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nf-core/nanoseq: Usage

Table of contents

Introduction

Nextflow handles job submissions on SLURM or other environments, and supervises running the jobs. Thus the Nextflow process must run until the pipeline is finished. We recommend that you put the process running in the background through screen / tmux or similar tool. Alternatively you can run nextflow within a cluster job submitted your job scheduler.

It is recommended to limit the Nextflow Java virtual machines memory. We recommend adding the following line to your environment (typically in ~/.bashrc or ~./bash_profile):

NXF_OPTS='-Xms1g -Xmx4g'

Running the pipeline

The typical command for running the pipeline is as follows:

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

This will launch the pipeline with the docker configuration profile. See below for more information about profiles.

Note that the pipeline will create the following files in your working directory:

work            # Directory containing the nextflow working files
results         # Finished results (configurable, see below)
.nextflow_log   # Log file from Nextflow
# Other nextflow hidden files, eg. history of pipeline runs and old logs.

Updating the pipeline

When you run the above command, Nextflow automatically pulls the pipeline code from GitHub and stores it as a cached version. When running the pipeline after this, it will always use the cached version if available - even if the pipeline has been updated since. To make sure that you're running the latest version of the pipeline, make sure that you regularly update the cached version of the pipeline:

nextflow pull nf-core/nanoseq

Reproducibility

It's a good idea to specify a pipeline version when running the pipeline on your data. This ensures that a specific version of the pipeline code and software are used when you run your pipeline. If you keep using the same tag, you'll be running the same version of the pipeline, even if there have been changes to the code since.

First, go to the nf-core/nanoseq releases page and find the latest version number - numeric only (eg. 1.3.1). Then specify this when running the pipeline with -r (one hyphen) - eg. -r 1.3.1.

This version number will be logged in reports when you run the pipeline, so that you'll know what you used when you look back in the future.

Main arguments

-profile

Use this parameter to choose a configuration profile. Profiles can give configuration presets for different compute environments.

Several generic profiles are bundled with the pipeline which instruct the pipeline to use software packaged using different methods (Docker, Singularity, Conda) - see below.

We highly recommend the use of Docker or Singularity containers for full pipeline reproducibility, however when this is not possible, Conda is also supported.

The pipeline also dynamically loads configurations from https://github.com/nf-core/configs when it runs, making multiple config profiles for various institutional clusters available at run time. For more information and to see if your system is available in these configs please see the nf-core/configs documentation.

Note that multiple profiles can be loaded, for example: -profile test,docker - the order of arguments is important! They are loaded in sequence, so later profiles can overwrite earlier profiles.

If -profile is not specified, the pipeline will run locally and expect all software to be installed and available on the PATH. This is not recommended.

  • docker
    • A generic configuration profile to be used with Docker
    • Pulls software from dockerhub: nfcore/nanoseq
  • singularity
  • conda
    • Please only use Conda as a last resort i.e. when it's not possible to run the pipeline with Docker or Singularity.
    • A generic configuration profile to be used with Conda
    • Pulls most software from Bioconda
  • test
    • A profile with a complete configuration for automated testing
    • Includes links to test data so needs no other parameters

--input

You will need to create a file with information about the samples in your experiment/run before executing the pipeline. Use this parameter to specify its location. It has to be a comma-separated file with 5 columns and a header row:

Column Description
sample Sample name without spaces.
sample_path Full path to FastQ file if previously demultiplexed or to BAM file if previously aligned. FastQ File has to be zipped and have the extension ".fastq.gz" or ".fq.gz". BAM file has to have the extension ".bam".
barcode Barcode identifier attributed to that sample during multiplexing. Must be an integer.
genome Genome fasta file for alignment. This can either be blank, a local path, or the appropriate key for a genome available in iGenomes config file. Must have the extension ".fasta", ".fasta.gz", ".fa" or ".fa.gz".
transcriptome Transcriptome fasta/gtf file for alignment. This can either be blank or a local path. Must have the extension ".fasta", ".fasta.gz", ".fa", ".fa.gz", ".gtf" or ".gtf.gz".
condition Name of condition. If there are two or more conditions with at least three samples in each, then DESeq2 and DEXseq will be performed for differential expression analysis.

Specifying a reference genome/transcriptome

Each sample in the sample sheet can be mapped to its own reference genome or transcriptome. Please see below for additional details required to fill in the genome and transcriptome columns appropriately:

  • If both genome and transcriptome are not specified then the mapping will be skipped for that sample.
  • If both genome and transcriptome are specified as local fasta files then the transcriptome will be preferentially used for mapping.
  • If genome is specified as a local fasta file and transcriptome is left blank then mapping will be performed relative to the genome.
  • If genome isnt specified and transcriptome is provided as a fasta file then mapping will be performed relative to the transcriptome.
  • If genome is specified as an AWS iGenomes key then the transcriptome column can be blank. The associated gtf file for the transcriptome will be automatically obtained in order to create a transcriptome fasta file. However, the reads will only be mapped to the transcriptome if --protocol cDNA or --protocol directRNA. If --protocol DNA then the reads will still be mapped to the genome essentially ignoring the gtf file.
  • If genome is specified as a local fasta file and transcriptome is a specified as a local gtf file then both of these will be used to create a transcriptome fasta file. However, the reads will only be mapped to the transcriptome if --protocol cDNA or --protocol directRNA. If --protocol DNA then the reads will still be mapped to the genome essentially ignoring the gtf file.

Skip basecalling/demultiplexing

As shown in the examples below, the accepted format of the file is slightly different if you would like to run the pipeline with or without basecalling/demultiplexing.

With basecalling and demultiplexing
Example samplesheet.csv
sample,sample_path,barcode,genome,transcriptome
Sample1,,1,mm10,
Sample2,,2,hg19,
Sample3,,3,/path/to/local/genome.fa,
Sample4,,4,,/path/to/local/transcriptome.fa
Sample5,,5,/path/to/local/genome.fa,/path/to/local/transcriptome.gtf
Sample6,,6,,
Example command
nextflow run nf-core/nanoseq \
    --input samplesheet.csv \
    --protocol cDNA \
    --input_path ./fast5/ \
    --flowcell FLO-MIN106 \
    --kit SQK-DCS109 \
    --barcode_kit EXP-NBD103 \
    -profile <docker/singularity/institute>
With basecalling but not demultiplexing
Example samplesheet.csv
sample,sample_path,barcode,genome,transcriptome
Sample1,,1,/path/to/local/genome.fa,

Only a single sample can be specified if you would like to skip demultiplexing

Example command
nextflow run nf-core/nanoseq \
    --input samplesheet.csv \
    --protocol cDNA \
    --input_path ./fast5/ \
    --flowcell FLO-MIN106 \
    --kit SQK-DCS108 \
    --skip_demultiplexing \
    -profile <docker/singularity/institute>
With demultiplexing but not basecalling
Example samplesheet.csv
sample,sample_path,barcode,genome,transcriptome
Sample1,,1,mm10,
Sample2,,2,hg19,
Sample3,,3,/path/to/local/genome.fa,
Sample4,,4,,/path/to/local/transcriptome.fa
Sample5,,5,/path/to/local/genome.fa,/path/to/local/transcriptome.gtf
Sample6,,6,,
Example command
nextflow run nf-core/nanoseq \
    --input samplesheet.csv \
    --protocol DNA \
    --input_path ./undemultiplexed.fastq.gz \
    --barcode_kit 'NBD103/NBD104' \
    --skip_basecalling \
    -profile <docker/singularity/institute>
Without both basecalling and demultiplexing
Example samplesheet.csv
sample,sample_path,barcode,genome,transcriptome
Sample1,SAM101A1.fastq.gz,,mm10
Sample2,SAM101A2.fastq.gz,,hg19
Sample3,SAM101A3.fastq.gz,/path/to/local/genome.fa,
Sample4,SAM101A4.fastq.gz,,
Example command
nextflow run nf-core/nanoseq \
    --input samplesheet.csv \
    --protocol cDNA \
    --skip_basecalling \
    --skip_demultiplexing \
    -profile <docker/singularity/institute>
Without basecalling, demultiplexing, and alignment
Example samplesheet.csv
sample,sample_path,barcode,genome,transcriptome
Sample1,SAM101A1.bam,,hg19
Sample2,SAM101A2.bam,,hg19
Sample3,SAM101A3.bam,/path/to/local/genome.fa,/path/to/local/genes.gtf
Example command
nextflow run nf-core/nanoseq \
    --input samplesheet.csv \
    --protocol cDNA \
    --skip_basecalling \
    --skip_demultiplexing \
    --skip_qc \
    --skip_alignment \
    -profile <docker/singularity/institute>

--protocol

Specifies the type of data that was sequenced i.e. "DNA", "cDNA" or "directRNA".

Basecalling and demultiplexing

--input_path

Path to Nanopore run directory (e.g. fastq_pass/) or a basecalled fastq file that requires demultiplexing. The latter can only be provided in conjunction with the --skip_basecalling parameter.

--flowcell

Flowcell used to perform the sequencing e.g. "FLO-MIN106". Not required if --guppy_config is specified.

--kit

Kit used to perform the sequencing e.g. "SQK-LSK109". Not required if --guppy_config is specified.

--barcode_kit

Barcode kit used to perform the sequencing e.g. "SQK-PBK004".

If you would like to skip the basecalling (--skip_basecalling) but still perform the demultiplexing please specify a barcode kit that can be recognised by qcat:

qcat barcode kit specifications description
Auto Auto detect barcoding kit
RBK001 Rapid barcoding kit
RBK004 Rapid barcoding kit v4
NBD103/NBD104 Native barcoding kit with barcodes 1-12
NBD114 Native barcoding kit with barcodes 13-24
NBD104/NBD114 Native barcoding kit with barcodes 1-24
PBC001 PCR barcoding kits with 12 barcodes
PBC096 PCR barcoding kits with 96 barcodes
RPB004/RLB001 Rapid PCR Barcoding Kit (SQK-RPB004) and Rapid Low Input by PCR Barcoding Kit
RPB004/LWB001 Low Input by PCR Barcoding Kit
RAB204 16S Rapid Amplicon Barcoding Kit with 12 Barcodes
VMK001 Voltrax Barcoding Kit with 4 barcodes

--guppy_config

Config file used for basecalling that will be passed to Guppy via the "--config" parameter. Cannot be used in conjunction with --flowcell and --kit. This can be a local file (i.e. /your/dir/guppy_conf.cfg) or a string specifying a configuration stored in the /opt/ont/guppy/data/ directory of Guppy.

--guppy_model

Custom basecalling model file in json format that will be passed to Guppy via the "--model" parameter. Custom basecalling models can be trained with software such as Taiyaki. This can also be a string specifying a model stored in the /opt/ont/guppy/data directory of Guppy.

--guppy_gpu

Whether to demultiplex with Guppy in GPU mode (default: false).

--guppy_gpu_runners

Number of "--gpu_runners_per_device" used for Guppy when using --guppy_gpu (default: 6).

--guppy_cpu_threads

Number of "--cpu_threads_per_caller" used for Guppy when using --guppy_gpu (default: 1).

--gpu_device

Basecalling device specified to Guppy in GPU mode using "--device" (default: 'auto').

--gpu_cluster_options

Cluster options required to use GPU resources (e.g. '--part=gpu --gres=gpu:1').

--qcat_min_score

Specify the minimum quality score for qcat in the range 0-100 (default: 60).

--qcat_detect_middle

Search for adapters in the whole read by applying the '--detect-middle' parameter in qcat (default: false).

--skip_basecalling

Skip basecalling with Guppy.

--skip_demultiplexing

Skip demultiplexing with Guppy/qcat.

Alignments

--stranded

Specifies if the data is strand-specific. Automatically activated when using --protocol directRNA (default: false).

When using --protocol/--stranded the following command-line arguments will be set for minimap2 and graphmap2:

nanoseq input minimap2 presets graphmap2 presets
--protocol DNA -ax map-ont no presets
--protocol cDNA -ax splice -x rnaseq
--protocol directRNA -ax splice -uf -k14 -x rnaseq
--protocol cDNA --stranded -ax splice -uf -x rnaseq

--aligner

Specifies the aligner to use i.e. graphmap2 or minimap2.

--save_align_intermeds

Save the .sam files from the alignment step - not done by default.

--skip_alignment

Skip alignment and downstream processes.

Transcript Quantification

--transcriptquant

Specifies the transcript quantification method to use i.e. bambu or stringtie2.

--skip_transcriptquant

Skip transcript quantification processes.

Coverage tracks

Step Description
--skip_bigwig Skip BigWig file generation
--skip_bigbed Skip BigBed file generation

Skipping QC steps

The pipeline contains a number of quality control steps. Sometimes, it may not be desirable to run all of them if time and compute resources are limited. The following options make this easy:

Step Description
--skip_qc Skip all QC steps apart from MultiQC
--skip_pycoqc Skip pycoQC
--skip_nanoplot Skip NanoPlot
--skip_fastqc Skip FastQC
--skip_multiqc Skip MultiQC

Job resources

Automatic resubmission

Each step in the pipeline has a default set of requirements for number of CPUs, memory and time. For most of the steps in the pipeline, if the job exits with an error code of 143 (exceeded requested resources) it will automatically resubmit with higher requests (2 x original, then 3 x original). If it still fails after three times then the pipeline is stopped.

Custom resource requests

Wherever process-specific requirements are set in the pipeline, the default value can be changed by creating a custom config file. See the files hosted at nf-core/configs for examples.

If you are likely to be running nf-core pipelines regularly it may be a good idea to request that your custom config file is uploaded to the nf-core/configs git repository. Before you do this please can you test that the config file works with your pipeline of choice using the -c parameter (see definition below). You can then create a pull request to the nf-core/configs repository with the addition of your config file, associated documentation file (see examples in nf-core/configs/docs), and amending nfcore_custom.config to include your custom profile.

If you have any questions or issues please send us a message on Slack.

AWS Batch specific parameters

Running the pipeline on AWS Batch requires a couple of specific parameters to be set according to your AWS Batch configuration. Please use -profile awsbatch and then specify all of the following parameters.

--awsqueue

The JobQueue that you intend to use on AWS Batch.

--awsregion

The AWS region in which to run your job. Default is set to eu-west-1 but can be adjusted to your needs.

--awscli

The AWS CLI path in your custom AMI. Default: /home/ec2-user/miniconda/bin/aws.

Please make sure to also set the -w/--work-dir and --outdir parameters to a S3 storage bucket of your choice - you'll get an error message notifying you if you didn't.

Other command line parameters

--outdir

The output directory where the results will be saved.

--email

Set this parameter to your e-mail address to get a summary e-mail with details of the run sent to you when the workflow exits. If set in your user config file (~/.nextflow/config) then you don't need to specify this on the command line for every run.

--email_on_fail

This works exactly as with --email, except emails are only sent if the workflow is not successful.

--max_multiqc_email_size

Threshold size for MultiQC report to be attached in notification email. If file generated by pipeline exceeds the threshold, it will not be attached (Default: 25MB).

-name

Name for the pipeline run. If not specified, Nextflow will automatically generate a random mnemonic.

This is used in the MultiQC report (if not default) and in the summary HTML / e-mail (always).

NB: Single hyphen (core Nextflow option)

-resume

Specify this when restarting a pipeline. Nextflow will used cached results from any pipeline steps where the inputs are the same, continuing from where it got to previously.

You can also supply a run name to resume a specific run: -resume [run-name]. Use the nextflow log command to show previous run names.

NB: Single hyphen (core Nextflow option)

-c

Specify the path to a specific config file (this is a core NextFlow command).

NB: Single hyphen (core Nextflow option)

Note - you can use this to override pipeline defaults.

--custom_config_version

Provide git commit id for custom Institutional configs hosted at nf-core/configs. This was implemented for reproducibility purposes. Default: master.

## Download and use config file with following git commid id
--custom_config_version d52db660777c4bf36546ddb188ec530c3ada1b96

--custom_config_base

If you're running offline, nextflow will not be able to fetch the institutional config files from the internet. If you don't need them, then this is not a problem. If you do need them, you should download the files from the repo and tell nextflow where to find them with the custom_config_base option. For example:

## Download and unzip the config files
cd /path/to/my/configs
wget https://github.com/nf-core/configs/archive/master.zip
unzip master.zip

## Run the pipeline
cd /path/to/my/data
nextflow run /path/to/pipeline/ --custom_config_base /path/to/my/configs/configs-master/

Note that the nf-core/tools helper package has a download command to download all required pipeline files + singularity containers + institutional configs in one go for you, to make this process easier.

--max_memory

Use to set a top-limit for the default memory requirement for each process. Should be a string in the format integer-unit. eg. --max_memory '8.GB'

--max_time

Use to set a top-limit for the default time requirement for each process. Should be a string in the format integer-unit. eg. --max_time '2.h'

--max_cpus

Use to set a top-limit for the default CPU requirement for each process. Should be a string in the format integer-unit. eg. --max_cpus 1

--plaintext_email

Set to receive plain-text e-mails instead of HTML formatted.

--monochrome_logs

Set to disable colourful command line output and live life in monochrome.

--multiqc_config

Specify a path to a custom MultiQC configuration file.