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Citation and references

Citing MetaFlux

Publications that use MetaFlux should cite the Zenodo record:

Antonielli, L. (2026). MetaFlux: a unified short-read multi-marker amplicon and shotgun taxonomic profiling workflow. Zenodo. https://doi.org/10.5281/zenodo.22876451

That DOI always resolves to the newest release. To cite the exact version you ran, use its own DOI instead — v2.6.1 is 10.5281/zenodo.23898251 — and give the version number in the reference, because results can depend on it.

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. Three of them cannot be pinned in advance: the Kraken2/Bracken index (ref. 23), the sylph databases (ref. 88) 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.

MetaFlux was developed in the context of MICROBE — MICRObiome Biobanking (RI) Enabler, a Horizon Europe research-infrastructure project coordinated by AIT (2023–2027) that develops the methods, and the routes of access, for preserving microbiomes with their composition and function intact. This project has received funding from the European Union's Horizon Europe research and innovation programme under grant agreement No. 101094353.

A large part of MetaFlux — merging the amplicon and shotgun workflows into one, the marker packs, two full code audits, this documentation, and the phylogeny module with its validation — was built in working sessions with Claude Code. Anthropic accepted MetaFlux into their Open Source Program and provided Claude Max for it, generously and with no strings attached; that support is what made the pace and the scope of this work possible, and it is acknowledged here with real gratitude. The deal on this side has been that every line is still read, understood and tested by a human before it goes in — which is why the code and these pages read the way they do.

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. Chuvochina, M., Gerken, J., Frentrup, M., Sandikci, Y., Goldmann, R., Freese, H. M., Göker, M., Sikorski, J., Yarza, P., Quast, C., Peplies, J., Glöckner, F. O. & Reimer, L. C. (2026). SILVA in 2026: a global core biodata resource for rRNA within the DSMZ digital diversity. Nucleic Acids Research 54(D1):D334–D341. https://doi.org/10.1093/nar/gkaf1247 (SILVA 144, the 16S default) · SILVA 144 DADA2 files: toGenus training set https://doi.org/10.82364/144/SSU/Ref-NR99/DADA2/genus and assignSpecies file https://doi.org/10.82364/144/SSU/Ref/DADA2/species · Quast, C., et al. (2013). The SILVA ribosomal RNA gene database project. Nucleic Acids Research 41:D590–D596. https://doi.org/10.1093/nar/gks1219 (SILVA 138.2, and SILVA-Euk for 18S) · Callahan, B. J. (2024). Silva taxonomic training data formatted for DADA2 (Silva version 138.2). Zenodo. https://doi.org/10.5281/zenodo.14169026
  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. McDonald, D., et al. (2012). The Biological Observation Matrix (BIOM) format or: how I learned to stop worrying and love the ome-ome. GigaScience 1:7. https://doi.org/10.1186/2047-217X-1-7 (the BIOM format of otu_table.biom and sylph_table.biom, written with the biom-format package)
  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., Baker, D. N. & 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 the min_distinct_minimizers default: the paper's optimum of about 1,000 unique k-mers converts to about 333 distinct minimizers, rounded down to 300; 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 (filtering classifier output on relative abundance; bracken.threshold: auto applies such a cut-off, as a fixed share of each sample's classified read pairs)
  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 (rhizosphere) and strain-madness short-read datasets, samples 0–9, 0–20 and 0–9, used to fit threshold_alpha; see Benchmark data)
  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 (used to check the benchmark scorer's species-level counts)
  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: SRA run SRR29240428, BioProject PRJNA1118346, from Martí et al. (2025), mSystems 10(4):e01239-24, https://doi.org/10.1128/msystems.01239-24; Zymo's manual lists its Fusobacterium as F. nucleatum 2/1/50A, which is scored as F. animalis, see Benchmark data)

Phylogeny module (optional, 16S only)

Cite the aligner and whichever tree backend you actually ran — the backend is recorded in 07.phylogeny/phylogeny.params.json.

  1. Katoh, K. & Standley, D. M. (2013). MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Molecular Biology and Evolution 30(4):772–780. https://doi.org/10.1093/molbev/mst010 (the aligner, always used when the module runs)
  2. Wong, T. K. F., Ly-Trong, N., et al. & Minh, B. Q. (2026). IQ-TREE 3: phylogenomic inference software using complex evolutionary models. Molecular Biology and Evolution 43(5):msag117. https://doi.org/10.1093/molbev/msag117 (default tree backend)
  3. Kalyaanamoorthy, S., et al. (2017). ModelFinder: fast model selection for accurate phylogenetic estimates. Nature Methods 14:587–589. https://doi.org/10.1038/nmeth.4285 (cite only if you set model: MFP)
  4. Price, M. N., Dehal, P. S. & Arkin, A. P. (2010). FastTree 2 — approximately maximum-likelihood trees for large alignments. PLoS ONE 5(3):e9490. https://doi.org/10.1371/journal.pone.0009490 (cite if backend: fasttree)
  5. Kozlov, A. M., et al. (2019). RAxML-NG: a fast, scalable and user-friendly tool for maximum likelihood phylogenetic inference. Bioinformatics 35(21):4453–4455. https://doi.org/10.1093/bioinformatics/btz305 (cite if backend: raxml-ng)
  6. Sukumaran, J. & Holder, M. T. (2010). DendroPy: a Python library for phylogenetic computing. Bioinformatics 26(12):1569–1571. https://doi.org/10.1093/bioinformatics/btq228 (tree validation, midpoint rooting and the long-branch QC report)

Supporting the module's design decisions, rather than tools it runs:

  1. Tan, G., et al. (2015). Current methods for automated filtering of multiple sequence alignments frequently worsen single-gene phylogenetic inference. Systematic Biology 64(5):778–791. https://doi.org/10.1093/sysbio/syv033 (why no alignment masking)
  2. Janssen, S., et al. (2018). Phylogenetic placement of exact amplicon sequences improves associations with clinical information. mSystems 3(3):e00021-18. https://doi.org/10.1128/msystems.00021-18 (the long-branch UniFrac artifact the QC report exists to catch)
  3. Mai, U., Sayyari, E. & Mirarab, S. (2017). Minimum variance rooting of phylogenetic trees and implications for species tree reconstruction. PLoS ONE 12(8):e0182238. https://doi.org/10.1371/journal.pone.0182238 (midpoint-rooting instability; why the tree is exported unrooted only)

Methods behind the phylogeny benchmark

Referenced from the Phylogeny validation page. Not tools MetaFlux runs — the measures and models used to check its defaults.

  1. Robinson, D. F. & Foulds, L. R. (1981). Comparison of phylogenetic trees. Mathematical Biosciences 53:131–147. https://doi.org/10.1016/0025-5564(81)90043-2 (Robinson–Foulds distance)
  2. Sokal, R. R. & Rohlf, F. J. (1962). The comparison of dendrograms by objective methods. Taxon 11:33–40. https://doi.org/10.2307/1217208 (cophenetic / patristic distance correlation)
  3. Faith, D. P. (1992). Conservation evaluation and phylogenetic diversity. Biological Conservation 61:1–10. https://doi.org/10.1016/0006-3207(92)91201-3 (Faith's PD — a sum of branch lengths)
  4. Strimmer, K. & von Haeseler, A. (1997). Likelihood-mapping: a simple method to visualize phylogenetic content of a sequence alignment. PNAS 94:6815–6819. https://doi.org/10.1073/pnas.94.13.6815
  5. Hoang, D. T., et al. (2018). UFBoot2: improving the ultrafast bootstrap approximation. Molecular Biology and Evolution 35:518–522. https://doi.org/10.1093/molbev/msx281
  6. Guindon, S., et al. (2010). New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Systematic Biology 59:307–321. https://doi.org/10.1093/sysbio/syq010 (SH-aLRT branch test)
  7. Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics 6:461–464. https://doi.org/10.1214/aos/1176344136 (BIC, the criterion ModelFinder ranks by)
  8. Tavaré, S. (1986). Some probabilistic and statistical problems in the analysis of DNA sequences. Lectures on Mathematics in the Life Sciences 17:57–86. (GTR model)
  9. Kimura, M. (1981). Estimation of evolutionary distances between homologous nucleotide sequences. PNAS 78:454–458. https://doi.org/10.1073/pnas.78.1.454 (three-substitution-type models; TPM3u)
  10. Yang, Z. (1994). Maximum likelihood phylogenetic estimation from DNA sequences with variable rates over sites: approximate methods. Journal of Molecular Evolution 39:306–314. https://doi.org/10.1007/BF00160154 (discrete gamma rate heterogeneity, +G4)
  11. Yang, Z. (1995). A space-time process model for the evolution of DNA sequences. Genetics 139:993–1005. (FreeRate site-rate heterogeneity, +R)
  12. Soubrier, J., et al. (2012). The influence of rate heterogeneity among sites on the time dependence of molecular rates. Molecular Biology and Evolution 29:3345–3358. https://doi.org/10.1093/molbev/mss140 (FreeRate heterogeneity, +R)
  13. Capella-Gutiérrez, S., Silla-Martínez, J. M. & Gabaldón, T. (2009). trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25:1972–1973. https://doi.org/10.1093/bioinformatics/btp348 (evaluated as an optional mask; not shipped)
  14. Bianchini, G., Zhu, Q., Cicconardi, F. & Moody, E. R. R. (2026). AliFilter. Molecular Biology and Evolution 43(4):msag097. https://doi.org/10.1093/molbev/msag097 (evaluated as an optional mask; not shipped — not installable from bioconda)

Phylogeny module — design decisions and the full-gene check

Papers the Phylogeny page cites for why the module is 16S-only, and the Phylogeny validation page for the full-gene check.

  1. Medlar, A., Aivelo, T. & Löytynoja, A. (2014). Séance: reference-based phylogenetic analysis for 18S rRNA studies. BMC Evolutionary Biology 14:235. https://doi.org/10.1186/s12862-014-0235-7 (short-fragment branch lengths are underestimated; the 18S deferral)
  2. Tedersoo, L., et al. (2022). Best practices in metabarcoding of fungi: from experimental design to results. Molecular Ecology 31:2769–2795. https://doi.org/10.1111/mec.16460 (ITS is not alignable beyond genus level)
  3. Poirier, S., et al. (2018). Deciphering intra-species bacterial diversity of meat and seafood spoilage microbiota using gyrB amplicon sequencing: a comparative analysis with 16S rDNA V3-V4 amplicon sequencing. PLoS ONE 13:e0204629. https://doi.org/10.1371/journal.pone.0204629 (parE paralogue co-amplification; the gyrB exclusion)
  4. Case, R. J., et al. (2007). Use of 16S rRNA and rpoB genes as molecular markers for microbial ecology studies. Applied and Environmental Microbiology 73:278–288. https://doi.org/10.1128/AEM.01177-06 (rpoB saturation at all three codon positions; the rpoB exclusion)
  5. Apprill, A., McNally, S., Parsons, R. & Weber, L. (2015). Minor revision to V4 region SSU rRNA 806R gene primer greatly increases detection of SAR11 bacterioplankton. Aquatic Microbial Ecology 75:129–137. https://doi.org/10.3354/ame01753 (806R-B; used with 515F-Y, ref. 27, for the V4 fragments of the full-gene check)

Downstream analysis in R (the worked example)

Packages and methods the Phylogeny page's R example uses or cites. None is a MetaFlux dependency — they run in your own session.

  1. Paradis, E. & Schliep, K. (2019). ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35:526–528. https://doi.org/10.1093/bioinformatics/bty633
  2. Schliep, K. P. (2011). phangorn: phylogenetic analysis in R. Bioinformatics 27:592–593. https://doi.org/10.1093/bioinformatics/btq706 (midpoint rooting)
  3. Smith, D. P. & CMMR. rbiom: read/write, transform and visualize microbiome datasets. R package. https://cmmr.github.io/rbiom/ (UniFrac; compiled implementation)
  4. Oksanen, J., et al. vegan: Community Ecology Package. R package. https://cran.r-project.org/package=vegan (PCoA, PERMANOVA, betadisper)
  5. Davis, N. M., Proctor, D. M., Holmes, S. P., Relman, D. A. & Callahan, B. J. (2018). Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome 6:226. https://doi.org/10.1186/s40168-018-0605-2 (decontam)
  6. Saary, P., Forslund, K., Bork, P. & Hildebrand, F. (2017). RTK: efficient rarefaction analysis of large datasets. Bioinformatics 33:2594–2595. https://doi.org/10.1093/bioinformatics/btx206 (multiple rarefaction of non-phylogenetic indices; takes no tree)
  7. Lozupone, C. & Knight, R. (2005). UniFrac: a new phylogenetic method for comparing microbial communities. Applied and Environmental Microbiology 71:8228–8235. https://doi.org/10.1128/AEM.71.12.8228-8235.2005 · Lozupone, C., Hamady, M., Kelley, S. T. & Knight, R. (2007). Quantitative and qualitative beta diversity measures lead to different insights. AEM 73:1576–1585. https://doi.org/10.1128/AEM.01996-06 (weighted UniFrac)
  8. Hurlbert, S. H. (1971). The nonconcept of species diversity: a critique and alternative parameters. Ecology 52:577–586. https://doi.org/10.2307/1934145 (the rarefaction expectation)
  9. Nipperess, D. A. & Matsen, F. A. (2013). The mean and variance of phylogenetic diversity under rarefaction. Methods in Ecology and Evolution 4:603–615. https://doi.org/10.1111/2041-210X.12055 (the closed form for expected Faith's PD used in the example)
  10. McMurdie, P. J. & Holmes, S. (2014). Waste not, want not: why rarefying microbiome data is inadmissible. PLoS Computational Biology 10:e1003531. https://doi.org/10.1371/journal.pcbi.1003531 (the case against rarefying)
  11. Schloss, P. D. (2024). Rarefaction is currently the best approach to control for uneven sequencing effort in amplicon sequence analyses. mSphere 9:e00354-23. https://doi.org/10.1128/msphere.00354-23 · Schloss, P. D. (2024). Waste not, want not: revisiting the analysis that called into question the practice of rarefaction. mSphere 9:e00355-23. https://doi.org/10.1128/msphere.00355-23 (the case for rarefaction proper)

  12. Anderson, M. J. (2001). A new method for non-parametric multivariate analysis of variance. Austral Ecology 26:32–46. https://doi.org/10.1111/j.1442-9993.2001.01070.pp.x (PERMANOVA) · Anderson, M. J. (2006). Distance-based tests for homogeneity of multivariate dispersions. Biometrics 62:245–253. https://doi.org/10.1111/j.1541-0420.2005.00440.x (betadisper)

  13. Mantel, N. (1967). The detection of disease clustering and a generalized regression approach. Cancer Research 27:209–220. (Mantel test for agreement between distance matrices)

  14. Gower, J. C. (1975). Generalized Procrustes analysis. Psychometrika 40:33–51. https://doi.org/10.1007/BF02291478 · Peres-Neto, P. R. & Jackson, D. A. (2001). How well do multivariate data sets match? The advantages of a Procrustean superimposition approach over the Mantel test. Oecologia 129:169–178. https://doi.org/10.1007/s004420100720 (Procrustes comparison of ordinations; vegan::protest)

sylph profiler (optional, shotgun)

Cite these when shotgun.profiler is sylph or both, together with the source of the genomes in each database used.

  1. Shaw, J. & Yu, Y. W. (2025). Rapid species-level metagenome profiling and containment estimation with sylph. Nature Biotechnology 43(8):1348–1359. https://doi.org/10.1038/s41587-024-02412-y
  2. Shaw, J. sylph-tax: taxonomy manipulation for sylph. GitHub. https://github.com/bluenote-1577/sylph-tax (adds the lineages to sylph's output; MetaFlux pins version 1.9.2, whose lineage tables come from Zenodo, and GlobDB r232's from the GlobDB server)
  3. sylph pre-built databases. https://sylph-docs.github.io/pre%E2%80%90built-databases/ (user-supplied at runtime — cite the exact database files used, e.g. gtdb-r232-c200-dbv1.syldb, and the source of their genomes: GTDB, ref. 91, at the release of the database, or NCBI RefSeq for the fungal database)

COI marker

Cite these for a COI run, with the version of the set of bacterial COI homologues that was used.

  1. Leray, M., Knowlton, N. & Machida, R. J. (2022). MIDORI2: a collection of quality controlled, preformatted, and regularly updated reference databases for taxonomic assignment of eukaryotic mitochondrial sequences. Environmental DNA 4:894–907. https://doi.org/10.1002/edn3.303 (release GB273, UNIQ NUC_SP SINTAX file and taxonomy snapshot: https://doi.org/10.5281/zenodo.22685701)
  2. Antonielli, L. (2026). Bacterial and archaeal COI homologues from GTDB R232: a reference set for off-target screening (Version gtdb-r232.v1). Zenodo. https://doi.org/10.5281/zenodo.23820760 (identity B of the off-target guard, and the list of MIDORI2 records it matched, those judged bacterial being left out by the conversion; how it was built is on Bacterial COI homologues (reference set))
  3. Parks, D. H., Chaumeil, P.-A., Mussig, A. J., Rinke, C., Chuvochina, M. & Hugenholtz, P. (2026). GTDB release 10: a complete and systematic taxonomy for 715 230 bacterial and 17 245 archaeal genomes. Nucleic Acids Research 54(D1):D743–D754. https://doi.org/10.1093/nar/gkaf1040 (the paper of GTDB release 10, R226; GTDB is the source of the set of bacterial COI homologues, which is built from release R232)
  4. Elbrecht, V., et al. (2019). Validation of COI metabarcoding primers for terrestrial arthropods. PeerJ 7:e7745. https://doi.org/10.7717/peerj.7745 (the insect mock used to validate COI; see Test datasets)

Cited on the COI page; not a tool MetaFlux runs:

  1. Andújar, C., et al. (2021). Validated removal of nuclear pseudogenes and sequencing artefacts from mitochondrial metabarcode data. Molecular Ecology Resources 21:1772–1787. https://doi.org/10.1111/1755-0998.13337 (metaMATE, which judges NUMTs by abundance and phylogeny)

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.