nf-core/fetchngs is a bioinformatics pipeline to fetch metadata and raw FastQ files from both public and private databases. At present, the pipeline supports SRA / ENA / DDBJ / GEO / Synapse ids (see usage docs).

The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It uses Docker/Singularity containers making installation trivial and results highly reproducible. The Nextflow DSL2 implementation of this pipeline uses one container per process which makes it much easier to maintain and update software dependencies.

On release, automated continuous integration tests run the pipeline on a full-sized dataset on the AWS cloud infrastructure. This ensures that the pipeline runs on AWS, has sensible resource allocation defaults set to run on real-world datasets, and permits the persistent storage of results to benchmark between pipeline releases and other analysis sources. The results obtained from the full-sized test can be viewed on the nf-core website.

Pipeline summary

Via a single file of ids, provided one-per-line (see example input file) the pipeline performs the following steps:

SRA / ENA / DDBJ / GEO ids

  1. Resolve database ids back to appropriate experiment-level ids and to be compatible with the ENA API
  2. Fetch extensive id metadata via ENA API
  3. Download FastQ files:
    • If direct download links are available from the ENA API, fetch in parallel via curl and perform md5sum check
    • Otherwise use sra-tools to download .sra files and convert them to FastQ
  4. Collate id metadata and paths to FastQ files in a single samplesheet

Synapse ids

  1. Resolve Synapse directory ids to their corresponding FastQ files ids via the synapse list command.
  2. Retrieve FastQ file metadata including FastQ file names, md5sums, etags, annotations and other data provenance via the synapse show command.
  3. Download FastQ files in parallel via synapse get
  4. Collate paths to FastQ files in a single samplesheet

Samplesheet format

The columns in the auto-created samplesheet can be tailored to be accepted out-of-the-box by selected nf-core pipelines, these currently include nf-core/rnaseq and the Illumina processing mode of nf-core/viralrecon. You can use the --nf_core_pipeline parameter to customise this behaviour e.g. --nf_core_pipeline rnaseq. More pipelines will be supported in due course as we adopt and standardise samplesheet input across nf-core.

Quick Start

  1. Install Nextflow (>=21.10.3)

  2. Install any of Docker, Singularity (you can follow this tutorial), Podman, Shifter or Charliecloud for full pipeline reproducibility (you can use Conda both to install Nextflow itself and also to manage software within pipelines. Please only use it within pipelines as a last resort; see docs).

  3. Download the pipeline and test it on a minimal dataset with a single command:

    nextflow run nf-core/fetchngs -profile test,YOURPROFILE --outdir <OUTDIR>

    Note that some form of configuration will be needed so that Nextflow knows how to fetch the required software. This is usually done in the form of a config profile (YOURPROFILE in the example command above). You can chain multiple config profiles in a comma-separated string.

    • The pipeline comes with config profiles called docker, singularity, podman, shifter, charliecloud and conda which instruct the pipeline to use the named tool for software management. For example, -profile test,docker.
    • Please check nf-core/configs to see if a custom config file to run nf-core pipelines already exists for your Institute. If so, you can simply use -profile <institute> in your command. This will enable either docker or singularity and set the appropriate execution settings for your local compute environment.
    • If you are using singularity, please use the nf-core download command to download images first, before running the pipeline. Setting the NXF_SINGULARITY_CACHEDIR or singularity.cacheDir Nextflow options enables you to store and re-use the images from a central location for future pipeline runs.
    • If you are using conda, it is highly recommended to use the NXF_CONDA_CACHEDIR or conda.cacheDir settings to store the environments in a central location for future pipeline runs.
  4. Start running your own analysis!

    nextflow run nf-core/fetchngs --input ids.txt --outdir <OUTDIR> -profile <docker/singularity/podman/shifter/charliecloud/conda/institute>


The nf-core/fetchngs pipeline comes with documentation about the pipeline usage, parameters and output.


nf-core/fetchngs was originally written by Harshil Patel (@drpatelh) from Seqera Labs, Spain and Jose Espinosa-Carrasco (@JoseEspinosa) from The Comparative Bioinformatics Group at The Centre for Genomic Regulation, Spain. Support for download of sequencing reads without FTP links via sra-tools was added by Moritz E. Beber (@Midnighter) from Unseen Bio ApS, Denmark. The Synapse workflow was added by Daisy Han @daisyhan97 and Bruno Grande @BrunoGrandePhD from Sage Bionetworks, Seattle.

Contributions and Support

If you would like to contribute to this pipeline, please see the contributing guidelines.

For further information or help, don’t hesitate to get in touch on the Slack #fetchngs channel (you can join with this invite).


If you use nf-core/fetchngs for your analysis, please cite it using the following doi: 10.5281/zenodo.5070524

An extensive list of references for the tools used by the pipeline can be found in the file.

You can cite the nf-core publication as follows:

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.