Introduction

nfcore/nanoseq is a bioinformatics analysis pipeline that can be used to perform basecalling, demultiplexing, mapping and QC of Nanopore DNA/RNA sequencing data.

The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker containers making installation trivial and results highly reproducible.

Pipeline Summary

1. Basecalling and/or demultiplexing (Guppy or qcat; optional)
2. Sequencing QC (pycoQC, NanoPlot)
3. Raw read QC (NanoPlot, FastQC)
4. Alignment (GraphMap2 or minimap2)
   • Both aligners are capable of performing unspliced and spliced alignment. Sensible defaults will be applied automatically based on a combination of the input data and user-specified parameters
   • Each sample can be mapped to its own reference genome if multiplexed in this way
   • Convert SAM to co-ordinate sorted BAM and obtain mapping metrics (SAMtools)
5. Create bigWig (BEDTools, bedGraphToBigWig) and bigBed (BEDTools, bedToBigBed) coverage tracks for visualisation
6. Present QC for alignment results (MultiQC)
7. Transcript reconstruction and quantification (bambu or StringTie2)
   • When StringTie2 is chosen, each sample can be processed individually and combined. After which, featureCounts will be used for both gene and transcript quantification.
   • bambu performs both transcript reconstruction and quantification.
8. Differential expression analysis with (DESeq2 or DEXSeq for condition comparison
   • At least 3 replicates for each condtion need to be satistified for this step.

Quick Start

i. Install nextflow

ii. Install one of docker or singularity

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

nextflow run nf-core/nanoseq -profile test,<docker/singularity/institute>

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.

iv. Start running your own analysis!

nextflow run nf-core/nanoseq \
    --input samplesheet.csv \
    --protocol DNA \
    --input_path ./fast5/ \
    --flowcell FLO-MIN106 \
    --kit SQK-LSK109 \
    --barcode_kit SQK-PBK004 \
    -profile <docker/singularity/institute>

See usage docs for all of the available options when running the pipeline. An example input samplesheet for performing both basecalling and demultiplexing can be found here.

Documentation

The nf-core/nanoseq pipeline comes with documentation about the pipeline, found in the docs/ directory:

1. Installation
2. Pipeline configuration
   • Local installation
   • Adding your own system config
   • Reference genomes
3. Running the pipeline
4. Output and how to interpret the results
5. Troubleshooting

Credits

nf-core/nanoseq was originally written by Chelsea Sawyer and Harshil Patel from The Bioinformatics & Biostatistics Group for use at The Francis Crick Institute, London. Other primary contributors include Laura Wratten, Ying Chen, Yuk Kei Wan and Jonathan Goeke from the Genome Institute of Singapore, Johannes Alneberg and Franziska Bonath from SciLifeLab, Sweden.

Many thanks to others who have helped out along the way too, including (but not limited to): @crickbabs, @AnnaSyme.

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 Slack (you can join with this invite).

Citation

If you use nf-core/nanoseq for your analysis, please cite it using the following doi: 10.5281/zenodo.3697959

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.
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