Introduction

This pipeline is designed to assemble haploid (or diploid inbred) genomes from long-reads. nf-core/genomeassembler can take ONT and HiFi reads, and supports different assembly strategies. The pipeline can also integrate information on a reference genome (e.g. closely related individual) and short-reads for quality control. This pipeline can perform assembly, polishing, scaffolding using long-reads, HiC data, or a reference, and annotation lift-over from a reference genome.

Since it is often difficult to know which tool, or assembly strategy will perform best on a dataset, nf-core/genomeassembler can also be used to compare outcomes of different approaches in one run. To compare different samples, a column named group is required, which should contain the same value for all samples that should be compared to each other.

Parameterization

Parameters for this pipeline can either be supplied globally, e.g:

• via --paramname value,
• or in a config with params { paramname = value },
• or a yaml with:

---
params:
  - paramname: "value"

or as sample parameters, by adding a correctly named column to the samplesheet. In the above example this would be a column named paramname.

Sample parameters take priority over global parameters, if both are provided the sample-specific parameter will be used for that sample.

The list of all parameters that can be provided globally is available here, parameters that can be set per sample are provided at the end of this page.

Samples and grouping

This pipeline is intended to support two main use-cases:

• either a larger set of samples is assembled using a shared set of parameters and settings set mostly via params,
• or a single, or few samples, are assembled using different strategies, typically with the goal of comparing strategies to identify the best approach for a given dataset.

In the second case, it is likely that several samples will use the same inputs (i.e. reads). Such samples can be put into one group, by assigning them the same value in the group column of the samplesheet, and for these samples pre-processing of reads will only be done once per group, instead of once per sample. This can be used to avoid unneccessary redundant work on the same set of inputs and will only affect preprocessing and reporting, where these samples will be displayed together.

Choice of assembly-strategy and assembler

Assembly strategy is controlled via strategy (either pipeline parameter or sample-setting), and assembler(s) used are chosen via assembler (either pipeline parameter or sample-setting) nf-core/genomeassembler currently supports the following assembly strategies:

• single (default): Use a single assembler for a single type of read. The assembler should be provided via assembler and can be hifiasm (default) or flye.
• hybrid: Use a single assembler for a combined assembly of ONT and HiFi reads. The assembler should be provided via assembler. Currently, only hifiasm supports hybrid assembly.
• scaffold: Assemble ONT reads and HiFi indepently and scaffold one assembly onto the other. assembler has to be provided as "ontAssembler_hifiAssembler" and could for example be: “flye_hifiasm" to assemble ont reads with flye and HiFi reads with hifiasm or “hifiasm_hifiasm” to assemble both ont and hifi reads indepently with hifiasm. When running in “scaffold” mode, assembly_scaffolding_order can be used to control which assembly gets scaffolded onto which, the default being “ont_on_hifi” where ONT assembly is scaffolded onto HifI assembly.

Assembler specific arguments can be provided for the assembler via hifiasm_args or flye_args, or with more fine-grained control via assembler_ont_args and assembler_hifi_args for scaffolding. assembler_ont_args controls the parameters for the assembler used with ONT-reads in single and hybrid strategies, or for the assembler used for ONT reads when using scaffold. assembler_hifi_args can be used to pass arguments to the assembler used for HiFi reads in single, or scaffold mode.

Samplesheet input

You will need to create a samplesheet with information about the samples you would like to analyse before running the pipeline. Use the input parameter to specify its location:

--input samplesheet.csv

Samplesheet layout

The samplesheet must contain a column name sample [string]. The most barebone samplesheet format is:

sample
Sample1
Sample2

Further commonly used columns can be:

• group [string] to group different samples in the report to facilitate comparisons.
• ontreads [path] for long reads produced with oxford nanopore sequencers
• hifireads [path] for long reads produced with pacbio sequencers in “HiFi” mode
• Reference information:
  • ref_fasta [path] fasta file of a reference genome
  • ref_gff [path] annotations of the reference genome in gff format
• Short reads
  • shortread_F : shortread forward file
  • shortread_R: shortread reverse file (paired end)
  • paired: [true/false] true if the reads are paired end, false if they are single-end. The shortreads_R column should exist if paired is false but can be empty.

But samplesheets can grow more complex if a range of strategies should be compared in a single pipeline run. A list of all possible columns can be found at the end of this page

[!INFO] It is strongly recommended to provide all paths as absolute paths

Running the pipeline

The typical command for running the pipeline is as follows:

nextflow run nf-core/genomeassembler --input ./samplesheet.csv --outdir ./results -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
<OUTDIR>            # Finished results in specified location (defined with --outdir)
.nextflow_log       # Log file from Nextflow
# Other nextflow hidden files, eg. history of pipeline runs and old logs.

If you wish to repeatedly use the same parameters for multiple runs, rather than specifying each flag in the command, you can specify these in a params file.

Pipeline settings can be provided in a yaml or json file via -params-file <file>.

The above pipeline run specified with a params file in yaml format:

nextflow run nf-core/genomeassembler -profile docker -params-file params.yaml

with:

input: './samplesheet.csv'
outdir: './results/'
<...>

You can also generate such YAML/JSON files via nf-core/launch.

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/genomeassembler

Reproducibility

It is a good idea to specify the 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/genomeassembler releases page and find the latest pipeline version - numeric only (eg. 1.3.1). Then specify this when running the pipeline with -r (one hyphen) - eg. -r 1.3.1. Of course, you can switch to another version by changing the number after the -r flag.

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.

To further assist in reproducibility, you can use share and reuse parameter files to repeat pipeline runs with the same settings without having to write out a command with every single parameter.

Core Nextflow 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, Podman, Shifter, Charliecloud, Apptainer, Conda) - see below.

[!INFO] 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 check if your system is supported, 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, since it can lead to different results on different machines dependent on the computer environment.

• test
  • A profile with a complete configuration for automated testing
  • Includes links to test data so needs no other parameters
• docker
  • A generic configuration profile to be used with Docker
• singularity
  • A generic configuration profile to be used with Singularity
• podman
  • A generic configuration profile to be used with Podman
• shifter
  • A generic configuration profile to be used with Shifter
• charliecloud
  • A generic configuration profile to be used with Charliecloud
• apptainer
  • A generic configuration profile to be used with Apptainer
• wave
  • A generic configuration profile to enable Wave containers. Use together with one of the above (requires Nextflow 24.03.0-edge or later).
• conda
  • A generic configuration profile to be used with Conda. Please only use Conda as a last resort i.e. when it’s not possible to run the pipeline with Docker, Singularity, Podman, Shifter, Charliecloud, or Apptainer.

-resume

Specify this when restarting a pipeline. Nextflow will use cached results from any pipeline steps where the inputs are the same, continuing from where it got to previously. For input to be considered the same, not only the names must be identical but the files’ contents as well. For more info about this parameter, see this blog post.

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.

-c

Specify the path to a specific config file (this is a core Nextflow command). See the nf-core website documentation for more information.

Custom configuration

Resource requests

Whilst the default requirements set within the pipeline will hopefully work for most people and with most input data, you may find that you want to customise the compute resources that the pipeline requests. Each step in the pipeline has a default set of requirements for number of CPUs, memory and time. For most of the pipeline steps, if the job exits with any of the error codes specified here it will automatically be resubmitted with higher resources request (2 x original, then 3 x original). If it still fails after the third attempt then the pipeline execution is stopped.

To change the resource requests, please see the max resources and customise process resources section of the nf-core website.

Custom Containers

In some cases, you may wish to change the container or conda environment used by a pipeline steps for a particular tool. By default, nf-core pipelines use containers and software from the biocontainers or bioconda projects. However, in some cases the pipeline specified version maybe out of date.

To use a different container from the default container or conda environment specified in a pipeline, please see the updating tool versions section of the nf-core website.

Custom Tool Arguments

A pipeline might not always support every possible argument or option of a particular tool used in pipeline. Fortunately, nf-core pipelines provide some freedom to users to insert additional parameters that the pipeline does not include by default.

To learn how to provide additional arguments to a particular tool of the pipeline, please see the customising tool arguments section of the nf-core website.

nf-core/configs

In most cases, you will only need to create a custom config as a one-off but if you and others within your organisation are likely to be running nf-core pipelines regularly and need to use the same settings 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. 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.

See the main Nextflow documentation for more information about creating your own configuration files.

If you have any questions or issues please send us a message on Slack on the #configs channel.

Running in the background

Nextflow handles job submissions and supervises the running jobs. The Nextflow process must run until the pipeline is finished.

The Nextflow -bg flag launches Nextflow in the background, detached from your terminal so that the workflow does not stop if you log out of your session. The logs are saved to a file.

Alternatively, you can use screen / tmux or similar tool to create a detached session which you can log back into at a later time. Some HPC setups also allow you to run nextflow within a cluster job submitted your job scheduler (from where it submits more jobs).

Nextflow memory requirements

In some cases, the Nextflow Java virtual machines can start to request a large amount of memory. We recommend adding the following line to your environment to limit this (typically in ~/.bashrc or ~./bash_profile):

NXF_OPTS='-Xms1g -Xmx4g'

Sample Parameters

This section lists all possible parameters that can be set per sample. If parameters are not provided, they are inherited from the pipeline parameters; see params for default settings.

Sample information

Reference Parameters

Options controlling pipeline behavior

Assembly options

Options controlling assembly.

The difference between {hifiasm,flye}_args and assembler_{ont,hifi}_args is subtle: the former will be applied for all cases where this particular assembler is used, whereas the latter will apply the args to the assembler used for assembling a specific type of data. {hifiasm,flye}_args are generally expected to be used for e.g. system specific configuration via params, although they can also be set per-sample, whereas assembler_{ont,hifi}_args provide a bit more of an abstract interface, possibly more appropriate to adjust certain parameters per-sample.

Long-read preprocessing

All long-reads will be passed to fastplong for trimming and quality control. If reads should not be modified by fastplong, adaptor trimming can be disabled using -A, quality filtering can be disabled with -Q. These arguments can be passed via <hifi/ont>_fastplot_args for the different read types.

Short read options

Options for short reads.

Polishing options

Polishing options. When using polish with either dorado+pilon or medaka+pilon, the assembly will be polished using ONT reads first, and then the ONT-polished assembly will be polished with short reads using pilon. dorado and medaka are mutually exclusive. dorado is not available via conda. dorado is an experimental feature which may not work for all inputs.

Scaffolding options

Options _longstitch, _links and _hic are mutually exclusive. RagTag scaffolding can be used to either scaffold the (polished) assembly, or can be combined with longstitch, links or hic, to scaffold the scaffolding results onto a reference. RagTag will always scaffold the most “advanced” stage of assembly; meaning that if the assembly was scaffolded, the scaffolded assembly will be used, if the assembly was polished, the polished assembly will be used, if the pipeline only carried out assembly for that sample, the assembly will be scaffolded.

HiC

HiC scaffolding specific parameters. Supplying HiC reads activates HiC scaffolding, unless explicitly deactivated using scaffold_hic. bwa-mem2 generally is more suitable for HiC alignments than minimap2, and is the recommended option. However, bwamem2 requires substantial memory for large genomes, which may prohibit use of bwamem2 in some cases.

QC options

Options for QC tools

Annotations options

Options controlling annotation liftover

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/genomeassembler

Reproducibility

It is a good idea to specify the 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/genomeassembler releases page and find the latest pipeline version - numeric only (eg. 1.3.1). Then specify this when running the pipeline with -r (one hyphen) - eg. -r 1.3.1. Of course, you can switch to another version by changing the number after the -r flag.

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.

To further assist in reproducibility, you can use share and reuse parameter files to repeat pipeline runs with the same settings without having to write out a command with every single parameter.

Core Nextflow 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, Podman, Shifter, Charliecloud, Apptainer, Conda) - see below.

[!INFO] 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 check if your system is supported, 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, since it can lead to different results on different machines dependent on the computer environment.

• test
  • A profile with a complete configuration for automated testing
  • Includes links to test data so needs no other parameters
• docker
  • A generic configuration profile to be used with Docker
• singularity
  • A generic configuration profile to be used with Singularity
• podman
  • A generic configuration profile to be used with Podman
• shifter
  • A generic configuration profile to be used with Shifter
• charliecloud
  • A generic configuration profile to be used with Charliecloud
• apptainer
  • A generic configuration profile to be used with Apptainer
• wave
  • A generic configuration profile to enable Wave containers. Use together with one of the above (requires Nextflow 24.03.0-edge or later).
• conda
  • A generic configuration profile to be used with Conda. Please only use Conda as a last resort i.e. when it’s not possible to run the pipeline with Docker, Singularity, Podman, Shifter, Charliecloud, or Apptainer.

-resume

Specify this when restarting a pipeline. Nextflow will use cached results from any pipeline steps where the inputs are the same, continuing from where it got to previously. For input to be considered the same, not only the names must be identical but the files’ contents as well. For more info about this parameter, see this blog post.

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.

-c

Specify the path to a specific config file (this is a core Nextflow command). See the nf-core website documentation for more information.

Custom configuration

Resource requests

Whilst the default requirements set within the pipeline will hopefully work for most people and with most input data, you may find that you want to customise the compute resources that the pipeline requests. Each step in the pipeline has a default set of requirements for number of CPUs, memory and time. For most of the pipeline steps, if the job exits with any of the error codes specified here it will automatically be resubmitted with higher resources request (2 x original, then 3 x original). If it still fails after the third attempt then the pipeline execution is stopped.

To change the resource requests, please see the max resources and tuning workflow resources section of the nf-core website.

Custom Containers

In some cases, you may wish to change the container or conda environment used by a pipeline steps for a particular tool. By default, nf-core pipelines use containers and software from the biocontainers or bioconda projects. However, in some cases the pipeline specified version maybe out of date.

To use a different container from the default container or conda environment specified in a pipeline, please see the updating tool versions section of the nf-core website.

Custom Tool Arguments

A pipeline might not always support every possible argument or option of a particular tool used in pipeline. Fortunately, nf-core pipelines provide some freedom to users to insert additional parameters that the pipeline does not include by default.

To learn how to provide additional arguments to a particular tool of the pipeline, please see the customising tool arguments section of the nf-core website.

nf-core/configs

In most cases, you will only need to create a custom config as a one-off but if you and others within your organisation are likely to be running nf-core pipelines regularly and need to use the same settings 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. 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.

See the main Nextflow documentation for more information about creating your own configuration files.

If you have any questions or issues please send us a message on Slack on the #configs channel.

Running in the background

Nextflow handles job submissions and supervises the running jobs. The Nextflow process must run until the pipeline is finished.

The Nextflow -bg flag launches Nextflow in the background, detached from your terminal so that the workflow does not stop if you log out of your session. The logs are saved to a file.

Alternatively, you can use screen / tmux or similar tool to create a detached session which you can log back into at a later time. Some HPC setups also allow you to run nextflow within a cluster job submitted your job scheduler (from where it submits more jobs).

Nextflow memory requirements

In some cases, the Nextflow Java virtual machines can start to request a large amount of memory. We recommend adding the following line to your environment to limit this (typically in ~/.bashrc or ~./bash_profile):

NXF_OPTS='-Xms1g -Xmx4g'