nf-core/tumourevo

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Introduction

This document describes the output produced by the pipeline. All plots generated in each step are summarised into the final report.

The directories listed below will be created in the results directory after the pipeline has finished. All paths are relative to both published and unpublished results.

Pipeline overview

The pipeline is built using Nextflow and consists in five main subworkflows:

• Variant Annotation
• Driver Annotation
• QC
• Signature Deconvolution
• Subclonal Deconvolution

Intermediate steps connetting the main subworkflows will output unpublished results which will be available in the working directory of the pipeline. These steps consist in:

• Formatter
• Lifter

Variant Annotation

This directory contains results from the variant annotation subworkflow. At the level of individual samples, genomic variants present in the input VCF files are annotated using VEP.

VEP

VEP (Variant Effect Predictor) is a Ensembl tool that determines the effect of all variants (SNPs, insertions, deletions, CNVs or structural variants) on genes, transcripts, and protein sequence. This step starts from VCF files.

Driver Annotation

This directory contains results from the driver annotation subworkflow.

Tumour-type driver annotation

According to the specified tumour type, potential driver mutations are identified and annotated using IntOGen database.

QC

The QC subworkflows requires in input a segmentation file from allele-specific copy number callers (either Sequenza, ASCAT) and the joint VCF file from join_positions subworkflow. The QC sub-workflows first conduct quality control on CNV and somatic mutation data for individual samples in CNAqc step, and subsequently summarize validated information at patient level in join_CNAqc step. The QC subworkflow is a crucial step of the pipeline as it ensures high confidence in identifying clonal and subclonal events while accounting for variations in tumor purity.

TINC

TINC is a package to calculate the contamination of tumor DNA in a matched normal sample. TINC provides estimates of the proportion of cancer cells, containing the normal sample, and the proportion of cancer cells in the tumor sample (tumor purity).

CNAqc

CNAqc is a package to quality control (QC) bulk cancer sequencing data for validating copy number segmentations against variant allele frequencies of somatic mutations.

join_CNAqc

This module creates a multi-CNAqc object for patient by summarizing the quality check performed at the single sample level. For more information about the strucutre of multi-CNAqc object see CNAqc documentation.

Signature Deconvolution

Mutational signatures are distinctive patterns of somatic mutations in cancer genomes that reveal the underlying mutational processes driving tumor evolution and progression. These signatures are identified by analyzing aggregated point-mutation counts from multiple samples. Validated mutations from the join_CNAqc step are converted into a joint TSV table (see tsvparse) and then input into the signature deconvolution subworkflow, which performs de novo extraction, inference, interpretation, or deconvolution of mutational counts.

The results of this step are collected in {pubslish_dir}/signature_deconvolution/. Two tools can be specified by using --tools parameter: SparseSignatures and SigProfiler.

SparseSignatures

SparseSignatures is an R-based computational framework that provides a set of functions to extract and visualize the mutational signatures that best explain the mutation counts of a large number of patients.

SigProfiler

SigProfiler is a python framework that allows de novo extraction of mutational signatures from data generated in a matrix format. The tool identifies the number of operative mutational signatures, their activities in each sample, and the probability for each signature to cause a specific mutation type in a cancer sample. The tool makes use of SigProfilerMatrixGenerator and SigProfilerPlotting, seamlessly integrating with other SigProfiler tools.

Subclonal Deconvolution

The subclonal deconvolution subworkflow requires in input a joint mCNAqc object resulting from the join_CNAqc step. The subworkflow will perform multi-sample deconvolution if more than one sample for each patient is present.

The results of subclonal decovnultion step are collected in {publish_dir}/subclonal_deconvolution/ directory.

MOBSTER

MOBSTER processes mutant allelic frequencies to identify and remove neutral tails from the input data, so that subclonal reconstruction algorithms can be applied downstream to find subclones from the processed read counts.

PyClone-VI

PyClone-VI is a computationally efficient Bayesian statistical method for inferring the clonal population structure of cancers, by considering allele fractions and coincident copy number variation using a variational inference approach.

VIBER

VIBER is an R package that implements a variational Bayesian model to fit multi-variate Binomial mixtures. In the context of subclonal deconvolution in singlesample modality, VIBER models read counts that are associated with the most represented karyotype.

ctree

Subclonal deconvolution results are used to build clone tree from both single samples and multple samples using ctree. ctree is a R-based package which implements basic functions to create, manipulate and visualize clone trees by modelling Cancer Cell Fractions (CCF) clusters. Annotated driver genes must be provided in the input data.

NB: When --tools pyclone-vi is used, the output of PyClone-VI subclonal deconvolution is preprocessed prior to clone tree inference. Since ctree requires labeling one of the clusters as “clonal,” the one with the highest CCF across all samples is choosen.

VIBER and MOBSTER fits are already compatible for ctree analysis.

Output directory: {publish_dir}/subclonal_deconvolution/ctree/<patient>/

• ctree_<tool>.rds
  • RDS file containing inferred clone tree
• ctree_<tool>_plots.rds
  • RDS file for single sample clone tree plot
• ctree_input_pyclonevi.csv
  • CSV file required for clone tree inference from pyclone

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Unpublished results

Formatter

The Formatter subworkflow is used to convert file to other formats and to standardize the output files resulting from different mutation (Mutect2, Strelka) and cna callers (ASCAT,Sequenza). Output files from this step are not published.

cna2CNAqc

This parser aims at standardize into a unique format copy number calls and purity estimate from different callers.

vcf2cnaqc

This parser aims at standardize into a unique format single nucleotide variants from different callers.

cnaqc2tsv

This parser aims at converting mutations data of joint CNAqc analysis from CNAqc format (RDS file) into a tabular format (TSV file). This step is mandatory for running python-based tools (e.g. PyClone-VI, SigProfiler).

Lifter

The Lifter subworkflow is an optional step and it is run when single sample VCF file are provided. When multiple samples from the same patient are provided, the user can specify either a single joint VCF file, containing variant calls from all tumor samples of the patient (see joint variant calling), or individual sample specific VCF files. In the latter case, path to tumor BAM files must be provided in order to collect all mutations from the samples and perform pile-up of sample’s private mutations in all the other samples. Two intermediate steps, get_positions and mpileup, are performed to identify private mutations in all the samples and retrieve their variant allele frequency. Once private mutations are properly defined, they are merged back into the original VCF file during the join_positions step. The updated VCF file is then converted into a vcfR RDS object.

mpileup

At this stage, bcftools is used to perform the pileup in order to retrieve frequency information of private mutations across all samples.

positions

This intermediate step allows to retrieve private and shared mutations across samples originated from the same patient. Retrieved mutations are joined with original mutations present in input VCF, which is in turn converted into an RDS object using vcfR.