nf-core/plasmodiumdrugres

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Introduction

nf-core/plasmodiumdrugres is a bioinformatics pipeline for analyzing drug resistance markers from microhaplotype data. It translates variants into amino acid changes at drug resistance loci and estimates allele frequencies and prevalences at both single-locus and multi-locus levels. Microhaplotype data can be supplied in the form of an allele table or a PMO file.

If you clone this repository for local development (instead of nextflow run nf-core/plasmodiumdrugres), initialize Git submodules so the bundled PGEcore scripts are available:

git clone --recurse-submodules https://github.com/nf-core/plasmodiumdrugres.git
# or, if already cloned:
git submodule update --init --recursive

Entry points

There are two supported entry points into the pipeline:

1. PMO input

   • Required: --pmo, --loci_of_interest_bed
   • Optional:
     • --loci_groups to enable multi-locus allele frequency estimation
     • --pmo_population_fields (+ optional --pmo_population_separator) to derive population assignment from PMO specimen metadata
     • --population_assignment to supply a population assignment table manually (use this instead of --pmo_population_fields if you prefer to define populations yourself)
     • --population_label for single-population runs (default: pop1)
     • --genome_reference or --targeted_reference if PMO does not include usable reference sequence information

2. Allele table input

   • Required: --allele_table, --panel_info_bed, --loci_of_interest_bed
   • Optional:
     • --loci_groups to enable multi-locus allele frequency estimation
     • --population_assignment for multi-population analysis
     • --population_label for single-population runs (default: pop1)

The pipeline enforces that exactly one of --pmo or --allele_table is provided.

Terminology note: “population”

In this pipeline, population means “a group of samples you want to estimate prevalence and frequency for”. See Population grouping (optional) for how to set populations via --population_assignment, --pmo_population_fields, or --population_label. A population can represent any grouping level you care about, e.g. country, health_facility, year, or any combination. If you don’t set any population parameters, the pipeline treats all samples as a single group (labelled by --population_label, default pop1).

Loci of Interest Input

You will need to create a bed file including the locations of the loci that you are interested in before running the pipeline. It has to be a tab-separated file with 9 columns, and a header row as shown in the examples below.

--loci_of_interest_bed '[path to loci of interest file]'

Full loci of interest bed file

This file will be used to call amino acids from your data and calculate frequencies and prevalences for the single loci. You can include as many single loci as you like. Loci that are not covered by any panel target are skipped during amino acid calling and reported as uncovered in the translation coverage output.

A final loci of interest bed file may look something like the one below.

#chrom  start end name  length  strand  gene  aa_position gene_id
Pf3D7_04_v3 748237  748240  PF3D7_0417200.1-AA51  3 + dhfr-ts 51  PF3D7_0417200.1
Pf3D7_04_v3 748261  748264  PF3D7_0417200.1-AA59  3 + dhfr-ts 59  PF3D7_0417200.1
Pf3D7_04_v3 748408  748411  PF3D7_0417200.1-AA108 3 + dhfr-ts 108 PF3D7_0417200.1
Pf3D7_04_v3 748576  748579  PF3D7_0417200.1-AA164 3 + dhfr-ts 164 PF3D7_0417200.1
Pf3D7_05_v3 958144  958147  PF3D7_0523000.1-AA86  3 + mdr1  86  PF3D7_0523000.1
Pf3D7_05_v3 958438  958441  PF3D7_0523000.1-AA184 3 + mdr1  184 PF3D7_0523000.1
Pf3D7_05_v3 961624  961627  PF3D7_0523000.1-AA1246  3 + mdr1  1246  PF3D7_0523000.1
Pf3D7_07_v3 403623  403626  PF3D7_0709000.1-AA76  3 + crt 76  PF3D7_0709000.1
Pf3D7_07_v3 403686  403689  PF3D7_0709000.1-AA97  3 + crt 97  PF3D7_0709000.1
Pf3D7_08_v3 549680  549683  PF3D7_0810800.1-AA436 3 + dhps  436 PF3D7_0810800.1
Pf3D7_08_v3 549683  549686  PF3D7_0810800.1-AA437 3 + dhps  437 PF3D7_0810800.1
Pf3D7_08_v3 549992  549995  PF3D7_0810800.1-AA540 3 + dhps  540 PF3D7_0810800.1
Pf3D7_08_v3 550115  550118  PF3D7_0810800.1-AA581 3 + dhps  581 PF3D7_0810800.1
Pf3D7_08_v3 550211  550214  PF3D7_0810800.1-AA613 3 + dhps  613 PF3D7_0810800.1

An example loci of interest bed file has been provided with the pipeline. It provides an extensive set of loci for Plasmodium falciparum, so you can simply filter for the loci relevant to your work rather than starting from scratch. If you identify a locus that should be added, please let us know.

Loci groups

--loci_groups is optional. When provided, the pipeline runs multi-locus allele frequency (MLAF) estimation and writes ml_summary.tsv plus raw_summaries/raw_sl_from_ml_summary.tsv. When omitted, those ML steps are skipped; ml_summary.tsv and the corresponding raw SL-from-ML table are still written as header-only stubs so outputs stay consistent.

Before running multi-locus estimates, create a tab-separated file that defines the groups of loci. It must have 3 columns and a header row as shown below.

--loci_groups '[path to loci groups file]'

Full loci groups file

This file specifies which loci from the loci of interest file should be grouped together for generating multi-locus estimates. You can include as many groups as you like, however some tools are limited in how many loci they can handle per group. Any gene_id / aa_position combination listed in this file should also be defined in the loci of interest file.

A final loci groups file may look something like the one below. In this example, three groups are defined: crt, mdr1, and pfdhfr_pfdhps, containing 2, 3, and 9 loci, respectively.

group_id  gene_id aa_position
crt PF3D7_0709000.1 76
crt PF3D7_0709000.1 97
mdr1  PF3D7_0523000.1 86
mdr1  PF3D7_0523000.1 184
mdr1  PF3D7_0523000.1 1246
pfdhfr_pfdhps PF3D7_0417200.1 51
pfdhfr_pfdhps PF3D7_0417200.1 59
pfdhfr_pfdhps PF3D7_0417200.1 108
pfdhfr_pfdhps PF3D7_0417200.1 164
pfdhfr_pfdhps PF3D7_0810800.1 436
pfdhfr_pfdhps PF3D7_0810800.1 437
pfdhfr_pfdhps PF3D7_0810800.1 540
pfdhfr_pfdhps PF3D7_0810800.1 581
pfdhfr_pfdhps PF3D7_0810800.1 613

Input file

Decide if you will be running the pipeline from a PMO file or an allele table as other required inputs will depend on this. The most simple way to run this pipeline is by using a Portable Microhaplotype Object (PMO) file. To maximize flexibility, the pipeline also allows users to provide a PMO with reference sequences separately, or to supply an allele table with panel information in a separate file.

Shared optional inputs such as population grouping and --loci_groups apply to both entry points.

PMO Inputs

Generate a PMO file using this documentation. If you include reference sequences in your PMO then this is all you need. If you don’t then you should provide a reference with either --genome_reference or --targeted_reference. --genome_reference can be a fasta file including a full genome. --targeted_reference is a fasta file where sequence names match up with target_names.

For population grouping with PMO runs (manual assignment table, derive labels from specimen metadata, or a single --population_label), see Population grouping (optional).

Allele Table Inputs

When running with an allele table you should create the following inputs:

• allele table
• panel info bed file
• population grouping (optional)

Allele Table

You will need to create an allele table file that includes your genomic data. It has to be a tab-separated file with 3 columns, and a header row as shown in the examples below. You can optionally include a 4th column.

--allele_table '[path to allele table file]'

Full allele table

A final allele table may look something like the one below.

specimen_name  target_name  seq reads
specimen_1  target1 TTATTTTTTTTGTCAATAGATAAATGATCAATATTTTCTATATTTAATCTATCAAGTATTTTTATATATCTATTATTTCTTTCTTCGATGGAT 93
specimen_1  target1 AATAAAGAAGAAGATAAATATGGAAAAAATGAAAAAAACGAAAAATATGACAAATATGACAAATATGAAAAATATGATAAATACAAAAAAGAT 708
specimen_1  target2 TCATTCTTTTTTTAACTAAAACTATTCATCTCAAAAATATAAGATATTTTATATGACGAATGCCATTGTATTTTTTGTTACGTAAAAC  236
specimen_2  target1 AATAAAGAAGAAGATAAATATGGAAAAAATGAAAAAAACGAAAAATATGACAAATATGACAAATATGAAAAATATGATAAATACAAAAAAGAT 733
specimen_3  target1 AATAAAGAAGAAGATAAATATGGAAAAAATGAAAAAAACGAAAAATATGACAAATATGACAAATATGAAAAATATGATAAATACAAAAAAGAT 650

Panel Info

Next, prepare a panel info bed file. This will define the locations of the targets in the target_name column in the allele table. It has to be a tab-separated file with 7 columns.

--panel_info_bed '[path to panel info file]'

Full panel info bed file

A final panel info bed file may look something like the one below.

#chrom  start   end     target_name       length  strand  ref_seq
Pf3D7_01_v3     145421  145629  target1    208     +       GATATGTTTAAATATATGATTCTCGAAAAAACTTTTTTTATTTTTTTTGTCAATAGATAAATGATCAATATTTTCTATATTTAATCTATCAAGTATTTTTATATATCTATTATTTCTTTCTTCGATGGATAAATTATAAGAATCAATATCCTTTCTTTCATCAACAAACTTTTTTATTGTTAACTCCATTTTTTTATTTAAGATACCA
Pf3D7_01_v3     162889  163091  target2   202     +       ATATACCAATAATACTTTTTTTTTTAAATAATGTAAAAAATGATTTATATAATTGTTATAAACAAATGATCACATATCATAATAATAATATCCTAAATCATAACTCTAATATTTTATCAAAAGAAAATGAAAAAAAACAACCTTTTTCAACATATAATATATCAAATCTTTGTTCTCCTGACCAAATGGTGATAAATAAAAA

Population grouping (optional)

These options are shared by PMO and allele-table runs. Choose one multi-population approach, or omit them to treat all samples as a single group.

Single population (--population_label)

If you do not set --population_assignment or --pmo_population_fields, all samples are analysed as one group labelled by --population_label (default: pop1).

--population_label '[label for this run]'

Population assignment table (--population_assignment)

To estimate prevalences and frequencies for several populations, provide a population assignment file. The file contains two columns: specimen_name (matching specimen names in your allele table or PMO-derived table) and population (the population label used in outputs).

--population_assignment '[path to population assignment file]'

Full population assignment file

A final population assignment file may look like this:

specimen_name population
specimen_1  pop1
specimen_2  pop2
specimen_3  pop2

Derive populations from PMO metadata (--pmo_population_fields)

When running with --pmo, you can build population labels from specimen metadata instead of supplying --population_assignment. Provide a comma-separated list of field names; values are joined with --pmo_population_separator (default: _).

--pmo_population_fields "collection_country,collection_date"
--pmo_population_separator "_"

Do not set both --population_assignment and --pmo_population_fields; use one or the other.

Other params

• --translate_loci_extra_args - Extra arguments when translating loci of interest. See documentation here.
• --slaf_method - chosen method to estimate single locus allele frequencies (Default: naive; Options: ["IDM","naive","mhaps_freq"])
• --mlaf_method - chosen method to estimate multi-locus allele frequencies (Default: naive; Options: ["MLBM","FEM","naive"])
• --naive_slaf_method - Chosen naive method when running --slaf_method naive. (Default: read_count_prop; Options: ["read_count_prop", "presence_absence"])

Running the pipeline

Run from PMO

Minimal PMO run:

nextflow run nf-core/plasmodiumdrugres --pmo input_file.pmo --loci_of_interest_bed loci_of_interest.bed --loci_groups loci_groups.tsv --outdir ./results -profile docker

If your PMO does not provide the reference context needed for loci translation, add --genome_reference:

nextflow run nf-core/plasmodiumdrugres --pmo input_file.pmo --loci_of_interest_bed loci_of_interest.bed --loci_groups loci_groups.tsv --genome_reference genome_reference.fasta --outdir ./results -profile docker

If you have a targeted reference FASTA instead, use --targeted_reference:

nextflow run nf-core/plasmodiumdrugres --pmo input_file.pmo --loci_of_interest_bed loci_of_interest.bed --loci_groups loci_groups.tsv  --targeted_reference genome_reference.fasta --outdir ./results -profile docker

To derive populations from PMO metadata fields, provide a comma-separated list:

nextflow run nf-core/plasmodiumdrugres \
  --pmo input_file.pmo \
  --pmo_population_fields "collection_country,collection_date" \
  --loci_of_interest_bed loci_of_interest.bed \
  --loci_groups loci_groups.tsv \
  --outdir ./results \
  -profile docker

Run from allele table

Minimal allele-table run:

nextflow run nf-core/plasmodiumdrugres --allele_table allele_table.tsv --panel_info_bed panel_info.bed --loci_of_interest_bed loci_of_interest.bed --loci_groups loci_groups.tsv --outdir ./results -profile docker

If you have a population assignment file, include --population_assignment:

nextflow run nf-core/plasmodiumdrugres --allele_table allele_table.tsv --panel_info_bed panel_info.bed --loci_of_interest_bed loci_of_interest.bed --loci_groups loci_groups.tsv --population_assignment population_assignment.tsv --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/plasmodiumdrugres -profile docker -params-file params.yaml

with:

pmo: './input_file.pmo'
loci_of_interest_bed: './loci_of_interest.bed'
loci_groups: './loci_groups.tsv'
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/plasmodiumdrugres

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/plasmodiumdrugres 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, see pipeline_info/nf_core_plasmodiumdrugres_software_versions.yml.

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 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).
• emulate_amd64
  • Run Docker containers as linux/amd64 (useful on Apple Silicon when using default amd64 images)
• arm64
  • Prefer arm64 container variants where available; typically used with wave
• 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'