From fc09725bf69a7c8d36602efd45c8a74d3c684606 Mon Sep 17 00:00:00 2001 From: Christopher Hakkaart Date: Mon, 23 Mar 2026 15:36:38 +1300 Subject: [PATCH 1/2] Update rnaseq example Signed-off-by: Christopher Hakkaart --- src/pages/rna-seq-pipeline.md | 118 ++++++++++++++++------------------ 1 file changed, 56 insertions(+), 62 deletions(-) diff --git a/src/pages/rna-seq-pipeline.md b/src/pages/rna-seq-pipeline.md index 0532ce2c1..4a3490771 100644 --- a/src/pages/rna-seq-pipeline.md +++ b/src/pages/rna-seq-pipeline.md @@ -7,95 +7,89 @@ layout: "@layouts/ExampleLayout.astro"

RNA-Seq pipeline

- This example shows how to put together a basic RNA-Seq pipeline. It maps a collection of read-pairs to a given reference genome and outputs the respective transcript model. + This pipeline shows an example of a basic 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.

```groovy -#!/usr/bin/env nextflow - -/* - * The following pipeline parameters specify the reference genomes - * and read pairs and can be provided as command line options - */ -params.reads = "$baseDir/data/ggal/ggal_gut_{1,2}.fq" -params.transcriptome = "$baseDir/data/ggal/ggal_1_48850000_49020000.Ggal71.500bpflank.fa" +// Parameter inputs +params.reads = "${baseDir}/data/ggal/ggal_gut_{1,2}.fq" +params.transcriptome = "${baseDir}/data/ggal/ggal_1_48850000_49020000.Ggal71.500bpflank.fa" params.outdir = "results" +params.multiqc = "${baseDir}/multiqc" + +// Component imports +include { RNASEQ } from './modules/rnaseq' +include { MULTIQC } from './modules/multiqc' + + +// Workflow block workflow { - read_pairs_ch = channel.fromFilePairs( params.reads, checkIfExists: true ) - INDEX(params.transcriptome) - FASTQC(read_pairs_ch) - QUANT(INDEX.out, read_pairs_ch) -} + log.info """\ + R N A S E Q - N F P I P E L I N E + =================================== + transcriptome: ${params.transcriptome} + reads : ${params.reads} + outdir : ${params.outdir} + """.stripIndent() -process INDEX { - tag "$transcriptome.simpleName" + read_pairs_ch = channel.fromFilePairs(params.reads, checkIfExists: true, flat: true) - input: - path transcriptome + (fastqc_ch, quant_ch) = RNASEQ(read_pairs_ch, params.transcriptome) - output: - path 'index' + multiqc_files_ch = fastqc_ch.mix(quant_ch).collect() - script: - """ - salmon index --threads $task.cpus -t $transcriptome -i index - """ + MULTIQC(multiqc_files_ch, params.multiqc) + + workflow.onComplete = { + log.info( + workflow.success + ? "\nDone! Open the following report in your browser --> ${params.outdir}/multiqc_report.html\n" + : "Oops .. something went wrong" + ) + } } +``` -process FASTQC { - tag "FASTQC on $sample_id" - publishDir params.outdir + - input: - tuple val(sample_id), path(reads) +### Synopsis - output: - path "fastqc_${sample_id}_logs" +The pipeline uses two imported components: - script: - """ - fastqc.sh "$sample_id" "$reads" - """ -} +

• RNASEQ: a subworkflow that contains three processes:

-process QUANT { - tag "$pair_id" - publishDir params.outdir +

• INDEX: creates a Salmon index from the transcriptome (runs once)

- input: - path index - tuple val(pair_id), path(reads) +

• FASTQC: analyzes each sample in parallel

- output: - path pair_id +

• QUANT: quantifies transcripts for each sample after indexing completes

- script: - """ - salmon quant --threads $task.cpus --libType=U -i $index -1 ${reads[0]} -2 ${reads[1]} -o $pair_id - """ -} -``` +

• MULTIQC: aggregates all quality control and quantification outputs into a comprehensive HTML report

- +The `workflow` block uses `channel.fromFilePairs` to create a channel of paired-end read files. It passes the reads and transcriptome to the `RNASEQ` subworkflow, then mixes the FastQC and quantification outputs and passes them to `MULTIQC`. + +
-### Try it in your computer +### Get started -To run this pipeline on your computer, you will need: +To run this pipeline: -- Unix-like operating system -- Java 17 (or higher) -- Docker +

1. Install Nextflow (version 25.10 or later)

-Install Nextflow by entering the following command in the terminal: +

2. Install Docker

- $ curl -fsSL get.nextflow.io | bash +

3. Run the pipeline directly from its GitHub repository:

-Then launch the pipeline with this command: +
- $ nextflow run rnaseq-nf -with-docker +``` +nextflow run nextflow-io/rnaseq-nf -profile docker +``` + +
-It will automatically download the pipeline [GitHub repository](https://github.com/nextflow-io/rnaseq-nf) and the associated Docker images, thus the first execution may take a few minutes to complete depending on your network connection. +
-**NOTE**: To run this example with versions of Nextflow older than 22.04.0, you must include the `-dsl2` flag with `nextflow run`. +See the [rnaseq-nf](https://github.com/nextflow-io/rnaseq-nf) GitHub repository for all of the pipeline code and the [rnaseq-nf tutorial](https://nextflow.io/docs/stable/tutorials/rnaseq-nf.html) for a full pipeline description. From 06dc64d8f7ebf6c9646929fb8996f0ca55a14cc9 Mon Sep 17 00:00:00 2001 From: Christopher Hakkaart Date: Mon, 23 Mar 2026 15:38:57 +1300 Subject: [PATCH 2/2] Tidy duplicated links --- src/components/Footer.astro | 7 ------- src/components/Menu/Menu.tsx | 6 ------ 2 files changed, 13 deletions(-) diff --git a/src/components/Footer.astro b/src/components/Footer.astro index c4507d2d9..0c34df25c 100644 --- a/src/components/Footer.astro +++ b/src/components/Footer.astro @@ -111,13 +111,6 @@ import LogoSeqera from "../../public/img/assets/Logo_Seqera_white.svg"; >Machine Learning pipeline -
  • - Simple RNAseq pipeline -
  • { Machine Learning pipeline
    • -
  • - - Simple RNAseq pipeline - External link - -
  • Implementation patterns