nf-core/deepmutscan

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Introduction

nf-core/deepmutscan is a workflow designed for the analysis of deep mutational scanning (DMS) data. DMS enables researchers to experimentally measure the fitness effects of thousands of genes or gene variants simultaneously, helping to classify disease causing mutants in human and animal populations, to learn the fundamental rules of virus evolution, protein architecture, splicing, small-molecule interactions and many other phenotypes.

This page provides in-depth descriptions of the data processing modules implemented in nf-core/deepmutscan. It is similarly intended for new deep mutational scanning aficionados, for advanced users and developers who want to understand the rationale behind certain design choices, to explore implementation details and consider potential future extensions.

Running the pipeline

The typical command for running the pipeline (on an example protein-coding gene with 100 amino acids) is as follows:

nextflow run nf-core/deepmutscan \
   -profile <docker/singularity/.../institute> \
   --input ./samplesheet.csv \
   --fasta ./ref.fa \
   --reading_frame 1-300 \
   --outdir ./results

The -profile <PROFILE> specification is mandatory and should reflect either your own institutional profile or any pipeline profile specified in the profile section.

This will launch the pipeline by performing sequencing read alignments, various raw data QC analyses, optional mutant count error corrections, fitness and fitness error estimations.

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

work               # Directory containing the nextflow working files
results            # Finished results in specified location (defined with --outdir)
.nextflow_log      # Log file from Nextflow

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/deepmutscan -profile docker -params-file params.yaml

with:

input: './samplesheet.csv'
outdir: './results/'
gene reference: 'ref.fa'
<...>

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

Inputs

Users need to first prepare a samplesheet with your input/output data in which each row represents a pair of matched fastq files (paired end). This should look as follows:

sample,type,replicate,file1,file2
ORF1,input,1,/reads/forward1.fastq.gz,/reads/reverse1.fastq.gz
ORF1,input,2,/reads/forward2.fastq.gz,/reads/reverse2.fastq.gz
ORF1,output,1,/reads/forward3.fastq.gz,/reads/reverse3.fastq.gz
ORF1,output,2,/reads/forward4.fastq.gz,/reads/reverse4.fastq.gz

Secondly, users need to specify the gene or gene region of interest using a reference FASTA file via --fasta. Provide the exact codon coordinates using --reading_frame.

Optional parameters

Several optional parameters are available for nf-core/deepmutscan, some of which are currently (in development).

Pipeline output

After execution, the pipeline creates the following directory structure:

results/
├── fastqc/              # Individual HTML reports for specified fastq files, raw sequencing QC
├── fitness/             # Merged variant count tables, fitness and error estimates, replicate correlations and heatmaps
├── intermediate_files/  # Raw alignments, raw and pre-filtered variant count tables, QC reports
├── library_QC/          # Sample-specific PDF visualizations: position-wise sequencing coverage, count heatmaps, etc.
├── multiqc/             # Shared HTML reports for all fastq files, raw sequencing QC
├── pipelineinfo/        # Nextflow helper files for timeline and summary report generation
├── timeline.html        # Nextflow timeline for all tasks
└── report.html          # Nextflow summary report incl. detailed CPU and memory usage per for all tasks

Detailed steps

1. Alignment

All paired-end raw reads are first aligned to the provided reference ORF using bwa-mem. This is a highly efficient mapping algorithm for reads ≥100 bp, with its multi-threading support automatically handled by nf-core.

In future versions of nf-core/deepmutscan, we consider the use of bwa-mem2, which provides similar alignment quality at moderate speed increase (Vasimuddin et al., IPDPS 2019). With the increasing diversity of sequencing platforms for DMS new read length, throughput and error profiles may require further alignment options to be implemented.

2. Filtering

For long ORF site-saturation mutagenesis libraries, most aligned shotgun sequencing reads contain exact matches against the reference. It is not possible to infer which of these stem from actual wildtype vs (upstream or downstream) mutant DNA molecules prior to fragmentation, hence they are filtered out. Currently, reads with likely artefactual indel-containing alignments are also removed.

To this end, we use samtools view.

3. Read Merging

Even the highest-quality next-generation sequencing platforms do not feature perfect base accuracy. To minimise the effect of base errors (which would otherwise be counted as “false mutations”), nf-core/deepmutscan uses the overlap of each aligned read pair. With base errors on the forward and reverse read being independent, the pipeline applies the vsearch fastq_mergepairs function to convert each read pair into a single consensus molecule with adjusted base error scores.

Future versions may offer additional options depending on sequencing type and error profiles.

4. Variant Counting

Aligned, non-wildtype consensus reads are screened for exact, base-level mismatches. nf-core/deepmutscan currently uses the popular GATK AnalyzeSaturationMutagenesis function to count occurrences of all single, double, triple, and higher-order nucleotide changes between each read and the reference ORF.

We are currently working on the implementation of an alternative, lightweight Python implementation for mutation counting. Users will thereby also be allowed to specify a minimum base quality cutoff for mutations to be included in the final count table – an option which is unfortunately not available in GATK.

5. DMS Library Quality Control

By integrating the reference ORF coordinates and the chosen DMS library type (default: NNK degenerate codons), nf-core/deepmutscan calculates a number of mutation count summary statistics.

Custom visualisations allow for inspection of (1) mutation efficiency along the ORF, (2) position-specific recovery of mutant amino acid diversity, and (3) overall sequencing coverage evenness and library saturation.

6. Data Summarisation for Fitness Estimation

Steps 1-5 are run in parallel across all individual samples defined in the .csv spreadsheet. Once read alignment, filtering, merging, variant counting, and DMS library QC have been completed for the full list of samples – if input/output sample pairs are available – users can opt to proceed towards fitness estimation. To this end, the pipeline generates all the necessary preparatory files by generating a merged mutation count table across samples.

7. Single Nucleotide Variant Error Correction (in development)

This module will feature strategies to distinguish true single nucleotide variants from sequencing artefacts. There are two options to perform this:

• Empirical error rate modelling based on additional wildtype sequencing
• Empirical error rate modelling based on false double mutants in the programmed single mutant library

8. Fitness Estimation

The final step of the pipeline will perform fitness estimation based on mutation counts. By default, we calculate fitness scores as the logarithm of variants’ output to input ratio, normalised to that of the provided wildtype nucleotide sequence.

Future expansions may include:

• Integration of other popular fitness inference tools, including DiMSum, Enrich2, rosace and mutscan
• Standardised output formats for downstream analyses and comparison

Notes for Developers

• Custom R scripts used in filtering and QC visualisation are available in the modules/local/dmsanalysis/bin/ directory of the repository.
• Modules are implemented in Nextflow DSL2 and follow the nf-core community guidelines.
• Contributions, optimisations, and additional analysis modules are welcome: please open a Github issue or pull request to discuss or suggest ideas.

This document is meant as a living reference. As the pipeline evolves, the descriptions of steps 7 and 8 will be further expanded with concrete implementation details.

Updating the pipeline

When you run the original command above, 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 this cached version if available - even if the pipeline has been updated since. To make sure that you are running the latest version of the pipeline, make sure that you regularly update the cached version:

nextflow pull nf-core/deepmutscan

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/deepmutscan releases page and find the latest pipeline version - numeric only (eg. 1.0.0). Then specify this when running the pipeline with -r (one hyphen) - eg. -r 1.0.0.

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 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.

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 may lead to varying or even irreproducible results across users’ different computer environments.

• 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 within the /work directory) 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'