nf-core/fetchngs
Pipeline to fetch metadata and raw FastQ files from public databases
1.4
). The latest
stable release is
1.12.0
.
Introduction
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
- Resolve database ids back to appropriate experiment-level ids and to be compatible with the ENA API
- Fetch extensive id metadata via ENA API
- Download FastQ files:
- If direct download links are available from the ENA API, fetch in parallel via
curl
and performmd5sum
check - Otherwise use
sra-tools
to download.sra
files and convert them to FastQ
- If direct download links are available from the ENA API, fetch in parallel via
- Collate id metadata and paths to FastQ files in a single samplesheet
Synapse ids
- Resolve Synapse directory ids to their corresponding FastQ files ids via the
synapse list
command. - Retrieve FastQ file metadata including FastQ file names, md5sums, etags, annotations and other data provenance via the
synapse show
command. - Download FastQ files in parallel via
synapse get
- 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
-
Install
Nextflow
(>=21.04.0
) -
Install any of
Docker
,Singularity
,Podman
,Shifter
orCharliecloud
for full pipeline reproducibility (please only useConda
as a last resort; see docs) -
Download the pipeline and test it on a minimal dataset with a single command:
- 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 eitherdocker
orsingularity
and set the appropriate execution settings for your local compute environment. - If you are using
singularity
then 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_container
parameter to pull and convert the Docker image instead. Alternatively, it is highly recommended to use thenf-core download
command to pre-download all of the required containers before running the pipeline and to set theNXF_SINGULARITY_CACHEDIR
orsingularity.cacheDir
Nextflow 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 theNXF_CONDA_CACHEDIR
orconda.cacheDir
settings to store the environments in a central location for future pipeline runs.
- 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
-
Start running your own analysis!
Documentation
The nf-core/fetchngs pipeline comes with documentation about the pipeline usage, parameters and output.
Credits
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).
Citations
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 CITATIONS.md
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.