nf-core pipeline · cage, cage-seq, cageseq-data
nf-core/cageseq
Processes CAGE-seq data through adapter trimming, alignment, and tag clustering for gene expression analysis.
What nf-core/cageseq is
nf-core/cageseq is an automated bioinformatics analysis pipeline designed for processing cap analysis of gene expression sequencing (CAGE-seq) datasets. CAGE-seq is a specialised RNA sequencing protocol that captures the 5' ends of capped transcripts to identify transcription start sites and measure expression levels. The pipeline was originally written by Kevin Menden and Tristan Kast, with subsequent updates contributed by Matthias Hörtenhuber.
Analysing CAGE-seq data requires removing technical sequencing artefacts—including adapters, EcoP15 enzyme recognition sites, and artificial 5' guanine nucleotides—followed by accurate mapping and aggregation of transcription initiation events. nf-core/cageseq resolves these needs by combining pre-processing, optional ribosomal RNA removal, reference genome alignment, and tag clustering into an end-to-end, reproducible workflow.
What the pipeline does
The pipeline accepts demultiplexed FASTQ files and begins by evaluating raw read quality using FastQC. Cutadapt is then employed to trim adapters, EcoP15 linker sequences, and 5' guanine artefacts. Users can optionally route reads through SortMeRNA to remove contaminating ribosomal RNA sequences, after which a second FastQC inspection assesses the trimmed and filtered data.
Cleaned reads are aligned to a reference genome using either STAR or Bowtie 1. Following alignment, paraclu performs CAGE tag counting and identifies clusters corresponding to transcription start regions. Quality assessment of the clustered tags is performed using RSeQC, and MultiQC aggregates all raw, alignment, and clustering metrics into a single summary report.
Key facts
- Input format
- Demultiplexed FASTQ files (.fastq, .fastq.gz)
- Trimming tool
- cutadapt
- rRNA removal tool
- SortMeRNA (optional)
- Supported aligners
- STAR, Bowtie 1
- Tag clustering tool
- paraclu
- Quality control tools
- FastQC, RSeQC, MultiQC
- Workflow framework
- Nextflow
- Original authors
- Kevin Menden, Tristan Kast
What you can use it for
- Transcription start site mapping
- Accurately map the 5' ends of capped RNA reads against a reference genome using STAR or Bowtie 1 to identify precise transcription initiation coordinates.
- CAGE tag clustering and quantification
- Cluster closely situated CAGE tags and quantify transcription initiation activity across genomic loci using paraclu.
- CAGE-seq library quality inspection
- Evaluate sequencing data quality, check trimming performance for EcoP15 and 5' guanine artefacts, and review clustered tag distributions using FastQC, RSeQC, and MultiQC.
Run it here
The research agent runs nf-core/cageseq 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
- Accommodates CAGE-specific trimming requirements, including EcoP15 restriction sites and added 5' guanine residues.
- Provides flexibility by supporting both STAR and Bowtie 1 aligners for genomic mapping.
- Includes multiple quality control checkpoints covering raw reads, filtered sequences, and final tag clusters.
Watch out for
- Requires demultiplexed FASTQ input files, meaning multiplexed sequencing runs must be separated before execution.
- Restricts genomic alignment options to STAR and Bowtie 1.
Questions
Can I run nf-core/cageseq without my own cluster?
The research agent runs nf-core/cageseq 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/cageseq under?
MIT, like every nf-core pipeline.
Which aligners are available in the pipeline?
Users can choose between STAR and Bowtie 1 to align reads to the reference genome.
How are ribosomal RNA reads handled?
The pipeline includes an optional rRNA filtering step using SortMeRNA to deplete ribosomal sequences before read alignment.
What method is used to define and count CAGE clusters?
The pipeline uses paraclu to aggregate mapped CAGE tags into clusters and compute tag counts across genomic positions.
What specific artefacts does the trimming step target?
Cutadapt removes standard sequencing adapters, EcoP15 enzyme sequences, and extra 5' guanine nucleotides.
How to cite it
Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen. The nf-core framework for community-curated bioinformatics pipelines. Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.
Written from nf-core/cageseq's own README. Statements citing a figure that does not appear there are removed before publication. Licence and running details come from our registry.