Introduction Usage Output

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/denovohybrid -input 'read_locations.tsv' -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/denovohybrid

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/denovohybrid 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. Note that multiple profiles can be loaded, for example: -profile docker - the order of arguments is important!

If -profile is not specified at all the pipeline will be run locally and expects all software to be installed and available on the PATH.

  • awsbatch
    • A generic configuration profile to be used with AWS Batch.
  • conda
    • A generic configuration profile to be used with conda
    • Pulls most software from Bioconda
  • docker
  • singularity
  • test
    • A profile with a complete configuration for automated testing
    • Includes links to test data so needs no other parameters

--input

This pipeline allows the assembly of multiple samples in a single run. Since each sample usually has multiple input files (2x paired end short reads and 1x long reads), a tab separated design file is used to supply the input files.

The design file should be a plain, tab-separated textfile with the following format: |sample1 |readfolder/reads_s1_nanopore.fastq.gz|readfolder/reads_s1_illumina_R1.fastq.gz|readfolder/reads_s1_illumina_R2.fastq.gz| |sample2 |readfolder/reads_s2_nanopore.fastq.gz|readfolder/reads_s2_illumina_R1.fastq.gz|readfolder/reads_s2_illumina_R2.fastq.gz|

Content of the columns: 1) Sample_Id Unique identifier to this sample, will be to label output files. 2) Short_Reads_1 Path to .fastq.gz file with part 1 of Illumina paired end reads. 3) Short_Reads_2 Path to .fastq.gz file with part 2 of Illumina paired end reads. 4) Long_Reads Path to .fastq.gz file containing long reads

File paths should be absolute or relative to the current workdirectory.

When short read files are missing in the design file, the pipeline automatically creates a long-read only assembly with the specified assembly algorithm.

--input 'path/to/design_file.tsv'

Please note the following requirements:

--mode

The --mode options allows to chose between different assemblers. The quality control and read preprocessing steps are identical. Currently, four different modes are available

unicycler

Creates an assembly with the bacterial genome assembler Unicycler. This assembly methods is a whole pipeline in itself and in the first step creates a short read assembly using SPAdes. The long reads are then mapped to the inital assembly graph in order to solve loops in the graph by creating bridges betwenn the contigs. Additional polishing steps increase the per base accuracy using accurate short reads. Unicycler is currently the state of the art hybrid assembler for bacteria and is in many cases able to resolve complete closed genomes. Unicycler is executed with default settings. Unicycler is the default mode when not specified different.

--mode unicycler

miniasm

miniasm is a simple and fast long read assembler. It is also suitable for large eukaryotic genomes. miniasm is followed by a consensus step with racon and short read polishing using Pilon.

--mode miniasm

wtdbg2

Wtdbg2 is a very recent, and extremely fast long read assembler. It can bei also used to assemble large genomes. A human genome takes about 30 CPU hours and 220Gb of memory. Similar to miniasm, wtdbg2 is also followed by a racon consensus step and a short read polishing using Pilon if available.

--mode miniasm

all

Runs all available assembly methods on the same sample. This options helps to compare the results and performance of the different assemblers. Results are stored in separate subfolders (see output documentation)

--mode all

minContigLength

Default: 1000

In the final filtering step, contigs with a size lower than this threshhold are removed from the FASTA file.

--minContigLength 1000

genomeSize

Default: 5300000

The genomeSize parameter is defined by the approximate size of the expected assembly result. Integer parameter with the number of base pairs. This value is used to calculate the amount of bases needed to reach the target coverage and is an required input to some of the assemblers.

--genomeSize 4900000

targetShortReadCov / targetLongReadCov

Default: 100

Very high coverage read files are subsampled to the specified target long and short coverages. This helps to speed up the assembly process.

--targetShortReadCov 60
--targetLongReadCov 60

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 the -awsbatch profile and then specify all of the following parameters.

--awsqueue

The JobQueue that you intend to use on AWS Batch.

--awsregion

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

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.

-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 is set to 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.
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