Introduction

nf-core/fastqtoconsensus is a bioinformatics best-practice analysis pipeline for Fastq -> Filtered Consensus Pipeline.

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. Read QC (FastQC)
2. Present QC for raw reads (MultiQC)
3. Create an tagged unaligned bam (fgbio FastqToBam)
4. Align fastq and merge with tagged bam (fgbio Zipperbams ; samtools fastq ; bwa mem)
5. Group reads by umi (fgbio GroupReadsByUmi)
6. Call consensus (fgbio CallMollecularConsensus)

7. Filter consensus (fgbio FilterConsensusReads)

   Quick Start

8. Install Nextflow (>=21.10.3)

9. 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).

10. Clone the pipeline

11. Clone the pipeline in your scratch folder

    console git clone https://github.com/chelauk/nf-core-demultiplex-methylation.git

12. Edit your .bashrc file to set the following variables:

   # Set all the Singularity cache dirs to Scratch
   export SINGULARITYCACHEDIR=/your/selected/scratch/folder/singularityimgs
   export SINGULARITYTMPDIR=$SINGULARITYCACHEDIR/tmp
   export SINGULARITYLOCALCACHEDIR=$SINGULARITYCACHEDIR/localcache
   export SINGULARITYPULLFOLDER=$SINGULARITYCACHEDIR/pull
   # match the NXFSINGULARITYCACHEDIR
   export NXFSINGULARITYCACHEDIR=/your/selected/scratch/folder/singularity_imgs
   

1. Start running your own analysis edit a sbatch script runNextflow.sh

   
   #!/bin/bash -l
   #SBATCH --job-name=demultiplex
   #SBATCH --output=nextflow_out.txt
   #SBATCH --partition=master-worker
   #SBATCH --ntasks=1
   #SBATCH --time=120:00:00
   
   nextflow run /location/of/your/nextflow_pipelines/nf-core-fastqtoconsensus \
   --input input.csv \
   -profile slurm,singularity \
   -c local.config \
   -resume
   

2. Start your sbatch job:

console sbatch runNextflow.sh `

1. local.config

   Adjust your local config file to match requirements. parameters can be set for individual processes or processes can be grouped with labels

   
   process {
     executor = 'slurm'
     errorStrategy = {task.exitStatus in [143,137,104,134,139,255] ? 'retry' : 'finish'}
     maxErrors = '-1'
     maxRetries = 5
   
     withLabel:processhigh {
       memory = 64.GB
       cpus   = 24
       time   = 48.h
     }
     withLabel:processlow {
       cpus   = 1
       memory = 8.GB
       time   = 2.h
     }
     withLabel:processlong {
       memory = 16.GB
       cpus = 1
       time = 72.h
     }
     withLabel:processmedium {
       memory = 16.GB
       time = 8.h
     }
     withName:ALIGN {
       cpus = 48
       memory = 384.GB
       time = 48.h
     }
   }
   

• 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.

1. Start running your own analysis!

<!-- TODO nf-core: Update the example "typical command" below used to run the pipeline -->

console nextflow run nf-core/fastqtoconsensus --input samplesheet.csv --outdir <OUTDIR> --genome GRCh37 -profile <docker/singularity/podman/shifter/charliecloud/conda/institute>

Documentation

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

Credits

nf-core/fastqtoconsensus was originally written by Chela James.

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

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 #fastqtoconsensus channel (you can join with this invite).

Citations

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