Predict de novo orthologous genes from differentially expressed translated protein sequences.
The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker containers making installation trivial and results highly reproducible.
i. Install nextflow
ii. Install either Docker
or Singularity
for full pipeline reproducibility (please only use Conda
as a last resort; see docs)
iii. Download the pipeline and test it on a minimal dataset with a single command
nextflow run czbiohub/nf-predictorthologs -profile test,<docker/singularity/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 eitherdocker
orsingularity
and set the appropriate execution settings for your local compute environment.
iv. Start running your own analysis!
nextflow run czbiohub/nf-predictorthologs -profile <docker/singularity/conda/institute> --reads '*_R{1,2}.fastq.gz' --genome GRCh37
See usage docs for all of the available options when running the pipeline.
The nf-core/predictorthologs pipeline comes with documentation about the pipeline, found in the docs/
directory:
nf-core/predictorthologs was originally written by Olga Botvinnik.
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 Slack (you can join with this invite).
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.
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