cDNAnano

rnaseq analysis alignment, assembly, quantification of isoforms

Introduction

nf-core-wgsnano is a bioinformatics best-practice analysis pipeline for Nanopore Whole Genome Sequencing.

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.

Pipeline summary

1. Basecalling (Guppy) - with GPU run option
2. Basecalling QC (PycoQC,NanoPlot)
3. Alignment (Guppy with minimap2)
4. Quantification

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 (this pipeline can NOT be run with conda)). This requirement is not needed for running the pipeline in WashU RIS cluster.

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

   nextflow run dhslab/nf-core-wgsnano -profile test,YOURPROFILE(S) --outdir <OUTDIR>

4. Start running your own analysis!

   nextflow run dhslab/nf-core-wgsnano --input samplesheet.csv --fasta <FASTA> -profile <docker/singularity/podman/shifter/charliecloud/conda/institute> --outdir <OUTDIR>

Usage

Required parameters:

1. Input samplesheet.cvs which provides directory paths for fast5 raw reads and their metadata. this can be provided either in a configuration file or as --input path/to/samplesheet.cvs command line parameter. Example sheet located in assets/samplesheet.csv.
2. Reference genome fasta file, either in a configuration file or as --fasta path/to/genome.fasta command line parameter.

Nanopore and runtime default parameters:

The following parameters are set to the shown default values, but should be modified when required in command line, or in user-provided config files:

• --basecall_config dna_r10.4.1_e8.2_400bps_modbases_5mc_cg_sup.cfg -> Guppy's config file for basecalling
• --nanopore_reads_type ont_r10_q20 -> PEPPER's reads-type option
• --use_gpu true -> Enable GPU for basecalling (it can be disabled, but basecallling will take significantly longer time with CPU run)

Running a pipeline test in LSF cluster (configured to WashU RIS cluster environment)

1) Directly from GitHub:

NXF_HOME=${PWD}/.nextflow LSF_DOCKER_VOLUMES="/storage1/fs1/dspencer/Active:/storage1/fs1/dspencer/Active $HOME:$HOME" bsub -g /dspencer/nextflow -G compute-dspencer -q dspencer -e nextflow_launcher.err -o nextflow_launcher.log -We 2:00 -n 2 -M 12GB -R "select[mem>=16000] span[hosts=1] rusage[mem=16000]" -a "docker(ghcr.io/dhslab/docker-nextflow)" nextflow run dhslab/nf-core-wgsnano -r dev -profile test,ris,dhslab --outdir results

Notice that three profiles are used here:

1. test-> to provide input and fasta paths for the test run
2. ris-> to set general configuration for RIS LSF cluster
3. dhslab-> to set lab-specific cluster configuration

2) Alternatively, clone the repository and run the pipeline from local directory:

git clone https://github.com/dhslab/nf-core-wgsnano.git
cd nf-core-wgsnano/
chmod +x bin/*
LSF_DOCKER_VOLUMES="/storage1/fs1/dspencer/Active:/storage1/fs1/dspencer/Active $HOME:$HOME" bsub -g /dspencer/nextflow -G compute-dspencer -q dspencer -e nextflow_launcher.err -o nextflow_launcher.log -We 2:00 -n 2 -M 12GB -R "select[mem>=16000] span[hosts=1] rusage[mem=16000]" -a "docker(ghcr.io/dhslab/docker-nextflow)" "NXF_HOME=${PWD}/.nextflow ; nextflow run main.nf -profile test,ris,dhslab --outdir results"

Directory tree for test run output:

.
├── multiqc
│   ├── multiqc_data
│   └── multiqc_plots
│       ├── pdf
│       ├── png
│       └── svg
├── pipeline_info
└── samples
    ├── sample_1
    │   ├── fastq
    │   ├── methylation_calls
    │   │   ├── accumulated
    │   │   └── stranded
    │   ├── pepper
    │   │   ├── haplotagged_bam
    │   │   └── vcf
    │   └── qc
    │       ├── mosdepth
    │       └── pycoqc
    └── sample_2
        ├── fastq
        ├── methylation_calls
        │   ├── accumulated
        │   └── stranded
        ├── pepper
        │   ├── haplotagged_bam
        │   └── vcf
        └── qc
            ├── mosdepth
            └── pycoqc

Notes:

• The pipeline is developed and optimized to be run in WashU RIS (LSF) HPC, but could be deployed in any HPC environment supported by Nextflow.
• The pipeline does NOT support conda because some of the tools used are not available as conda packages.
• The pipeline can NOT be fully tested in a personal computer as basecalling step is computationally intense even for small test files. For testing/development purposes, the pipeline can be run in stub (dry-run) mode (see below).

Stub (dry-run) for testing and development purposes

• stub run requires aws cli and docker (or any other Containerization software)
• steps:
  1. download the pipeline
  2. download the stub-data results generated from pre-run test analysis (requires aws cli installed). It should be downloaded in the pipeline directory (wgsnano/)
  3. Run the pipeline in stub mode

git clone https://github.com/dhslab/nf-core-wgsnano.git
cd nf-core-wgsnano/
aws s3 sync s3://davidspencerlab/nextflow/wgsnano/test-datasets/stub-test/ stub-test/ --no-sign-request
nextflow run main.nf -stub -profile stub,docker --outdir results