nf-core/multiplesequencealign

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Overview

nf-core/multiplesequencealign is a pipeline to deploy and systematically evaluate Multiple Sequence Alignment (MSA) methods.

The main steps of the pipeline are:

1. Input files summary: (Optional) computation of summary statistics on the input fasta file, such as the average sequence similarity across the input sequences, their length, etc. Skipped by the --skip_stats parameter.
2. Guide Tree: (Optional) Renders a guide tree. Only run if provided in the toolsheet input.
3. Align: aligns the sequences.
4. Evaluate: (Optional) The obtained alignments are evaluated with different metrics: Sum Of Pairs (SoP), Total Column score (TC), iRMSD, Total Consistency Score (TCS), etc. Skipped by passing --skip_eval as a parameter.
5. Report: Reports about the collected information of the runs are reported in a Shiny app and a summary table in MultiQC. These processes can be skipped by passing --skip_shiny and --skip_multiqc, respectively.

1. Input files summary statistics

This step generates the summary information about the input files and can be skipped using the --skip_stats parameter. The optional computed metrics are:

1. Sequence similarity: This step calculates pairwise and average sequence similarity using TCOFFEE. Activate with --calc_sim (default: false).
2. General summary: Calculates the number and the average length of sequences. Activate with --calc_seq_stats (default: true).
3. Extract plddt: If the structures were generated using AF2, plddt is extracted and reported. Activate with --extract_plddt (default: false).

2. Guide trees

Guide trees define the order in which sequences and profiles are aligned and play a crucial role in determining the final MSA accuracy. Tree rendering techniques most commonly rely on pairwise distances between sequences.

Note None of the aligners listed below need an explicit definition of a guide tree: if they require one, they compute their own default guide tree. However, an explicit definition of a guide tree is available in case you want to test non-default combination of guide trees and aligner methods.

Currently available GUIDE TREE methods are: (Optional):

• CLUSTALO
• FAMSA

3. Align

The available assembly methods are listed below (those that accept guide trees indicate it in parentheses):

sequence-based (only require a fasta file as input):

• CLUSTALO (accepts guide tree)
• FAMSA (accepts guide tree)
• KALIGN
• LEARNMSA
• MAFFT
• MAGUS (accepts guide tree)
• MUSCLE5
• TCOFFEE (accepts guide tree)
• REGRESSIVE (accepts guide tree)
• UPP (accepts guide tree)

sequence- and structure-based (require both fasta and structures as input):

• 3DCOFFEE (accepts guide tree)

structure-based (only require stuctures as input):

• MTMALIGN
• FOLDMASON

Optionally, M-COFFEE will combine the output of all alignments into a consensus MSA (--build_consensus).

4. Evaluate

Optionally, the produced MSAs will be evaluated. This step can be skipped using the --skip_eval parameter. The evaluations implemented are listed below.

sequence-based (no extra input required):

1. Number of gaps. Calculates the number of gaps and its average across sequences. Activate using --calc_gaps (default: true).

reference-based:

The reference MSAs (see samplesheet) are used to evaluate the quality of the produced MSA.

2. Sum Of Pairs (SP). Calculates the SP score using the TCOFFEE implementation. Activated using --calc_sp (default: true).
3. Total column (TC). Calculates the TC score TCOFFEE. Activate using --calc_tc (default: true).

structure-based:

The provided structures (see samplesheet) are used to evaluate the quality of the alignment.

4. iRMSD: Calculates the iRMSD using the TCOFFEE implementation. Activate using --calc_irmsd (default: false).

5. Report

Finally, a summary table with all the computed statistics and evaluations is reported in MultiQC (skip by using --skip_multiqc). Moreover, a Shiny app is generated with interactive summary plots (skip with --skip_shiny).

Samplesheet input

The sample sheet defines the input data that the pipeline will process. It should look like this:

id,fasta,reference,dependencies,template
seatoxin,seatoxin.fa,seatoxin-ref.fa,seatoxin_structures,seatoxin_template.txt
toxin,toxin.fa,toxin-ref.fa,toxin_structures,toxin_template.txt

Each row represents a set of sequences (in this case the seatoxin and toxin protein families) to be processed.

Toolsheet input

We provide a toolsheet as input to facilitate testing multiple arguments per tool when using the pipeline as a benchmarking framework. This, enables having multiple entries in the toolsheet, each corresponding to different arguments to be tested for the same tool.

Each line of the toolsheet defines a combination of guide tree and multiple sequence aligner to run with the respective arguments to be used.

A typical toolsheet should look at follows:

tree,args_tree,aligner,args_aligner,
FAMSA, -gt upgma -medoidtree, FAMSA,
, ,TCOFFEE,
FAMSA,,REGRESSIVE,

tree is the tool used to build the tree (optional).

Arguments to the tree tool can be provided using args_tree. Please refer to each tool’s documentation (optional).

The aligner column contains the tool to run the alignment (optional).

Finally, the arguments to the aligner tool can be set by using the args_aligner column (optional).

Running the pipeline

The typical command for running the pipeline is as follows:

nextflow run nf-core/multiplesequencealign --input ./samplesheet.csv --tools ./toolsheet.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/multiplesequencealign -profile docker -params-file params.yaml

with params.yaml containing:

input: './samplesheet.csv'
tools: "./toolsheet.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/multiplesequencealign

Reproducibility

It is 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/multiplesequencealign 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. For example, at the bottom of the MultiQC reports.

To further assist in reproducbility, you can use share and re-use 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.

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, since it can lead to different results on different machines dependent on the computer enviroment.

• 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 steps in the pipeline, if the job exits with any of the error codes specified here it will automatically be resubmitted with higher requests (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 which container or conda environment a step of the pipeline uses 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.

Azure Resource Requests

To be used with the azurebatch profile by specifying the -profile azurebatch. We recommend providing a compute params.vm_type of Standard_D16_v3 VMs by default but these options can be changed if required.

Note that the choice of VM size depends on your quota and the overall workload during the analysis. For a thorough list, please refer the Azure Sizes for virtual machines in Azure.

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'