nf-core pipeline ·
nf-core/omicsgenetraitassociation
Integrates multi-omic datasets to perform correlated meta-analysis and functional enrichment for trait associations.
What nf-core/omicsgenetraitassociation is
nf-core/omicsgenetraitassociation is a bioinformatics pipeline designed to integrate multiple omic data streams and carry out coordinated association analyses. It was originally developed by Woo Jung, with contributions from Chase Mateusiak, Sandeep Acharya, Edward Kang, Lisa Liao, Michael Brent, and Michael Province.
A central problem in multi-study omics analysis is the presence of hidden non-independencies between study elements, which frequently arise from overlapping cohorts or related samples. This pipeline addresses that issue by carrying out meta-analyses of trait associations that explicitly model and account for correlations across omics studies.
By moving from variant-level summaries to unified multi-omic signals, the pipeline enables researchers to detect consolidated gene-trait relationships and interpret them through downstream module and Gene Ontology enrichment analyses.
What the pipeline does
The pipeline accepts a samplesheet detailing input association files for each run, including genome-wide association study (GWAS) summary statistics, transcriptome-wide association study (TWAS) gene-trait association files, and paths to additional omic association p-values. Variant-level GWAS summary statistics are aggregated to the gene level using PASCAL, and gene-trait associations are handled using MMAP.
The aggregated data streams are then integrated using corrmeta to execute correlated meta-analyses that adjust for sample overlap and dependence structures. Finally, the pipeline runs module enrichment analysis using MEA (from PascalX) and Gene Ontology (GO) enrichment analysis using WebGestaltR to produce downstream functional reports.
Key facts
- Pipeline framework
- Nextflow
- Original author
- Woo Jung
- Primary input format
- CSV samplesheet specifying GWAS, TWAS, and additional omic data paths
- GWAS aggregation tool
- PASCAL
- Gene-trait association tool
- MMAP
- Meta-analysis tool
- corrmeta
- Module enrichment tool
- MEA (PascalX)
- GO enrichment tool
- WebGestaltR
What you can use it for
- Correlated multi-omics meta-analysis
- Integrating association statistics from multiple omic layers whilst correcting for sample overlap or related individuals across studies.
- GWAS gene-level aggregation
- Aggregating variant-level GWAS summary statistics into gene-level association metrics using PASCAL.
- Downstream functional enrichment
- Evaluating biological module enrichment and Gene Ontology terms for genes identified across coordinated omic associations.
Run it here
The research agent runs nf-core/omicsgenetraitassociation with Nextflow on its own machine — up to 32 cores, 256 GiB of memory and 24 hours per run, which covers the test profile and real single-sample runs. It builds the samplesheet, checks it against the pipeline's schema and reads the results back. When a run is too big for one machine, it gives you the exact command for your own cluster instead.
Strengths and limits
Good at
- Corrects for hidden dependencies and non-independence caused by overlapping or related samples across distinct omics data sources.
- Combines gene aggregation, correlated meta-analysis, and downstream enrichment reporting into a single automated workflow.
- Supports the inclusion of arbitrary additional omic association p-values alongside standard GWAS and TWAS datasets.
Watch out for
- Requires pre-computed association statistics and TWAS inputs rather than primary sequencing or genotyping raw data.
- Pipeline parameters cannot be supplied through custom configuration files specified via the Nextflow -c flag; they must be provided via the command-line interface or a parameter file.
Questions
Can I run nf-core/omicsgenetraitassociation without my own cluster?
The research agent runs nf-core/omicsgenetraitassociation with Nextflow on its own machine — up to 32 cores, 256 GiB of memory and 24 hours per run, which covers the test profile and real single-sample runs. It builds the samplesheet, checks it against the pipeline's schema and reads the results back. When a run is too big for one machine, it gives you the exact command for your own cluster instead.
What licence is nf-core/omicsgenetraitassociation under?
MIT, like every nf-core pipeline.
What inputs must be listed in the samplesheet?
Each row in the samplesheet represents a correlated meta-analysis run and requires a sample identifier, a trait name, paths to GWAS summary statistics for PASCAL aggregation, a TWAS gene-trait association file, and optional paths to additional omic association p-values.
How does the pipeline handle non-independent datasets?
It applies the corrmeta method to conduct meta-analysis whilst accounting for correlations that stem from shared or related study participants across omics studies.
Which tools are used for functional pathway analysis?
Module enrichment analysis is carried out using MEA from the PascalX package, and Gene Ontology enrichment is conducted using the WebGestaltR package.
How to cite it
Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen. The nf-core framework for community-curated bioinformatics pipelines. Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.
Written from nf-core/omicsgenetraitassociation's own README. Statements citing a figure that does not appear there are removed before publication. Licence and running details come from our registry.