Workflow management systems and reproducible pipelines for Plasmodium falciparum genomic surveillance: tools, design principles and infrastructure challenges
Genomic data are increasingly leveraged to enable malaria genomic surveillance, with many teams now generating Plasmodium falciparum sequence data for routine analysis. However, downstream analysis remains dependent on ad-hoc scripts and local infrastructure. This reliance limits reproducibility, and scalability, while obstructing the deployment of standardized pipelines across diverse high-performance computing (HPC) and cloud environments. This review describes the main workflow management systems and bioinformatics tools that are currently used for malaria genomic surveillance, with potential applicability to P. falciparum . We focus on the four most prominent workflow management systems (Nextflow, Snakemake, WDL/Cromwell, and Galaxy) and the core components of established pipelines in the field. Therefore, we summarize which tools are most widely used and how they are assembled into end-to-end workflows for surveillance. We organize these tools and pipelines around eight standardized PlasmoGenEPi use cases and propose a four-layer framework for pipeline design (data, pipeline structure, software environment, execution platform). Building on this, we introduce a pragmatic decision matrix that links sample volumes, infrastructure profiles and surveillance objectives to concrete choices of sequencing strategy and workflow system. We then discuss practical challenges that directly affect implementation in endemic settings, including heterogeneous computing infrastructure, long-term pipelines maintenance, staff turnover, and the systemic vulnerability of shared infrastructure, notably exposed by the recent VEuPathDB funding crisis. Finally, we highlight emerging directions, including community efforts such as PlasmoGenEPi, the increasing use of targeted Oxford Nanopore amplicon sequencing, and the move towards more standardized, portable and well-documented workflows that can be adapted to other pathogen surveillance systems.
Authors
- Abhinav Sharma (ORCID: https://orcid.org/0000-0002-6402-6993)
- Léon Mutesa (ORCID: https://orcid.org/0000-0002-5308-3706)
- Clement Igiraneza
- Jules Ndoli (ORCID: https://orcid.org/0000-0001-6649-3547)
- Didier Ménard (ORCID: https://orcid.org/0000-0003-1357-4495)
- Danilo de Castro Silva
- Firas Zemzem
- Marcel Da Câmara Ribeiro-Dantas
- Geraldine Van der Auwera
- Hastings Twalie Musopole
- Eduan Wilkinson
Institutions
- Inserm (FR)
- Institut Pasteur (FR)
- University of Monastir (TN)
- Institut Universitaire de France (FR)
- Université Paris Cité (FR)
- Stellenbosch University (ZA)
- Tygerberg Hospital (ZA)
- University of Kigali (RW)
- Hôpital Farhat Hached (TN)
- Universidade Potiguar (BR)
- Agbar (Spain) (ES)
- Rwanda Biomedical Center (RW)
- University Teaching Hospital of Butare (RW)
- Hôpital Civil, Strasbourg (FR)
- Université de Strasbourg (FR)
- University of KwaZulu-Natal (ZA)
Publication Details
- Journal
- Malaria Journal
- Published
- 2026-09-05
- DOI
- https://doi.org/10.1186/s12936-026-06123-4
- Primary Topic
- Scientific Computing and Data Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Institut Universitaire de France