The standardization, portability, and reproducibility of analysis pipelines is a renowned problem within the bioinformatics community. Bioinformatic analysis pipelines are often designed for execution on-premise, and this inevitably leads to a level of customisation and integration that is only applicable to the local infrastructure. More notably, the software required to run these pipelines is also tightly coupled with the local compute environment, and this leads to poor pipeline portability, and reproducibility of the ensuing results – both of which are fundamental requirements for the validation of scientific findings.
A team led by researchers at SciLifeLab have developed nf-core, a framework that provides a community-driven platform for the creation and development of best practice analysis pipelines written in the Nextflow language. Nextflow has built-in support for pipeline execution on most computational infrastructures, as well as automated deployment using container technologies such as Conda, Docker, and Singularity. Therefore, key obstacles in pipeline development such as portability, reproducibility, scalability and unified parallelism are inherently addressed by all nf-core pipelines. Furthermore, to ensure that new pipelines can be added seamlessly, and existing pipelines are able to inherit up-to-date functionality the nf-core community is actively developing a suite of tools that automate pipeline creation, testing, deployment and synchronization. The peer-review process during pipeline development ensures that best practices and common usage patterns are imposed and therefore, adhere to community guidelines. The primary goal is to provide a community-driven platform for high-quality, excellent documented and reproducible bioinformatics pipelines that can be utilized across various institutions and research facilities.
Availability – The nf-core/rnafusion 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.