fetchdatairidanext pipeline

This pipeline can be used to fetch data from NCBI for integration into IRIDA Next.

Input

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

sample	insdc_accession
SampleA	ERR1109373
SampleB	SRR13191702

That is, there are two columns:

• sample: The sample identifier downloaded read data should be associated with.
• insdc_accession: The accession from the International Sequence Data Collaboration (INSDC) for the data to download (currently only sequence runs supported, e.g., starting with SRR, ERR, or DRR).

The structure of this file is defined in assets/schema_input.json. An example of this file is provided at assets/samplesheet.csv.

IRIDA-Next Optional Input Configuration

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

sample_name: An optional column, to add the sample_name prefix before the accession code.

sample_name, allows more flexibility in naming reads. Unlike sample, sample_name is not required to contain unique values. Non-alphanumeric characters (excluding _,-,.) will be replaced with "_". sample_name can be provided without renaming by changing parameters.

An example samplesheet has been provided with the pipeline.

Parameters

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 (or -profile docker for docker) and -r [branch] to specify which GitHub branch you would like to run.

--rename_with_samplename (Default: true) When false, samplesheet column sample_name not used for reads-renaming.

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

Running

Test data

To run the pipeline with test data, please do:

nextflow run phac-nml/fetchdatairidanext -profile test,docker --outdir results

The downloaded data will appear in results/. A JSON file for integrating data with IRIDA Next will be written to results/iridanext.output.json.gz (see the Output section for details).

Other data

To run the pipeline with other data (a custom samplesheet), please do:

nextflow run phac-nml/fetchdatairidanext -profile docker --input assets/samplesheet.csv --outdir results

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

Output

Read data

The sequence reads will appear in the results/reads directory (assuming --outdir results is specified). For example:

results/reads/
├── ERR1109373.fastq.gz
├── ERR1109373_1.fastq.gz
├── ERR1109373_2.fastq.gz
├── SRR13191702_1.fastq.gz
└── SRR13191702_2.fastq.gz

IRIDA Next integration file

A JSON file for loading the data 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 irida.output.json.gz (ex: [outdir]/irida.output.json.gz).

{
  "files": {
    "global": [],
    "samples": {
      "SampleA": [{ "path": "reads/SRR13191702_1.fastq.gz" }, { "path": "reads/SRR13191702_2.fastq.gz" }]
    }
  }
}

Within the files section of this JSON file, all of the output paths are relative to the --outdir results. Therefore, "path": "reads/SRR13191702_1.fastq.gz" refers to a file located within sratools/reads/SRR13191702_1.fastq.gz.

An additional example of this file can be found at tests/data/test1_iridanext.output.json.

Failures

If one or more samples fail to download, the workflow will still attempt to download all other samples in the samplesheet. The samples that fail to download will be reported in a file named results/prefetch/failures_report.csv. This CSV file has two columns: sample (the name of the sample, matching the input samplesheet) and error_accession (the accession that failed to download).

For example:

sample,error_accession
ERROR1,SRR999908
ERROR2,SRR999934

Acknowledgements

This pipeline uses code and infrastructure developed and maintained by the nf-core initative, and reused here under the MIT license.

The nf-core framework for community-curated bioinformatics pipelines.

Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.

Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.

In addition, references of tools and data used in this pipeline are as follows:

• The fastq_download_prefetch_fasterqdump_sratools subworkflow from nf-core. Custom modifications to this workflow (and underlying modules) are found in the subworkflows/local and modules/local directories.

Other works this pipeline makes use of are found in the CITATIONS.md file.

Legal

Copyright 2024 Government of Canada

Licensed under the MIT License (the "License"); you may not use this work except in compliance with the License. You may obtain a copy of the License at:

https://opensource.org/license/mit/

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.