distances, the distance values are between 0.0 and 100.0. Please see the Distance Method section for more information.
• --pd_missing_threshold: The maximum proportion of missing data per locus for a locus to be kept in the analysis. Values from 0.0 to 1.0.
• --pd_sample_quality_threshold: The maximum proportion of missing data per sample for a sample to be kept in the analysis. Values from 0.0 to 1.0.
• --pd_file_type: Output format file type. One of text or parquet.
• --pd_mapping_file: A file used to map allele codes to integers for internal distance calculations. Normally, this is unneeded unless you wish to override the automated process of mapping alleles to integers.
• --pd_skip: Skip QA/QC steps. Can be used as a flag, --pd_skip, or passing a boolean, --pd_skip true or --pd_skip false.
• --pd_columns: Defines the loci to keep within the analysis (default when unset is to keep all loci). Formatted as a single column file with one locus name per line. For example:
  • Single column format loci1 loci2 loci3
• --pd_count_missing: Count missing alleles as different. Can be used as a flag, --pd_count_missing, or passing a boolean, --pd_count_missing true or --pd_count_missing false. If true, will consider missing allele calls for the same locus between samples as a difference, increasing the distance counts.

Other

Other parameters (defaults from nf-core) are defined in nextflow_schema.json.

Running

To run the pipeline, please do:

bash nextflow run phac-nml/fastmatchirida -profile singularity -r main -latest --input https://github.com/phac-nml/fastmatchirida/raw/dev/assets/samplesheet.csv --outdir results

Where the samplesheet.csv is structured as specified in the Input section.

Output

A JSON file for loading metadata into IRIDA Next is output by this pipeline. The format of this JSON file is specified in our Pipeline Standards for the IRIDA Next JSON. This JSON file is written directly within the --outdir provided to the pipeline with the name iridanext.output.json.gz (ex: [outdir]/iridanext.output.json.gz).

An example of the what the contents of the IRIDA Next JSON file looks like for this particular pipeline is as follows:

``` { "files": { "global": [ { "path": "pipelineinfo/softwareversions.yml" }, { "path": "process/results.xlsx" }, { "path": "process/results.tsv" }, { "path": "distances/profiledists.run.json" }, { "path": "distances/profiledists.results.tsv" }, { "path": "distances/profiledists.refprofile.tsv" }, { "path": "distances/profiledists.queryprofile.tsv" }, { "path": "distances/profiledists.allelemap.json" }, { "path": "locidex/concat/query/MLSTerrorreportconcatquery.csv" }, { "path": "locidex/concat/reference/MLSTerrorreportconcatref.csv" } ], "samples": {

    }
},
"metadata": {
    "samples": {

    }
}

} ```

Within the files section of this JSON file, all of the output paths are relative to the outdir. Therefore, "path": "process/results.xlsx" refers to a file located within outdir/process/results.xlsx.

Details on the individual output files can be found in the Output Documentation.

Test Profile

To run with the test profile, please do:

bash nextflow run phac-nml/fastmatchirida -profile docker,test -r main -latest --outdir results

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