This workflow takes provided JSON-formatted MLST allelic profiles and assigns cluster addresses to samples based on an existing cluster designations. This pipeline is designed to be integrated into IRIDA Next. However, it may be run as a stand-alone pipeline.
A brief overview of the usage of this pipeline is given below. Detailed documentation can be found in the docs/ directory.
The input to the pipeline is a standard sample sheet (passed as --input samplesheet.csv) that looks like:
| sample | mlst_alleles | address |
|---|---|---|
| sampleA | sampleA.mlst.json | 1.1.1 |
| sampleQ | sampleQ.mlst.json | |
| sampleF | sampleF.mlst.json |
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.
gasnomenclature 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 "_".
An example samplesheet has been provided with the pipeline.
The main parameters are --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.
Profile_Dists and the Genomic Address Service workflows can use two distance methods: hamming or scaled.
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 and provide Hamming distance thresholds as integers between [0, n]: --gm_thresholds "10,5,0" (10, 5, and 0 loci).
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 and provide percentages between [0.0, 100.0] as thresholds: --gm_thresholds "50,20,0" (50%, 20%, and 0% of loci).
The --gm_thresholds parameter sets thresholds for each cluster level, which dictate how sequences are assigned cluster codes. These thresholds specify the maximum allowable differences in loci between sequences sharing the same cluster code at each level. The consistency of these thresholds in ensuring uniform cluster codes across levels depends on the --gm_method parameter, which determines the linkage method used for clustering.
-
Complete Linkage: When using complete linkage clustering, sequences are grouped such that identical cluster codes at a particular level guarantee that all sequences in that cluster are within the specified threshold distance. For example, specifying
--pd_distm hammingand--gm_thresholds "10,5,0"would mean that sequences with no more than 10 loci differences are assigned the same cluster code at the first level, no more than 5 differences at the second level, and identical sequences at the third level. -
Average Linkage: With average linkage clustering, sequences may share the same cluster code if their average distance is below the specified threshold. For instance, sequences with average distances less than 10, 5, and 0 for each level respectively may share the same cluster code.
-
Single Linkage: Single linkage clustering can result in merging distant samples into the same cluster if there exists a third sample that bridges the distance between them. This method does not provide strict guarantees on the maximum distance within a cluster, potentially allowing distant sequences to share the same cluster code.
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 and Thresholds 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 to 1.--pd_sample_quality_threshold: The maximum proportion of missing data per sample for a sample to be kept in the analysis. Values from 0 to 1.--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. This is the same file as produced from the profile dists step (the allele_map.json file). 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 trueor--pd_skip false.--pd_columns: Path to a file that 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
- Single column format
--pd_count_missing: Count missing alleles as different. Can be used as a flag,--pd_count_missing, or passing a boolean,--pd_count_missing trueor--pd_count_missing false. If true, will consider missing allele calls for the same locus between samples as a difference, increasing the distance counts.
The following can be used to adjust parameters for the [gas call][] tool.
--gm_thresholds: Thresholds delimited by,. Values should match units from--pd_distm(either hamming or scaled). Please see the Distance Method and Thresholds section for more information.--gm_method: The linkage method to use for clustering. Value should be one of single, average, or complete.--gm_delimiter: Delimiter desired for nomenclature code. Must be alphanumeric or one of._-. Must be the same delimeter as samplesheet and cluster address database.
In addition to the reference samples included in the input samplesheet (which already contain pre-computed cluster addresses), users can incorporate additional pre-computed reference profiles and cluster addresses by providing them as parameterized databases. Note that any address levels present in the additional databases but absent from the input samplesheet addresses will be disregarded.
--db_profiles: Specifies the path to the database containing pre-merged MLST profiles in tab-separated format. To ensure compatibility, the database structure must adhere to the expected header format corresponding to the samples included in the input samplesheet:
| sample_id | l1 | l2 | ... | ln |
|---|---|---|---|---|
| sampleA | 1 | 1 | ... | 1 |
| sampleB | 1 | 1 | ... | 2 |
| sampleC | 2 | 1 | ... | 1 |
--db_clusters: Specifies the path to the database containing cluster addresses for additional samples in tab-separated format. To ensure compatibility, the database structure must adhere to the expected header format corresponding to the samples included in the input samplesheet:
| id | address |
|---|---|
| sampleA | 1.1.1 |
| sampleB | 1.1.2 |
| sampleC | 2.1.1 |
Note: To add additional reference samples to the pipeline, both --db_profiles and --db_clusters must be provided together, and all sample_id's in --db_profiles must match the id's in --db_clusters
Other parameters (defaults from nf-core) are defined in nextflow_schema.json.
To run the pipeline, please do:
nextflow run phac-nml/gasnomenclature -profile singularity -r main -latest --input assets/samplesheet.csv --outdir resultsWhere the samplesheet.csv is structured as specified in the Input section.
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": [],
"samples": {
"sampleF": [
{
"path": "input/sampleF_error_report.csv"
}
],
}
},
"metadata": {
"samples": {
"sampleQ": {
"address": "1.1.3",
}
}
}
}
Within the files section of this JSON file, all of the output paths are relative to the outdir. Therefore, "path": "input/sampleF_error_report.csv" refers to a file located within outdir/input/sampleF_error_report.csv. This file is generated only if a sample fails the input check during samplesheet assessment.
To run with the test profile, please do:
nextflow run phac-nml/gasnomenclature -profile docker,test -r main -latest --outdir resultsCopyright 2024 Government of Canada
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