Pipeline to fetch metadata and raw FastQ files from public and private databases
*nf-core/fetchngs* is a bioinformatics pipeline to fetch metadata and raw FastQ files from public databases. At present, the pipeline supports SRA / ENA / GEO 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.
Via a single file of ids, provided one-per-line (see example input file) the pipeline performs the following steps:
- Resolve database ids back to appropriate experiment-level ids and to be compatible with the ENA API
- Fetch extensive id metadata including direct download links to FastQ files via ENA API
- Download FastQ files in parallel via
- Collate id metadata and paths to FastQ files in a single samplesheet
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.
Download the pipeline and test it on a minimal dataset with a single command:
nextflow run nf-core/fetchngs -profile test,<docker/singularity/podman/shifter/charliecloud/conda/institute>
* 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
singularityand set the appropriate execution settings for your local compute environment.
* If you are using
singularitythen the pipeline will auto-detect this and attempt to download the Singularity images directly as opposed to performing a conversion from Docker images. If you are persistently observing issues downloading Singularity images directly due to timeout or network issues then please use the
--singularity_pull_docker_containerparameter to pull and convert the Docker image instead. Alternatively, it is highly recommended to use the
nf-core downloadcommand to pre-download all of the required containers before running the pipeline and to set the
singularity.cacheDirNextflow options to be able 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
conda.cacheDirsettings to store the environments in a central location for future pipeline runs.
Start running your own analysis!
nextflow run nf-core/fetchngs --input ids.txt -profile <docker/singularity/podman/shifter/charliecloud/conda/institute>
nf-core/fetchngs was originally written by Harshil Patel (@drpatelh) from The Bioinformatics & Biostatistics Group at The Francis Crick Institute, London and Jose Espinosa-Carrasco (@JoseEspinosa) from The Comparative Bioinformatics Group at The Centre for Genomic Regulation, Spain.
Contributions and Support
If you would like to contribute to this pipeline, please see the contributing guidelines.
If you use nf-core/fetchngs for your analysis, please cite it using the following doi: 10.5281/zenodo.XXXXXX
An extensive list of references for the tools used by the pipeline can be found in the
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.