Getting started with rnaseq-nf

rnaseq-nf is a basic Nextflow pipeline for RNA-Seq analysis that performs quality control, transcript quantification, and result aggregation. The pipeline processes paired-end FASTQ files, generates quality control reports with FastQC, quantifies transcripts with Salmon, and produces a unified report with MultiQC.

This tutorial describes the architecture of the rnaseq-nf pipeline and provides instructions on how to run it.

Pipeline architecture

The pipeline is organized into modular workflows and processes that coordinate data flow from input files through analysis steps to final outputs.

Entry workflow

The entry workflow orchestrates the entire pipeline by coordinating input parameters and data flow:

flowchart TB subgraph " " subgraph params v0["transcriptome"] v1["reads"] v5["multiqc"] v2["outdir"] end v4([RNASEQ]) v6([MULTIQC]) v0 --> v4 v1 --> v4 v4 --> v6 v5 --> v6 end

Data flow:

The transcriptome and reads parameters are passed to the RNASEQ subworkflow, which performs indexing, quality control, and quantification.
The outputs from RNASEQ, along with the MultiQC configuration (multiqc), are passed to the MULTIQC module, which aggregates results into a unified HTML report.
The outdir parameter defines where all results are published.

`RNASEQ`

The RNASEQ subworkflow coordinates three processes that run in parallel and sequence:

flowchart TB subgraph RNASEQ subgraph take v0["read_pairs_ch"] v1["transcriptome"] end v2([INDEX]) v4([FASTQC]) v6([QUANT]) subgraph emit v8["fastqc"] v9["quant"] end v1 --> v2 v0 --> v4 v0 --> v6 v2 --> v6 v4 --> v8 v6 --> v9 end

Inputs (take:):

read_pairs_ch: A channel of paired-end read files
transcriptome: A reference transcriptome file

Data flow (main:):

INDEX creates a Salmon index from the transcriptome input (runs once).
FASTQC analyzes the samples in the read_pairs_ch channel in parallel (runs independently for each sample).
QUANT quantifies transcripts using the index from INDEX and the samples in the read_pairs_ch channel (runs for each sample after INDEX completes).

Outputs (emit:):

fastqc: The results from FASTQC
quant: The results from QUANT

`MULTIQC`

The MULTIQC process aggregates all quality control and quantification outputs into a comprehensive HTML report.

Inputs:

Input files: All collected outputs from the RNASEQ subworkflow (FastQC reports and Salmon quantification files).
config: MultiQC configuration files and branding (logo, styling).

Process execution:

MULTIQC scans all input files, extracts metrics and statistics, and generates a unified report.

Outputs:

multiqc_report.html: A single consolidated HTML report providing an overview of:
- General stats
- Salmon fragment length distribution
- FastQC quality control
- Software versions

Pipeline parameters

The pipeline behavior can be customized using command-line parameters to specify input data, output locations, and configuration files.

The pipeline accepts the following command-line parameters:

--reads: Path to paired-end FASTQ files (default: data/ggal/ggal_gut_{1,2}.fq).
--transcriptome: Path to reference transcriptome FASTA (default: data/ggal/ggal_1_48850000_49020000.Ggal71.500bpflank.fa).
--outdir: Output directory for results (default: results).
--multiqc: Path to MultiQC configuration directory (default: multiqc).

Configuration profiles

Configuration profiles allow you to customize how and where the pipeline runs by specifying the -profile flag. Multiple profiles can be specified as a comma-separated list. Profiles are defined in the nextflow.config file in the base directory.

Software profiles

Software profiles specify how software dependencies for processes should be provisioned:

conda: Provision a Conda environment for each process based on its required Conda packages
docker: Use a Docker container which contains all required dependencies
singularity: Use a Singularity container which contains all required dependencies
wave: Provision a Wave container for each process based on its required Conda packages

Note

The respective container runtime or package manager must be installed to use these profiles.

Execution profiles

Execution profiles specify the compute and storage environment used by the pipeline:

slurm: Run on a SLURM HPC cluster
batch: Run on AWS Batch
google-batch: Run on Google Cloud Batch
azure-batch: Run on Azure Batch

Note

Depending on your environment, you may need to configure underlying infrastructure such as resource pools, storage, and credentials.

Test data

The pipeline includes test data in the data/ggal/ directory for demonstration and validation purposes:

Paired-end FASTQ files from four tissue samples (gut, liver, lung, spleen):
- ggal_gut_{1,2}.fq
- ggal_liver_{1,2}.fq
- ggal_lung_{1,2}.fq
- ggal_spleen_{1,2}.fq
Reference transcriptome:
- ggal_1_48850000_49020000.Ggal71.500bpflank.fa

By default, only the gut sample is processed. You can use the all-reads profile to process all four tissue samples.

Quick start

The rnaseq-nf pipeline is executable out-of-the-box. This section provides examples for running the pipeline with different configurations.

Basic execution

Run the pipeline with default parameters using Docker:

nextflow run nextflow-io/rnaseq-nf -profile docker

Configuring individual parameters

Override default parameters to use custom input files and output locations:

nextflow run nextflow-io/rnaseq-nf \
  --reads '/path/to/reads/*_{1,2}.fastq.gz' \
  --transcriptome '/path/to/transcriptome.fa' \
  --outdir 'my_results' \
  -profile docker

Using profiles

Specify configuration profiles to customize runtime environments and data sources:

# Use Conda to provision software dependencies
nextflow run nextflow-io/rnaseq-nf -profile conda

# Run on a SLURM cluster
nextflow run nextflow-io/rnaseq-nf -profile slurm

# Combine multiple profiles: process all reads using Docker
nextflow run nextflow-io/rnaseq-nf -profile all-reads,docker

Tip

See Configuration profiles for more information about profiles.

Expected outputs

The rnaseq-nf pipeline produces the following outputs in the results directory:

results/
├── fastqc_<SAMPLE_ID>_logs/      # FastQC quality reports per sample
│   ├── <SAMPLE_ID>_1_fastqc.html
│   ├── <SAMPLE_ID>_1_fastqc.zip
│   ├── <SAMPLE_ID>_2_fastqc.html
│   └── <SAMPLE_ID>_2_fastqc.zip
└── multiqc_report.html           # Aggregated QC and Salmon report

The MultiQC report (multiqc_report.html) can be viewed in a web browser.