nf-core pipeline · drug-resistance, frequency-estimation, genomics
nf-core/plasmodiumdrugres
Analyses drug resistance markers from microhaplotype data to estimate allele frequencies and prevalences.
What nf-core/plasmodiumdrugres is
nf-core/plasmodiumdrugres is a bioinformatics pipeline designed to analyse drug resistance markers from microhaplotype data. Originally developed by PlasmoGenEpi, it addresses the challenge of identifying and monitoring resistance variants in pathogens where infections can be polyclonal and phenotypes of interest are determined by combinations of mutations across multiple loci.
The workflow translates nucleotide variants into amino acid substitutions at designated drug resistance loci and estimates allele prevalence and frequency at both single-locus and multi-locus resolutions across populations. Although developed for Plasmodium falciparum and provided with an example set of P. falciparum resistance loci, the pipeline can be applied to any organism with available microhaplotypes, an appropriate reference, and a BED file detailing the loci of interest.
What the pipeline does
The workflow accepts microhaplotype data either as a Portable Microhaplotype Object (PMO) file or as an allele table alongside a panel BED file, accompanied by a BED file defining the loci of interest. PGEcore is used to translate the target loci into amino acid changes, and data can then be partitioned by population assignments. Allele prevalence is subsequently calculated using PGEcore.
Allele frequencies are calculated using user-selected models. Multilocus frequencies can be determined using MultiLociBiallelicModel, FreqEstimationModel, or a naive PGEcore method. Single-locus frequencies are calculated using the Incomplete Data Model (IDM), the naive PGEcore method, or the mhaps_freq approach via DCIFER. Prevalence and frequency outputs are merged and concatenated into standardised summary tables (sl_summary.tsv and ml_summary.tsv), while raw tool-specific columns are preserved in a dedicated summaries directory.
Key facts
- Primary target organism
- Plasmodium falciparum (adaptable to other organisms with microhaplotypes)
- Input formats
- PMO (.pmo) or allele table (.tsv) with panel BED (.bed), plus loci of interest BED (.bed)
- Optional inputs
- Loci groups table (.tsv), population assignment data
- Output files
- sl_summary.tsv, ml_summary.tsv, and tool-specific tables in raw_summaries/
- Single-locus frequency methods
- Incomplete Data Model (IDM), Naive PGEcore, mhaps_freq (DCIFER)
- Multilocus frequency methods
- MultiLociBiallelicModel, FreqEstimationModel, Naive PGEcore
What you can use it for
- Plasmodium falciparum Resistance Surveillance
- Translate microhaplotype variants into amino acid changes and determine the prevalence and frequency of known drug resistance markers in malaria parasite cohorts.
- Multilocus Haplotype Frequency Estimation
- Estimate frequencies of complex multilocus genotypes across polyclonal pathogen infections using statistical models such as MultiLociBiallelicModel or FreqEstimationModel.
- Population-level Resistance Comparisons
- Partition sample cohorts by population metadata to evaluate and contrast resistance allele frequencies and prevalences across multiple groups.
- Microhaplotype Analysis in Other Pathogens
- Apply the translation, prevalence, and frequency estimation steps to other organisms by supplying custom reference and target loci BED files.
Run it here
The research agent runs nf-core/plasmodiumdrugres with Nextflow on its own machine — up to 32 cores, 256 GiB of memory and 24 hours per run, which covers the test profile and real single-sample runs. It builds the samplesheet, checks it against the pipeline's schema and reads the results back. When a run is too big for one machine, it gives you the exact command for your own cluster instead.
Strengths and limits
Good at
- Translates microhaplotype variants into amino acid changes directly at user-specified loci.
- Provides specialised statistical algorithms to account for polyclonal infections during frequency estimation.
- Supports multiple input data types, including Portable Microhaplotype Objects (PMO) and tabular allele tables.
- Produces standardised summary tables across single-locus and multilocus analyses while keeping raw tool metrics.
Watch out for
- Requires a predefined BED file detailing the specific loci of interest.
- Multilocus frequency estimation requires a separate loci groups definition file.
- Non-PMO input requires both an allele table and a corresponding panel BED file.
Questions
Can I run nf-core/plasmodiumdrugres without my own cluster?
The research agent runs nf-core/plasmodiumdrugres with Nextflow on its own machine — up to 32 cores, 256 GiB of memory and 24 hours per run, which covers the test profile and real single-sample runs. It builds the samplesheet, checks it against the pipeline's schema and reads the results back. When a run is too big for one machine, it gives you the exact command for your own cluster instead.
What licence is nf-core/plasmodiumdrugres under?
MIT, like every nf-core pipeline.
What input formats can be supplied to the pipeline?
The pipeline accepts input either as a Portable Microhaplotype Object (PMO) or as an allele table paired with a panel BED file. In both cases, a loci of interest BED file is mandatory.
Can this workflow be used for species other than Plasmodium falciparum?
Yes. Although developed for P. falciparum and distributed with example loci for it, it can analyse any organism with microhaplotypes when supplied with a matching reference and loci of interest BED file.
Which models are available for single-locus frequency estimation?
Single-locus allele frequencies can be estimated using the Incomplete Data Model (IDM), a naive PGEcore calculation, or the mhaps_freq method using DCIFER.
Which algorithms are supported for multilocus frequency estimation?
The pipeline provides three options for multilocus allele frequencies: MultiLociBiallelicModel, FreqEstimationModel, and a naive PGEcore method.
What output files does the pipeline produce?
The primary outputs are standardized tables named sl_summary.tsv for single-locus results and ml_summary.tsv for multilocus results, with comprehensive tool-specific metrics stored under raw_summaries/.
How to cite it
Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen. The nf-core framework for community-curated bioinformatics pipelines. Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.
Written from nf-core/plasmodiumdrugres's own README. Statements citing a figure that does not appear there are removed before publication. Licence and running details come from our registry.