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Citation and references

Citing MetaFlux

Publications that use MetaFlux should cite this repository. A formal release with a Zenodo DOI is forthcoming:

Antonielli, L. (2026). MetaFlux: a unified short-read multi-marker amplicon and shotgun taxonomic profiling workflow. Zenodo. DOI: pending release.

MetaFlux is a wrapper around published tools and reference databases, and those do the actual work. Cite them too — the list below covers the tools and databases the workflow can invoke, general-purpose helpers such as pigz and seqtk aside, so pick the entries matching the mode, marker and databases that were actually used. Two of them cannot be pinned in advance: the Kraken2/Bracken index (ref. 23) and any host genome supplied for decontamination (ref. 25) are chosen at runtime, so cite the exact dated build used.

Acknowledgements

Developed at the AIT Austrian Institute of Technology. MetaFlux consolidates and modernises methods refined across many amplicon and metagenomics collaborations, and is part of the BioFlux family of workflows.

Portions of this codebase were developed with the assistance of Claude Code.

References

  1. Köster, J. & Rahmann, S. (2012). Snakemake — a scalable bioinformatics workflow engine. Bioinformatics.
  2. Callahan, B. J., et al. (2016). DADA2: High-resolution sample inference from Illumina amplicon data. Nature Methods.
  3. Martin, M. (2011). Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal.
  4. Langmead, B. & Salzberg, S. L. (2012). Fast gapped-read alignment with Bowtie 2. Nature Methods.
  5. de Sena Brandine, G. & Smith, A. D. (2019). Falco: high-speed FastQC emulation for fastq files. F1000Research.
  6. Bengtsson-Palme, J., et al. (2015). Metaxa2: improved identification and taxonomic classification of small and large subunit rRNA in metagenomic data. Molecular Ecology Resources.
  7. Bengtsson-Palme, J., et al. (2013). ITSx: improved software detection and extraction of ITS1 and ITS2. Methods in Ecology and Evolution.
  8. Rognes, T., et al. (2016). VSEARCH: a versatile open source tool for metagenomics. PeerJ.
  9. Quast, C., et al. (2013). The SILVA ribosomal RNA gene database project. Nucleic Acids Research.
  10. Abarenkov, K., et al. (2024). UNITE general FASTA release for eukaryotes. UNITE Community.
  11. Chen, S., et al. (2018). fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics.
  12. Bushnell, B. BBTools (BBDuk, BBMap). DOE Joint Genome Institute.
  13. Wood, D. E., Lu, J. & Langmead, B. (2019). Improved metagenomic analysis with Kraken 2. Genome Biology.
  14. Lu, J., et al. (2017). Bracken: estimating species abundance in metagenomics data. PeerJ Computer Science.
  15. Lu, J., et al. (2022). Metagenome analysis using the Kraken software suite (KrakenTools). Nature Protocols.
  16. Ewels, P., et al. (2016). MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics.
  17. Wang, Q., et al. (2007). Naïve Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Applied and Environmental Microbiology.
  18. Edgar, R. C. (2016). SINTAX: a simple non-Bayesian taxonomy classifier for 16S and ITS sequences. bioRxiv. https://doi.org/10.1101/074161
  19. Guillou, L., et al. (2013). The Protist Ribosomal Reference database (PR2): a catalog of unicellular eukaryote small sub-unit rRNA sequences with curated taxonomy. Nucleic Acids Research. (database v5.1.1 — https://github.com/pr2database/pr2database)
  20. Briand, M., Rué, O. & Barret, M. (2025). gyrB database for taxonomic assignment formatted for DADA2 (train_set_gyrB_v6). Recherche Data Gouv (INRAE Dataverse). https://doi.org/10.57745/DD7RZ8
  21. FROGS rpoB reference databank (2024). Bacterial rpoB genes from complete and chromosome NCBI RefSeq genomes, build 20240707. INRAE Toulouse. https://web-genobioinfo.toulouse.inrae.fr/frogs_databanks/assignation/rpoB/
  22. Parks, D. H., et al. (2022). GTDB: an ongoing census of bacterial and archaeal diversity through a phylogenetically consistent, rank normalized and complete genome-based taxonomy. Nucleic Acids Research. (Genome Taxonomy Database, release r226)
  23. Kraken 2 / Bracken pre-built index collection. Langmead Lab, via the AWS Open Data Sponsorship Program. https://benlangmead.github.io/aws-indexes/k2 (user-supplied at runtime — cite the exact dated build used, e.g. PlusPF)
  24. Sanger, F., et al. (1977). Nucleotide sequence of bacteriophage φX174 DNA. Nature. (spike-in removal reference: NCBI RefSeq GCF_000819615.1 / NC_001422.1)
  25. Handley, S. A. (2020). Virus+ Sequence Masked Human Reference Genome (hg19). Zenodo. https://doi.org/10.5281/zenodo.4116107 (default host reference; cite the actual assembly if a different host genome is configured)
  26. Stoeck, T., et al. (2010). Multiple marker parallel tag environmental DNA sequencing reveals a highly complex eukaryotic community in marine anoxic water. Molecular Ecology. (18S V4 primers TAReuk454FWD1 / TAReukREV3)
  27. Parada, A. E., Needham, D. M. & Fuhrman, J. A. (2016). Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environmental Microbiology. (18S V4 primers 515Y / 926R — V4–V5 in 16S nomenclature)
  28. Parfrey, L. W., et al. (2014). Communities of microbial eukaryotes in the mammalian gut within the context of environmental eukaryotic diversity. Frontiers in Microbiology. (18S V4 primers 515F / 1119r)
  29. Hadziavdic, K., et al. (2014). Characterization of the 18S rRNA gene for designing universal eukaryote specific primers. PLoS ONE. (18S V4 primers 566F / 1200R)
  30. Amaral-Zettler, L. A., et al. (2009). A method for studying protistan diversity using massively parallel sequencing of V9 hypervariable regions of small-subunit ribosomal RNA genes. PLoS ONE. (18S V9 primers Euk1391F / EukBr)
  31. Barret, M., et al. (2015). Emergence shapes the structure of the seed microbiota. Applied and Environmental Microbiology. (gyrB primers F64 / R353; also used to build the DD7RZ8 reference amplicons)
  32. Ogier, J.-C., et al. (2019). rpoB, a promising marker for analyzing the diversity of bacterial communities by amplicon sequencing. BMC Microbiology. (rpoB primers Univ_rpoB_deg)
  33. Dabdoub, S. M. (2016). kraken-biom: enabling interoperative format conversion for Kraken results. GitHub. https://github.com/smdabdoub/kraken-biom
  34. Leinonen, R., Sugawara, H. & Shumway, M. (2011). The Sequence Read Archive. Nucleic Acids Research. (SRA Toolkit — prefetch, fasterq-dump — and NCBI E-utilities; https://github.com/ncbi/sra-tools)
  35. Wright, R. J., Comeau, A. M. & Langille, M. G. I. (2023). From defaults to databases: parameter and database choice dramatically impact the performance of metagenomic taxonomic classification tools. Microbial Genomics 9(3). https://doi.org/10.1099/mgen.0.000949 (source of the Kraken2 confidence: 0.15 default)
  36. Nyström-Persson, J., Bapatdhar, N. & Ghosh, S. (2025). Precise and scalable metagenomic profiling with sample-tailored minimizer libraries. NAR Genomics and Bioinformatics 7(2), lqaf076. https://doi.org/10.1093/nargab/lqaf076 (CAMI2 benchmarking at confidence: 0.15)
  37. Breitwieser, F. P. & Salzberg, S. L. (2018). KrakenUniq: confident and fast metagenomics classification using unique k-mer counts. Genome Biology 19:198. https://doi.org/10.1186/s13059-018-1568-0 (origin of the unique-k-mer evidence idea behind shotgun.kmer_evidence)
  38. Pochon, Z., et al. (2023). aMeta: an accurate and memory-efficient ancient metagenomic profiling workflow. Genome Biology 24:242. https://doi.org/10.1186/s13059-023-03083-9 (source of the paired 1,000 unique k-mers + 200 reads convention)
  39. Oskolkov, N. (2026). Refining filtering criteria of Kraken family of tools for accurate taxonomic profiling of ancient metagenomic data. Frontiers in Microbiology 17:1603339. https://doi.org/10.3389/fmicb.2026.1603339 (source of min_distinct_minimizers: 333 after unit conversion, and of min_reads: 0)
  40. Ye, S. H., et al. (2019). Benchmarking Metagenomics Tools for Taxonomic Classification. Cell 178(4):779–794. https://doi.org/10.1016/j.cell.2019.07.010 (principle that a read-count threshold should scale with sequencing depth — the basis of bracken.threshold: auto)
  41. Meyer, F., et al. (2022). Critical Assessment of Metagenome Interpretation: the second round of challenges. Nature Methods 19:429–440. https://doi.org/10.1038/s41592-022-01431-4 (CAMI II marine, plant-associated and strain-madness datasets used to fit threshold_alpha)
  42. Meyer, F., et al. (2019). Assessing taxonomic metagenome profilers with OPAL. Genome Biology 20:51. https://doi.org/10.1186/s13059-019-1646-y (independent scorer used to cross-check the benchmark)
  43. Zymo Research. ZymoBIOMICS Gut Microbiome Standard (D6331), Instruction Manual v1.2.0. https://files.zymoresearch.com/protocols/_d6331_zymobiomics_gut_microbiome_standard.pdf (mock community with known composition used for validation)

License

MetaFlux is released under the MIT License. Third-party tools invoked by the workflow are distributed under their own licenses (a mix of MIT, BSD, and GPL/LGPL); see each tool's repository.