nf-core/isoseq is a bioinformatics best-practice analysis pipeline for Isoseq gene annotation with uLTRA and TAMA. Starting from raw isoseq subreads, the pipeline:

  • Generates the Circular Consensus Sequences (CSS)

  • Clean and polish CCS to create Full Length Non Chimeric (FLNC) reads

  • Maps FLNCs on the genome

  • Define and clean gene models


The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It uses Docker/Singularity containers making installation trivial and results highly reproducible. The Nextflow DSL2 implementation of this pipeline uses one container per process which makes it much easier to maintain and update software dependencies. Where possible, these processes have been submitted to and installed from nf-core/modules in order to make them available to all nf-core pipelines, and to everyone within the Nextflow community!

On release, automated continuous integration tests run the pipeline on a full-sized dataset on the AWS cloud infrastructure. This ensures that the pipeline runs on AWS, has sensible resource allocation defaults set to run on real-world datasets, and permits the persistent storage of results to benchmark between pipeline releases and other analysis sources. The results obtained from the full-sized test can be viewed on the nf-core website.

Pipeline summary

  1. Generate CCS consensuses from raw isoseq subreads (PBCCS)
  2. Remove primer sequences from consensuses (LIMA)
  3. Detect and remove chimeric reads (ISOSEQ3 REFINE)
  4. Convert bam file into fasta file (BAMTOOLS CONVERT)
  5. Select reads with a polyA tail and trim it (GSTAMA_POLYACLEANUP)
  6. uLTRA path: decompress FLNCs (GUNZIP)
  7. uLTRA path: index GTF file for mapping (uLTRA)
  8. Map consensuses on the reference genome (MINIMAP2 or uLTRA)
  9. Clean gene models (TAMA collapse)
  10. Merge annotations by sample (TAMA merge)

Quick Start

  1. Install Nextflow (>=22.10.1)

  2. Install any of Docker, Singularity (you can follow this tutorial), Podman, Shifter or Charliecloud for full pipeline reproducibility (you can use Conda both to install Nextflow itself and also to manage software within pipelines. Please only use it within pipelines as a last resort; see docs).

  3. Download the pipeline and test it on a minimal dataset with a single command:

    nextflow run nf-core/isoseq -profile test,YOURPROFILE --outdir <OUTDIR>

    Note that some form of configuration will be needed so that Nextflow knows how to fetch the required software. This is usually done in the form of a config profile (YOURPROFILE in the example command above). You can chain multiple config profiles in a comma-separated string.

    • The pipeline comes with config profiles called docker, singularity, podman, shifter, charliecloud and conda which instruct the pipeline to use the named tool for software management. For example, -profile test,docker.
    • Please check nf-core/configs to see if a custom config file to run nf-core pipelines already exists for your Institute. If so, you can simply use -profile <institute> in your command. This will enable either docker or singularity and set the appropriate execution settings for your local compute environment.
    • If you are using singularity, please use the nf-core download command to download images first, before running the pipeline. Setting the NXF_SINGULARITY_CACHEDIR or singularity.cacheDir Nextflow options enables you to store and re-use the images from a central location for future pipeline runs.
    • If you are using conda, it is highly recommended to use the NXF_CONDA_CACHEDIR or conda.cacheDir settings to store the environments in a central location for future pipeline runs.
  4. Start running your own analysis!

    nextflow run nf-core/isoseq --input samplesheet.csv --outdir <OUTDIR> --genome <GENOME NAME (e.g. GRCh37)> --primers <PRIMER FASTA> -profile <docker/singularity/podman/shifter/charliecloud/conda/institute>


The nf-core/isoseq pipeline comes with documentation about the pipeline usage, parameters and output.


nf-core/isoseq was originally written by Sébastien Guizard.

We thank the following people for their extensive assistance in the development of this pipeline:

This pipeline has been developed as part of the GENE-SWitCH project. This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under the grant agreement n° 817998.

Contributions and Support

If you would like to contribute to this pipeline, please see the contributing guidelines.

For further information or help, don’t hesitate to get in touch on the Slack #isoseq channel (you can join with this invite).


  • Isoseq3
  • TAMA: Kuo, R.I., Cheng, Y., Zhang, R. et al. Illuminating the dark side of the human transcriptome with long read transcript sequencing. BMC Genomics 21, 751 (2020). 10.1186/s12864-020-07123-7
  • Minimap2: Li, H. (2018). Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics, 34:3094-3100., Li, H. (2021). New strategies to improve minimap2 alignment accuracy. Bioinformatics, 37:4572-4574. 10.1093/bioinformatics/btab705
  • uLTRA: Kristoffer Sahlin, Veli Mäkinen, Accurate spliced alignment of long RNA sequencing reads, Bioinformatics, Volume 37, Issue 24, 15 December 2021, Pages 4643–4651. 10.1093/bioinformatics/btab540
  • Samtools: Danecek P, Bonfield JK, Liddle J, Marshall J, Ohan V, Pollard MO, Whitwham A, Keane T, McCarthy SA, Davies RM, Li H, Twelve years of SAMtools and BCFtools, GigaScience (2021) 10(2) giab008. 10.1093/gigascience/giab007
  • MultiQC: Philip Ewels, Måns Magnusson, Sverker Lundin, Max Käller, MultiQC: summarize analysis results for multiple tools and samples in a single report, Bioinformatics, Volume 32, Issue 19, 1 October 2016, Pages 3047–3048. 10.1093/bioinformatics/btw354
  • samtools: Danecek P, Bonfield JK, Liddle J, Marshall J, Ohan V, Pollard MO, Whitwham A, Keane T, McCarthy SA, Davies RM, Li H, Twelve years of SAMtools and BCFtools, GigaScience (2021) 10(2) 10.1093/gigascience/giab008

An extensive list of references for the tools used by the pipeline can be found in the file.

You can cite the nf-core publication as follows:

The nf-core framework for community-curated bioinformatics pipelines.

Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.

Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.