Scalable and efficient data analysis pipelines for clinical metagenomics from nf-core

BACKGROUND

Clinical metagenomics is an emerging field with the potential to transform
infectious disease diagnostics. Clinical metagenomics analysis often includes
a series of initial steps, including rapid taxonomic identification of
high-throughput sequencing (HTS) reads against known reference databases, de
novo genome assembly for identification of database-unrepresented taxa, and
functional screening for characteristics such as antimicrobial resistance.

As data throughput increases, clinical scientists need high-quality,
efficient, automated pipelines that scale with increasing numbers of samples
and genomes, replicability to ensure data fidelity, and portability to handle
execution on a different computing infrastructure.

METHODS

The nf-core initiative aims to build community-based, software-development
best-practices and (bio)informatic pipelines written in the workflow manager
language Nextflow. A range of metagenomics-based pipelines have been
developed within the initiative to address primary analysis in a reproducible
and interoperable manner. Design decisions during the construction of the
pipeline have drawn from established metagenomics procedures, but also
experiences from low-biomass ancient DNA metagenomics research.

RESULTS

I will introduce some of the nf-core pipeline offerings relevant for clinical
metagenomics: (1) nf-core/taxprofiler for profiling microbial communities
with potential for monitoring known and emerging pathogens, (2) nf-core/mag
for metagenomic de novo genome assembly for identification of novel
(potentially pathogenic) taxa, and (3) nf-core/funcscan for screening for
genes relating to antimicrobial resistance and natural product production.

Writing pipelines in Nextflow provides efficiency through integration with
job schedulers on HPC or the cloud, automatic retry of failed jobs, and
reproducibility through software containers.

DISCUSSION

I will describe how the various pipelines can be used in an interoperable
manner to produce an end-to-end set of results relevant for clinicians in
standardised and unified outputs. I will also show how nf-core aims to be as
user-friendly as possible, through extensive documentation, common
command-line and graphical-user interfaces, and interactive and customisable
result summaries.