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 project id or 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

Quick Start

1. Install Nextflow (>=21.10.3)

2. Install any of Docker, Singularity, Podman, Shifter or Charliecloud for full pipeline reproducibility (please only use Conda as a last resort; see docs)

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

   nextflow run kaitlinchaung/fetchngs -profile test,horence,conda

   • 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 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 the nf-core download command to pre-download all of the required containers before running the pipeline and to set the NXF_SINGULARITY_CACHEDIR or singularity.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 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!

Use the --num_reads parameter to define how many reads you want to download. Use the --num_samples parameter to define how many samples you want to download. OPTION 1: bash nextflow run kaitlinchaung/fetchngs \ --id_list ids.txt \ -profile horence,singularity \ -latest \ -r master \ -resume \ --num_reads 4000000 \ --num_samples 20

OPTION 2:
```bash
nextflow run kaitlinchaung/fetchngs \
    --id_list ids.txt \
    -profile horence,singularity \
    -latest \
    -r master \
    -resume \
    --num_reads 4000000 \
    --num_samples 20
```

Where `ids.txt` looks something like:
```
SRR7993771
SRR7993772
SRR7993773
SRR7993774
```

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.