A Robust Tool for Performing Transcriptome Profiling on Cancer Data • RNAsum

Transforms RNA-sequencing data into actionable clinical insights with automated reports.

Documentation | umccr.github.io/RNAsum

What is RNAsum?

RNAsum is an R package that integrates whole-genome sequencing (WGS) and whole-transcriptome sequencing (WTS) data to generate comprehensive, interactive HTML reports for cancer patient samples.

Quick start

RNAsum can be installed using one of the following two methods.

Installation

Option 1: from GitHub

RNAsum depends on pdftools, which requires system-level libraries (poppler, cairo, etc.) to be installed before installing the R package.

System dependencies installation

Ubuntu/Debian:

sudo apt-get install libpoppler-cpp-dev libharfbuzz-dev libfribidi-dev \
                     libfreetype6-dev libcairo2-dev libpango1.0-dev

macOS:

brew install poppler

HPC/Cluster (without root):

If you do not have root access (e.g., on a cluster), creating a fresh Conda environment is the most reliable way to provide necessary system libraries:

conda create -n rnasum_env -c conda-forge -c bioconda \
  r-base=4.1 poppler harfbuzz fribidi freetype pkg-config \
  cairo openssl pango make gxx_linux-64
conda activate rnasum_env

Once system dependencies are met, you can install the package directly from GitHub from within R console.

# 1. Increase timeout to prevent download failure for RNAsum.data
options(timeout = 600)

# 2. Install via remotes
if (!require("remotes")) install.packages("remotes")
remotes::install_github("umccr/RNAsum")

Option 2: from Conda

Conda package is available from the Anaconda umccr channel:

conda create -n rnasum -c umccr -c conda-forge -c bioconda r-rnasum
conda activate rnasum

Workflow

The pipeline consists of five main components.

rnasum_workflow

WTS data collection: ingests per-gene read counts and gene fusions.
Reference integration: normalises against reference cohorts.
WGS data integration: links genomic alterations with expression data.
Knowledge enrichment: annotates with clinically relevant databases.
Report generation: prioritises findings and creates interactive visualizations.

Detailed workflow documentation

Usage

Add RNAsum to PATH environment variable.

rnasum_cli=$(Rscript -e 'cat(system.file("cli", package="RNAsum"))')
ln -sf "$rnasum_cli/rnasum.R" "$rnasum_cli/rnasum"
export PATH="$rnasum_cli:$PATH"

rnasum --version

Common options

Option	Description	Default
`--sample_name`	Sample identifier	Required
`--dataset`	TCGA reference cohort	`PANCAN`
`--salmon`	Salmon quantification file	-
`--kallisto`	Kallisto abundance file	-
`--arriba_tsv`	Arriba fusion detection output	-
`--pcgr_tiers_tsv`	PCGR variant calls (tier 1-4)	-
`--cn_gene_tsv`	Copy number by gene	-
`--filter`	Filter low-expressed genes	`TRUE`

Run rnasum --help to get complete list of options.

For format and minimal content of input files (e.g. --pcgr_tiers_tsv, --cn_gene_tsv, --sv_tsv), see Input file formats.

Note: human reference genome GRCh38 (Ensembl based annotation version 105) is used for gene annotation by default. GRCh37 is no longer supported.

Examples

Test data: in /inst/rawdata/test_data folder of the GitHub repo
Runtime: < 15 minutes (16GB RAM, 1 CPU)

Scenario 1: WGS + WTS (recommended)

Comprehensive reporting, in which WGS-based findings are used as a primary source for expression profile prioritisation.

cd $rnasum_cli

rnasum \
  --sample_name test_sample_WTS \
  --dataset TEST \
  --salmon "$PWD/../rawdata/test_data/dragen/TEST.quant.genes.sf" \
  --arriba_pdf "$PWD/../rawdata/test_data/dragen/arriba/fusions.pdf" \
  --arriba_tsv "$PWD/../rawdata/test_data/dragen/arriba/fusions.tsv"  \
  --dragen_fusions "$PWD/../rawdata/test_data/dragen/test_sample_WTS.fusion_candidates.final"  \
  --pcgr_tiers_tsv "$PWD/../rawdata/test_data/small_variants/TEST-snvs_indels.tiers.tsv" \
  --cn_gene_tsv "$PWD/../rawdata/test_data/copy_number/TEST.cnv.gene.tsv" \
  --sv_tsv "$PWD/../rawdata/test_data/structural/TEST-sv.tsv" \
  --report_dir "$PWD/../rawdata/test_data/RNAsum" \
  --save_tables FALSE \
  --filter TRUE

The HTML report test_sample_WTS.RNAsum.html will be created in the inst/rawdata/test_data/dragen/RNAsum folder.

Scenario 2: WTS only

Basic reporting including information about detected gene fusions and expression levels of key genes.

cd $rnasum_cli

rnasum \
  --sample_name test_sample_WTS \
  --dataset TEST \
  --salmon "$PWD/../rawdata/test_data/dragen/TEST.quant.genes.sf" \
  --arriba_pdf "$PWD/../rawdata/test_data/dragen/arriba/fusions.pdf" \
  --arriba_tsv "$PWD/../rawdata/test_data/dragen/arriba/fusions.tsv"  \
  --report_dir "$PWD/../rawdata/test_data/RNAsum" \
  --save_tables FALSE \
  --filter TRUE

The HTML report test_sample_WTS.RNAsum.html will be created in the inst/rawdata/test_data/dragen/RNAsum folder.

What’s in the report?

RNAsum generates an interactive HTML report with the following core sections:

Findings summary: summary of genes listed across various report sections
Mutated genes: expression of genes with somatic mutations (requires WGS)
Fusion genes: detected gene fusions with functional annotations
Structural variants: expression of genes located within structural variants (requires WGS)
CN altered genes: expression in CN-gained/lost regions (requires WGS)
Cancer genes: expression of cancer-associated genes

View example reports.

Available reference datasets

RNAsum includes 33 TCGA cancer type cohorts for comparative analysis:

Cancer Type	Dataset Code	Samples
Pan-Cancer	`PANCAN`	330
Breast Invasive Carcinoma	`BRCA`	300
Lung Adenocarcinoma	`LUAD`	300
Pancreatic Adenocarcinoma	`PAAD`	150

See the complete TCGA projects summary table.

Documentation

Resource	Link
Full documentation	umccr.github.io/RNAsum
Workflow details	Workflow details
Report structure	Report structure
TCGA datasets	TCGA projects summary

Contributing

We welcome contributions! Please see our Code of Conduct and contribution guidelines.

Reporting Issues

Found a bug or have a feature request? Open an issue.

Citation

If you use RNAsum please cite:

Kanwal S, Marzec J, Diakumis P, Hofmann O, Grimmond S (2024). “RNAsum: An R package to comprehensively post-process, summarise and visualise genomics and transcriptomics data.” version 1.1.0, https://umccr.github.io/RNAsum/

A BibTeX entry for LaTeX users is

@Unpublished{,
  title = {RNAsum: An R package to comprehensively post-process, summarise and visualise genomics and transcriptomics data},
  author = {Sehrish Kanwal and Jacek Marzec and Peter Diakumis and Oliver Hofmann and Sean Grimmond},
  year = {2024},
  note = {version 1.1.0},
  url = {https://umccr.github.io/RNAsum/},
}