nf-core pipeline · cancer-evolution, cancer-genomics, copy-number-variation
nf-core/tumourevo
Models tumour clonal evolution and subclonal architecture from matched tumour-normal whole-genome sequencing data.
What nf-core/tumourevo is
nf-core/tumourevo is a bioinformatics pipeline designed to reconstruct tumour clonal evolution from whole-genome sequencing data. Developed by researchers at the Cancer Data Science Laboratory at the University of Trieste, the workflow performs downstream analysis of somatic variant and copy-number alteration calls derived from tumour-normal matched sequencing assays.
The pipeline models the evolutionary trajectories leading to an observed tumour genome across single samples, multi-region or longitudinal assays from individual patients, and multi-patient cohorts. It brings together variant annotation, quality assessment of copy-number calls, subclonal deconvolution, phylogenetic clone tree inference, and mutational signature extraction into an automated workflow.
What the pipeline does
The workflow takes a samplesheet specifying tumour-normal pairs, somatic variant call format (VCF) files with index files, copy-number segment files, and tumour purity and ploidy estimates alongside a reference genome FASTA. Somatic variants are annotated using VEP, followed by driver annotation and quality control procedures using CNAqc to assess copy-number calls and TINC to evaluate tumour-in-normal contamination.
For subclonal deconvolution, the pipeline applies clustering algorithms including PyClone, MOBSTER, or VIBER. When multiple samples are provided for a single patient, they are processed in a multivariate framework to capture shared and private clonal structures. Inferred clones are organised into phylogenetic trees using ctree, and mutational signature deconvolution is performed using SparseSignatures and SigProfiler.
Key facts
- Pipeline framework
- Nextflow DSL2
- Sequencing assay type
- Tumour-normal matched whole-genome sequencing (WGS)
- Variant annotation tool
- VEP
- Quality control tools
- CNAqc, TINC
- Subclonal deconvolution tools
- PyClone, MOBSTER, VIBER
- Clone tree inference tool
- ctree
- Signature deconvolution tools
- SparseSignatures, SigProfiler
- Input formats
- VCF (.vcf.gz, .tbi), CNA segments, CNA purity/ploidy metrics, FASTA
- Developer institution
- Cancer Data Science Laboratory, University of Trieste
What you can use it for
- Single-sample tumour evolution analysis
- Model subclonal heterogeneity, driver alterations, and mutational processes from single tumour-normal whole-genome sequencing pairs.
- Multi-region clonal mapping
- Apply multivariate subclonal deconvolution across spatially distinct tumour biopsies from the same patient to reconstruct phylogenetic clone trees.
- Longitudinal clonal tracking
- Trace temporal changes in clone frequency, copy-number events, and active mutational signatures across serial patient biopsies.
- Cohort-level tumour deconvolution
- Assess clonal architecture and mutational signatures across collections of patients representing distinct cancer types.
Run it here
The research agent runs nf-core/tumourevo 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
- Supports multi-sample patient data directly within a multivariate deconvolution framework.
- Incorporates dedicated quality control tools (CNAqc and TINC) for copy-number calls and tumour-in-normal contamination.
- Integrates subclonal clustering with downstream phylogenetic clone tree reconstruction via ctree.
- Provides modular, containerised execution via Nextflow DSL2 using one container per process.
Watch out for
- Requires pre-called somatic VCF and copy-number segmentation inputs from supported upstream callers rather than raw sequencing reads.
- Designed specifically for tumour-normal matched whole-genome sequencing data rather than targeted or exome sequencing.
- Pipeline execution parameters must be provided via the command-line interface or parameter files rather than custom Nextflow configuration files.
Questions
Can I run nf-core/tumourevo without my own cluster?
The research agent runs nf-core/tumourevo 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/tumourevo under?
MIT, like every nf-core pipeline.
What input files are required to run the pipeline?
The pipeline requires a samplesheet specifying the patient and sample identifiers, somatic VCF files with tabix indices (.tbi), segmented copy-number files, purity and ploidy estimates, cancer type, and a reference FASTA file.
How does the pipeline handle multi-region or longitudinal patient data?
When multiple samples from the same patient are provided, the pipeline defaults to analysing them in a multivariate framework, which jointly informs the subclonal deconvolution and evolutionary reconstruction steps.
Which tools are used for subclonal deconvolution and mutational signatures?
Subclonal deconvolution is conducted using PyClone, MOBSTER, or VIBER, while mutational signature deconvolution is performed using SparseSignatures and SigProfiler.
How to cite it
doi: 10.5281/zenodo.23098067
Written from nf-core/tumourevo's own README. Statements citing a figure that does not appear there are removed before publication. Licence and running details come from our registry.