phac-nml/metadatatransformation: Usage

September 23, 2025 · View on GitHub

Introduction

This pipeline transforms metadata from IRIDA Next.

You will need to create a sample sheet with information about the samples you would like to analyse before running the pipeline. Use this parameter to specify its location. It has to be a comma-separated file with 10 columns, and a header row as shown in the examples below.

--input '[path to samplesheet file]'

Full samplesheet

The input samplesheet must contain the following columns: sample, and metadata_1 through metadata_16. The IDs within a samplesheet should be unique. You may optionally provide a sample_name column, which will replace the Irida Next IDs in the sample column if available. All other columns will be ignored.

A final samplesheet file contain the sample_name column may look something like the one below.

sample,sample_name,metadata_1,metadata_2,metadata_3,metadata_4,metadata_5,metadata_6,metadata_7,metadata_8
sample1,"ABC",1.1,1.2,1.3,1.4,1.5,1.6,1.7,1.8
sample2,"DEF",2.1,2.2,2.3,2.4,2.5,2.6,2.7,2.8
sample3,"GHI",3.1,3.2,3.3,3.4,3.5,3.6,3.7,3.8

Column	Description
`sample`	Sample ID. Samples should be unique within a samplesheet. Likely Irida Next IDs.
`sample_name`	Sample name. Likely user-provided IDs that should be unique, but are not required to be unique. Will be used over `sample` when available.
`metadata_1..16`	Metadata that will be used in the metadata transformations.

An example samplesheet has been provided with the pipeline.

Running the pipeline

The typical command for running the pipeline is as follows:

nextflow run phac-nml/metadatatransformation -profile singularity -r main -latest --input assets/samplesheet.csv --outdir results --transformation lock

This will launch the pipeline with the singularity configuration profile. See below for more information about profiles.

Note that the pipeline will create the following files in your working directory:

work                # Directory containing the nextflow working files
<OUTDIR>            # Finished results in specified location (defined with --outdir)
.nextflow_log       # Log file from Nextflow
# Other nextflow hidden files, eg. history of pipeline runs and old logs.

If you wish to repeatedly use the same parameters for multiple runs, rather than specifying each flag in the command, you can specify these in a params file.

Pipeline settings can be provided in a yaml or json file via -params-file <file>.

Do not use -c <file> to specify parameters as this will result in errors. Custom config files specified with -c must only be used for tuning process resource specifications, other infrastructural tweaks (such as output directories), or module arguments (args).

Lock

The lock transformation may be run as follows:

nextflow run phac-nml/metadatatransformation -profile singularity -r main -latest --input assets/samplesheet.csv --outdir results --transformation lock

You may wish to specify the --metadata_1_header through --metadata_16_header parameters to ensure the metadata is named as desired. These metadata headers are automatically converted to lowercase.

nextflow run phac-nml/metadatatransformation -profile singularity -r main -latest --input assets/samplesheet.csv --outdir results --transformation lock --metadata_1_header country --metadata_2_header outbreak

Age

The calculate age transformation may be run as follows:

nextflow run phac-nml/metadatatransformation -profile singularity --input tests/data/samplesheets/age/success_failure_mix.csv --outdir results --transformation age --metadata_1_header "date_of_birth" --metadata_2_header "collection_date" --age_header "age_at_collection"

For this transformation, the metadata_1 column of the sample sheet is understood as the date of birth and the metadata_2 column is understood as the date at which to calculate the age. The metadata headers are automatically converted to lowercase.

The following parameters can be used to rename CSV-generated output columns and Irida Next fields as follows:

--metadata_1_header: names the date of birth column header
--metadata_2_header: names the current/target data column header
--age_header: names the calculated age column header and related output columns

Age PNC

The calculate age PNC transformation may be run as follows:

nextflow run phac-nml/metadatatransformation -profile singularity --input tests/data/samplesheets/age/basic.csv --outdir results --transformation age_pnc --age_header calc_host_age --metadata_1_header host_date_of_birth_dob --metadata_2_header calc_earliest_date --metadata_3_header host_age --metadata_4_header host_age_unit

The metadata header parameters (--metadata_1_header through --metadata_16_header) are required for the transformation. In particular, at least four of the metadata headers must be renamed to the exactly the following:

host_date_of_birth_dob
calc_earliest_date
host_age
host_age_unit

For example, if the 2nd metadata column corresponds to the date of birth, then it must be parameterized as follows: --metadata_1_header host_date_of_birth_dob. If the 5th metadata column of the input corresponds to the age unit, then it must be parameterized as follows: --metadata_5_header host_age_unit. The order of the metadata columns in the input does not matter, as long as the names are assigned correctly as above. The metadata headers are automatically converted to lowercase.

The age metadata column in the output can be renamed as follows, but this is not recommended as the expected age metadata column name is exactly calc_host_age (the default):

--age_header: names the calculated age column header and related output columns

For simplicity, the the following assumptions are made when calculating ages:

365 days in a year
52 weeks in a year
12 months in a year
ages cannot be less than or equal to 0
ages cannot be greater than 150

Furthermore, the following values are ignored and treated as "years" when provided as an age unit: Not Applicable, Missing, Not Collected, Not Provided, Restricted Access, (blank). For example, this means that an age number of 10 and an age unit of Restricted Access will report an age of 10 years old.

Earliest

The earliest date transformation may be run as follows:

nextflow run phac-nml/metadatatransformation -profile singularity --input tests/data/samplesheets/earliest/basic.csv --outdir results --transformation earliest

For this transformation, the metadata_1 column through metadata_16 column of the sample sheet are understood as containing a date or being empty. The transformation will determine the earliest date among these metadata columns.

You may wish to specify the --metadata_1_header through --metadata_16_header parameters to provide appropriate column names in the results.csv file, but these headers do not affect results returned to IRIDA Next.

If at least one metadata column contains non-empty data that does not conform to the expected "YYYY-MM-DD" date format, then the sample will report an error and no date will be reported for the sample (even if one other valid date appears among the invalid metadata for that sample). However, the following special entries are ignored when calculating the earliest age (they are not considered malformed data): Not Applicable, Missing, Not Collected, Not Provided, Restricted Access, (blank)

Populate

The populate transformation may be run as follows:

nextflow run phac-nml/metadatatransformation -profile singularity --input tests/data/samplesheets/populate/basic.csv --outdir results --transformation populate --populate_header "new_header" --populate_value "new_value"

For this transformation, all input metadata (metadata_1 through metadata_16) will be ignored. However, the transformation will write or overwrite the input metadata as specified by --populate_header. The value specified by --populate_value will be written for every sample under the column specified by --populate_header.

For example, when running the transformation with the following sample sheet:

sample,sample_name,metadata_1,metadata_2,metadata_3,metadata_4,metadata_5,metadata_6,metadata_7,metadata_8
sample1,"ABC",1.1,1.2,1.3,1.4,1.5,1.6,1.7,1.8
sample2,"DEF",2.1,2.2,2.3,2.4,2.5,2.6,2.7,2.8
sample3,"GHI",3.1,3.2,3.3,3.4,3.5,3.6,3.7,3.8

and command:

nextflow run phac-nml/metadatatransformation -profile singularity --input tests/data/samplesheets/populate/basic.csv --outdir results --transformation populate --populate_header "new_header" --populate_value "new_value"

following output results.csv file will be generated:

sample,sample_name,metadata_1,metadata_2,metadata_3,metadata_4,metadata_5,metadata_6,metadata_7,metadata_8,new_header
sample1,ABC,1.1,1.2,1.3,1.4,1.5,1.6,1.7,1.8,new_value
sample2,DEF,2.1,2.2,2.3,2.4,2.5,2.6,2.7,2.8,new_value
sample3,GHI,3.1,3.2,3.3,3.4,3.5,3.6,3.7,3.8,new_value

and the following transformation.csv file (written back to IRIDA Next) will be generated:

sample,new_header
sample1,new_value
sample2,new_value
sample3,new_value

Categorize

The categorize transformation may be run as follows:

nextflow run phac-nml/metadatatransformation -profile singularity --input tests/data/samplesheets/categorize/basic.csv --outdir results --transformation categorize --metadata_1_header host_scientific_name --metadata_2_header host_common_name  --metadata_3_header food_product --metadata_4_header environmental_site  --metadata_5_header environmental_material

For this transformation, a new field "calc_source_type" will be assigned based on the values of other fields in the input metadata.

For example, when running the transformation with the following sample sheet:

sample,sample_name,metadata_1,metadata_2,metadata_3,metadata_4,metadata_5
sample1,"A",Homo sapiens (Human),Human NCBITaxon:9606,,,
sample2,"B",,dog,,,
sample3,"C",,,eggs,,
sample4,"D",,,,farm,wastewater
sample5,"E",,,,,
sample6,"F",Homo sapiens (Human),dog,,,
sample7,"G",Homo sapiens (Human),,,,
sample8,"H",,Human NCBITaxon:9606,,,
sample9,"J",Homo sapiens (Human),Human NCBITaxon:9606,eggs,farm,wastewater
sample10,"K",,dog,eggs,,
sample11,"L",,,eggs,farm,
sample12,"M",,,eggs,,wastewater

following output results.csv file will be generated:

sample,sample_name,host_scientific_name,host_common_name,food_product,environmental_site,environmental_material,calc_source_type,calc_source_type_valid,calc_source_type_error
sample1,A,Homo sapiens (Human),Human NCBITaxon:9606,,,,Human,True,
sample2,B,,dog,,,,Animal,True,
sample3,C,,,eggs,,,Food,True,
sample4,D,,,,farm,wastewater,Environmental,True,
sample5,E,,,,,,Unknown,True,
sample6,F,Homo sapiens (Human),dog,,,,Host Conflict,True,
sample7,G,Homo sapiens (Human),,,,,Human,True,
sample8,H,,Human NCBITaxon:9606,,,,Human,True,
sample9,J,Homo sapiens (Human),Human NCBITaxon:9606,eggs,farm,wastewater,Human,True,
sample10,K,,dog,eggs,,,Animal,True,
sample11,L,,,eggs,farm,,Food,True,
sample12,M,,,eggs,,wastewater,Food,True,

and the following transformation.csv file (written back to IRIDA Next) will be generated:

sample,calc_source_type
sample1,Human
sample2,Animal
sample3,Food
sample4,Environmental
sample5,Unknown
sample6,Host Conflict
sample7,Human
sample8,Human
sample9,Human
sample10,Animal
sample11,Food
sample12,Food

NOTE This is expecting specific metadata fields (host_scientific_name, host_common_name, food_product, environmental_site and environmental_material). It will error out if they're not provided.

PNC

The PNC transformation may be run as follows:

nextflow run phac-nml/metadatatransformation -profile singularity --input tests/data/samplesheets/pnc/basic.csv --outdir results --transformation pnc -c pnc.config

Where the pnc.config file is as follows:

params {
    metadata_1_header = "isolate_received_date"
    metadata_2_header = "isolation_date"
    metadata_3_header = "sample_collection_date"
    metadata_4_header = "sample_received_date_collaborator"
    metadata_5_header = "sample_received_date_nml"
    metadata_6_header = "sequencing_date"
    metadata_7_header = "host_age"
    metadata_8_header = "host_age_unit"
    metadata_9_header = "host_date_of_birth_dob"
    metadata_10_header = "host_scientific_name"
    metadata_11_header = "host_common_name"
    metadata_12_header = "food_product"
    metadata_13_header = "environmental_material"
    metadata_14_header = "environmental_site"
}

The metadata header parameters (--metadata_1_header through --metadata_16_header) are required for the transformation. In particular, fourteen of the metadata headers must be renamed as appropriate to be exactly the following:

isolate_received_date
isolation_date
sample_collection_date
sample_received_date_collaborator
sample_received_date_nml
sequencing_date
host_age
host_age_unit
host_date_of_birth_dob
host_scientific_name
host_common_name
food_product
environmental_material
environmental_site

For example, if the 2nd metadata column of the sample sheet corresponds to the isolation date, then it must be parameterized as follows: --metadata_2_header isolation_date. If the 5th metadata column of the input corresponds to the sample received date for the NML, then it must be parameterized as follows: --metadata_5_header sample_received_date_nml. The order of the metadata columns in the input does not matter, as long as the names are assigned correctly as above. If any of the columns are missing, an error will be reported in the transformation/results.csv file.

Generally, the assumptions and requirements for the pnc transformation are similar to the categorize, earliest, and age_pnc transformations. However, they are repeated here for completeness:

The the following assumptions are made when calculating ages:

365 days in a year
52 weeks in a year
12 months in a year
ages cannot be less than or equal to 0
ages cannot be greater than 150

The following values are ignored and treated as "years" when provided as an age unit: Not Applicable, Missing, Not Collected, Not Provided, Restricted Access, (blank). For example, this means that an age number of 10 and an age unit of Restricted Access will report an age of 10 years old.

The supported range of calendar dates is [1677-09-22, 2262-04-10], which is related to the default timestamp limitations of pandas. The following date fields have additional requirements:

isolate_received_date: after 1900-01-01
isolation_date: after 1900-01-01
sample_collection_date: after 1900-01-01
sample_received_date_collaborator: after 1900-01-01
sample_received_date_nml: after 1995-01-01
sequencing_date: after 2007-01-01

Reproducibility

It is a good idea to specify a pipeline version when running the pipeline on your data. This ensures that a specific version of the pipeline code and software are used when you run your pipeline. If you keep using the same tag, you'll be running the same version of the pipeline, even if there have been changes to the code since.

First, go to the phac-nml/metadatatransformation page and find the latest pipeline version - numeric only (eg. 1.3.1). Then specify this when running the pipeline with -r (one hyphen) - eg. -r 1.3.1. Of course, you can switch to another version by changing the number after the -r flag.

This version number will be logged in reports when you run the pipeline, so that you'll know what you used when you look back in the future.

To further assist in reproducbility, you can use share and re-use parameter files to repeat pipeline runs with the same settings without having to write out a command with every single parameter.

If you wish to share such profile (such as upload as supplementary material for academic publications), make sure to NOT include cluster specific paths to files, nor institutional specific profiles.

Core Nextflow arguments

These options are part of Nextflow and use a single hyphen (pipeline parameters use a double-hyphen).

`-profile`

Use this parameter to choose a configuration profile. Profiles can give configuration presets for different compute environments.

Several generic profiles are bundled with the pipeline which instruct the pipeline to use software packaged using different methods (Docker, Singularity, Podman, Shifter, Charliecloud, Apptainer, Conda) - see below.

We highly recommend the use of Docker or Singularity containers for full pipeline reproducibility, however when this is not possible, Conda is also supported.

The pipeline also dynamically loads configurations from https://github.com/nf-core/configs when it runs, making multiple config profiles for various institutional clusters available at run time. For more information and to see if your system is available in these configs please see the nf-core/configs documentation.

Note that multiple profiles can be loaded, for example: -profile test,docker - the order of arguments is important! They are loaded in sequence, so later profiles can overwrite earlier profiles.

If -profile is not specified, the pipeline will run locally and expect all software to be installed and available on the PATH. This is not recommended, since it can lead to different results on different machines dependent on the computer enviroment.

test
- A profile with a complete configuration for automated testing
- Includes links to test data so needs no other parameters
docker
- A generic configuration profile to be used with Docker
singularity
- A generic configuration profile to be used with Singularity
podman
- A generic configuration profile to be used with Podman
shifter
- A generic configuration profile to be used with Shifter
charliecloud
- A generic configuration profile to be used with Charliecloud
apptainer
- A generic configuration profile to be used with Apptainer
conda
- A generic configuration profile to be used with Conda. Please only use Conda as a last resort i.e. when it's not possible to run the pipeline with Docker, Singularity, Podman, Shifter, Charliecloud, or Apptainer.

`-resume`

Specify this when restarting a pipeline. Nextflow will use cached results from any pipeline steps where the inputs are the same, continuing from where it got to previously. For input to be considered the same, not only the names must be identical but the files' contents as well. For more info about this parameter, see this blog post.

You can also supply a run name to resume a specific run: -resume [run-name]. Use the nextflow log command to show previous run names.

`-c`

Specify the path to a specific config file (this is a core Nextflow command). See the nf-core website documentation for more information.

Custom configuration

Resource requests

Whilst the default requirements set within the pipeline will hopefully work for most people and with most input data, you may find that you want to customise the compute resources that the pipeline requests. Each step in the pipeline has a default set of requirements for number of CPUs, memory and time. For most of the steps in the pipeline, if the job exits with any of the error codes specified here it will automatically be resubmitted with higher requests (2 x original, then 3 x original). If it still fails after the third attempt then the pipeline execution is stopped.

To change the resource requests, please see the max resources and tuning workflow resources section of the nf-core website.

Custom Containers

In some cases you may wish to change which container or conda environment a step of the pipeline uses for a particular tool. By default nf-core pipelines use containers and software from the biocontainers or bioconda projects. However in some cases the pipeline specified version maybe out of date.

To use a different container from the default container or conda environment specified in a pipeline, please see the updating tool versions section of the nf-core website.

Custom Tool Arguments

A pipeline might not always support every possible argument or option of a particular tool used in pipeline. Fortunately, nf-core pipelines provide some freedom to users to insert additional parameters that the pipeline does not include by default.

To learn how to provide additional arguments to a particular tool of the pipeline, please see the customising tool arguments section of the nf-core website.

nf-core/configs

In most cases, you will only need to create a custom config as a one-off but if you and others within your organisation are likely to be running nf-core pipelines regularly and need to use the same settings regularly it may be a good idea to request that your custom config file is uploaded to the nf-core/configs git repository. Before you do this please can you test that the config file works with your pipeline of choice using the -c parameter. You can then create a pull request to the nf-core/configs repository with the addition of your config file, associated documentation file (see examples in nf-core/configs/docs), and amending nfcore_custom.config to include your custom profile.

See the main Nextflow documentation for more information about creating your own configuration files.

Azure Resource Requests

To be used with the azurebatch profile by specifying the -profile azurebatch. We recommend providing a compute params.vm_type of Standard_D16_v3 VMs by default but these options can be changed if required.

Note that the choice of VM size depends on your quota and the overall workload during the analysis. For a thorough list, please refer the Azure Sizes for virtual machines in Azure.

Running in the background

Nextflow handles job submissions and supervises the running jobs. The Nextflow process must run until the pipeline is finished.

The Nextflow -bg flag launches Nextflow in the background, detached from your terminal so that the workflow does not stop if you log out of your session. The logs are saved to a file.

Alternatively, you can use screen / tmux or similar tool to create a detached session which you can log back into at a later time. Some HPC setups also allow you to run nextflow within a cluster job submitted your job scheduler (from where it submits more jobs).

Nextflow memory requirements

In some cases, the Nextflow Java virtual machines can start to request a large amount of memory. We recommend adding the following line to your environment to limit this (typically in ~/.bashrc or ~./bash_profile):

NXF_OPTS='-Xms1g -Xmx4g'