@article{fdi:010097131,
  title = {{S}train{M}ake : reproducible hybrid metagenomics with {MAG} recovery and strain-level resolution},
  author = {{H}ennecart, {B}aptiste and {B}elda, {E}. and de {L}ahond{\`e}s, {R}. and {Z}ucker, {J}ean-{D}aniel and {P}rifti, {E}di},
  editor = {},
  language = {{ENG}},
  abstract = {{M}etagenomic workflows involve complex multi-step analyses, from quality control and assembly to binning, annotation, and strain-level profiling. {F}ew existing metagenomic pipelines achieve the combination of flexibility, reproducibility, and hybrid assembly support within a unified workflow. {W}e present {S}train{M}ake, a {S}nakemake-based workflow for de novo metagenomic analysis from short, long, or hybrid sequencing data. {S}train{M}ake integrates widely used tools across all major steps-quality control, assembly, binning, dereplication, taxonomic and functional annotation-while also providing non-redundant gene catalogues, community-scale metabolic models, and strain-level microdiversity metrics. {T}he modular design enables the use of alternative tools, scalable execution on {HPC} systems, and full reproducibility through {S}nakemake and {C}onda.{R}esults {A}pplied to the {CAMI} {II} strain-madness dataset, {S}train{M}ake produced high-quality assemblies and metagenome-assembled genomes ({MAG}s), while enabling strain-resolved comparisons across samples. {H}ybrid assemblies improved contiguity, whereas short-read assemblies offered faster runtimes, illustrating the workflow's benchmarking capacity. {A}vailability and implementation {S}train{M}ake is open source and available at https://github.com/{UMMISCO}/strainmake, together with comprehensive documentation. {G}enerated data are deposited in {Z}enodo (doi: 10.5281/zenodo.16950162).},
  keywords = {},
  booktitle = {},
  journal = {{B}ioinformatics},
  volume = {42},
  numero = {5},
  pages = {btag212 [4 p.]},
  ISSN = {1367-4803},
  year = {2026},
  DOI = {10.1093/bioinformatics/btag212},
  URL = {https://www.documentation.ird.fr/hor/fdi:010097131},
}