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Citation and references

Terms used on this page
AMR antimicrobial resistance
WGS whole-genome sequencing
EFSA European Food Safety Authority
QPS Qualified Presumption of Safety, EFSA's safety status for well-characterised microorganisms
ICE integrative and conjugative element — moves itself between cells
IME integrative mobilisable element — needs a helper element to move
MAG metagenome-assembled genome, a genome reconstructed from a mixed community

Citing BacFlux

Antonielli, L., Großkinsky, D. K., Koch, H., Trognitz, F., Sanchez Mejia, A., & Nagel, M. (2024). BacFlux: A workflow for bacterial short-read assembly, QC, annotation, and more. Zenodo. https://doi.org/10.5281/zenodo.11143917

That DOI is the concept DOI — it always resolves to the newest release. Every tagged release also gets its own version DOI on Zenodo. Cite the version DOI when the exact code matters, which for a methods section it usually does.

Machine-readable metadata is in CITATION.cff; GitHub and Zenodo read that file to build a citation for you.

The retired sibling workflows keep their DOIs

v2.0.0 folds BacFluxL and BacFluxL+ into this repository. Their Zenodo records stay valid, so an analysis already published with one of them remains citable and reproducible — cite the record you actually ran. See Coming from v1.

Cite the tools as well

Almost all of the science BacFlux reports comes from other people's tools and databases, and the mobilome module in particular is an integrator: it decides how to combine other tools' calls, and every underlying detection belongs to somebody else.

Which references you need depends on the mode you ran and on which optional stages you switched on, so the tables below are grouped that way. The complete list, with the licensing notes attached, is in CITATIONS.md.

Every run, whatever the mode

Step Tool or database Reference
Workflow engine Snakemake; Bioconda (one conda environment per rule) 37, 24
Decontamination BLAST+ against NCBI core nt; BlobTools 7, 33
Coverage track for BlobTools Bowtie 2 (short reads), minimap2 (long reads or contigs), SAMtools 34, 35, 15
Assembly statistics QUAST 5.3.0 27
Completeness and contamination CheckM 1.2.5 42
Mapping QC (modes with reads) QualiMap 2.3 40
Taxonomic placement GTDB-Tk 2.7.2 against GTDB R232 10, 43
Annotation Bakta 1.12.1 (database v6.0) 46
Functional annotation eggNOG-mapper 2.1.15 against eggNOG 5.0.2 9, 28
Secondary metabolites antiSMASH 8.0.4 4
CAZymes run_dbCAN 5.1.2 (dbCAN3) 54
AMR and virulence on contigs ABRicate 1.4.0 — plus the databases below 47
Plasmids Platon 1.8, with a supplementary BLAST+ line beside each call 45, 7
Prophages VirSorter2 2.2.4, with CheckV 1.1.1 grading what it found 25, 38
Aggregated report MultiQC 1.33 21

ABRicate's eight databases. The screen runs once per database and the eight tables are kept side by side, so cite the ones you actually read.

db Database Reference
argannot ARG-ANNOT 26
card CARD 2, 30
ecoh EcOH — E. coli O- and H-antigen loci 29
ecoli_vf E. coli virulence factors ships with ABRicate — 47
megares MEGARes 2.0 17
ncbi NCBI AMRFinderPlus Reference Gene Catalog 23
resfinder ResFinder 53
vfdb VFDB 11

What your mode adds

mode: Front end Reference
illumina Bowtie 2 (PhiX removal), fastp 1.0.1, SPAdes 4.3.0 34, 12, 3
nanopore NanoPlot 1.47.1 (NanoPack), Filtlong 0.3.1, Flye 2.9.6, dnaapler 1.4.0, Medaka 2.2.2 (optional — parameters.{nanopore,hybrid}.medaka_model: false skips it) 16, 50, 32, 5, 41
hybrid both front ends above — the short reads are assembled too, as the comparator genome — then BWA + Polypolish 0.7.1 to correct the ONT assembly with them, and Snippy 4.6.0 to compare the two the two rows above, plus 36, 51, 48
contigs no assembler — contig filtering only, with minimap2 self-mapping for the coverage track 35

Short-read modes only. illumina and hybrid also map the trimmed reads onto CARD v4.0.1 with BBMap 39.33 (refs 6, 2, 30). Read Antimicrobial resistance before quoting that leg: it is a sensitive screen, and its specificity comes from the reference-length coverage gate rather than from read identity.

Optional stages and layers

Each of these is off until you turn it on, and each brings its own citations.

Switch What runs Reference
phage.caller: genomad geNomad 1.12.0 replaces VirSorter2 as the virus caller, and its plasmid calls are compared with Platon's 8
mobilome.run: true AMRFinderPlus, run from the Bakta environment against the AMRFinderPlus database that ships inside bakta_db; ISEScan 1.7.3 and MacSyFinder 2.1.6, both HMMER searches; and the CONJscan models MacSyFinder runs — cite all three CONJscan papers; they are the models that run 23, 22, 52, 39, 18, 1, 13, 14
mobilome.icescan.run: true the ICEscan model set, distributed with ICEfinder2 and itself a fork of CONJscan — so cite the ICEberg paper and the three CONJscan papers 49, 1, 13, 14
mobilome.tncentral.url or .dir TnCentral, to name curated transposons and integrons 44
mobilome.iceberg.urls or .dir ICEberg 3.0 sequences, to name the ICE and IME candidates 49
mobilome.isosdb.fasta_url + family_map_url, or .dir ISOSDB read mapping with BBMap, for IS copy number 31, 6

The mobility ladder is built around EFSA's intrinsic-versus-acquired distinction (refs 19, 20). Antimicrobial resistance lists the two further EFSA documents — the 2024 WGS statement behind the 80%/70% reporting thresholds, and the Bacillus catalogue — and says what BacFlux may and may not claim from any of them.

ISfinder is not part of a standard run

TnCentral publishes further endpoints whose content carries ISfinder's terms. Those are deliberately not wired into config.yaml, so nothing BacFlux fetches by default touches ISfinder data. If you point mobilome.tncentral.url at one of them yourself, cite ISfinder as well:

Siguier, P., Perochon, J., Lestrade, L., Mahillon, J., & Chandler, M. (2006). ISfinder: the reference centre for bacterial insertion sequences. Nucleic Acids Research, 34(Database issue), D32–D36. https://doi.org/10.1093/nar/gkj014

Terms for every optional layer are listed on Licensing; what each layer buys you is on Optional layers.

Acknowledgements

This work was originally supported by the Austrian Science Fund (FWF) under Project I6030-B.

Much of v2.0.0 — merging the four v1 workflows into one, building the mobilome module and writing this documentation — was done with Claude Code. Anthropic provided six months of Claude Max through their Open Source programme, and the scale of the v2 rewrite would not have been realistic without it.

References

  1. Abby, S. S., Cury, J., Guglielmini, J., Néron, B., Touchon, M., & Rocha, E. P. C. (2016). Identification of protein secretion systems in bacterial genomes. Scientific Reports, 6, 23080. https://doi.org/10.1038/srep23080 (CONJscan model set)
  2. Alcock, B. P., et al. (2023). CARD 2023: expanded curation, support for machine learning, and resistome prediction at the Comprehensive Antibiotic Resistance Database. Nucleic Acids Research, 51(D1), D690–D699. https://doi.org/10.1093/nar/gkac920
  3. Bankevich, A., et al. (2012). SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. Journal of Computational Biology, 19(5), 455–477. https://doi.org/10.1089/cmb.2012.0021
  4. Blin, K., et al. (2025). antiSMASH 8.0: extended gene cluster detection capabilities and analyses of chemistry, enzymology, and regulation. Nucleic Acids Research, 53(W1), W32–W38. https://doi.org/10.1093/nar/gkaf334
  5. Bouras, G., Grigson, S. R., Papudeshi, B., Mallawaarachchi, V., & Roach, M. J. (2024). Dnaapler: a tool to reorient circular microbial genomes. Journal of Open Source Software, 9(93), 5968. https://doi.org/10.21105/joss.05968
  6. Bushnell, B. (2014). BBMap: a fast, accurate, splice-aware aligner. https://escholarship.org/uc/item/1h3515gn
  7. Camacho, C., et al. (2009). BLAST+: architecture and applications. BMC Bioinformatics, 10, 421. https://doi.org/10.1186/1471-2105-10-421
  8. Camargo, A. P., et al. (2024). Identification of mobile genetic elements with geNomad. Nature Biotechnology, 42(8), 1303–1312. https://doi.org/10.1038/s41587-023-01953-y
  9. Cantalapiedra, C. P., Hernández-Plaza, A., Letunic, I., Bork, P., & Huerta-Cepas, J. (2021). eggNOG-mapper v2: functional annotation, orthology assignments, and domain prediction at the metagenomic scale. Molecular Biology and Evolution, 38(12), 5825–5829. https://doi.org/10.1093/molbev/msab293
  10. Chaumeil, P.-A., Mussig, A. J., Hugenholtz, P., & Parks, D. H. (2022). GTDB-Tk v2: memory friendly classification with the genome taxonomy database. Bioinformatics, 38(23), 5315–5316. https://doi.org/10.1093/bioinformatics/btac672
  11. Chen, L., Zheng, D., Liu, B., Yang, J., & Jin, Q. (2016). VFDB 2016: hierarchical and refined dataset for big data analysis — 10 years on. Nucleic Acids Research, 44(D1), D694–D697. https://doi.org/10.1093/nar/gkv1239
  12. Chen, S., Zhou, Y., Chen, Y., & Gu, J. (2018). fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics, 34(17), i884–i890. https://doi.org/10.1093/bioinformatics/bty560
  13. Coluzzi, C., Garcillán-Barcia, M. P., de la Cruz, F., & Rocha, E. P. C. (2022). Evolution of plasmid mobility: origin and fate of conjugative and nonconjugative plasmids. Molecular Biology and Evolution, 39(6), msac115. https://doi.org/10.1093/molbev/msac115 (CONJscan model set)
  14. Cury, J., Touchon, M., & Rocha, E. P. C. (2017). Integrative and conjugative elements and their hosts: composition, distribution and organization. Nucleic Acids Research, 45(15), 8943–8956. https://doi.org/10.1093/nar/gkx607 (CONJscan model set)
  15. Danecek, P., et al. (2021). Twelve years of SAMtools and BCFtools. GigaScience, 10(2), giab008. https://doi.org/10.1093/gigascience/giab008
  16. De Coster, W., D'Hert, S., Schultz, D. T., Cruts, M., & Van Broeckhoven, C. (2018). NanoPack: visualizing and processing long-read sequencing data. Bioinformatics, 34(15), 2666–2669. https://doi.org/10.1093/bioinformatics/bty149 (NanoPlot)
  17. Doster, E., et al. (2020). MEGARes 2.0: a database for classification of antimicrobial drug, biocide and metal resistance determinants in metagenomic sequence data. Nucleic Acids Research, 48(D1), D561–D569. https://doi.org/10.1093/nar/gkz1010
  18. Eddy, S. R. (2011). Accelerated profile HMM searches. PLoS Computational Biology, 7(10), e1002195. https://doi.org/10.1371/journal.pcbi.1002195 (the profile-HMM engine inside ISEScan, MacSyFinder, VirSorter2 and run_dbCAN)
  19. EFSA Panel on Biological Hazards (BIOHAZ). (2023). Statement on how to interpret the QPS qualification on 'acquired antimicrobial resistance genes'. EFSA Journal, 21(10), 8323. https://doi.org/10.2903/j.efsa.2023.8323 (the method for telling intrinsic from acquired)
  20. EFSA Scientific Committee. (2025). Guidance on the characterisation of microorganisms in support of the risk assessment of products used in the food chain. EFSA Journal, 23(11), e9705. https://doi.org/10.2903/j.efsa.2025.9705 (the guidance in force; its Appendix A lists what it supersedes in the 2018 FEEDAP guidance)
  21. Ewels, P., Magnusson, M., Lundin, S., & Käller, M. (2016). MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics, 32(19), 3047–3048. https://doi.org/10.1093/bioinformatics/btw354
  22. Feldgarden, M., et al. (2019). Validating the AMRFinder tool and resistance gene database by using antimicrobial resistance genotype–phenotype correlations in a collection of isolates. Antimicrobial Agents and Chemotherapy, 63(11), e00483-19. https://doi.org/10.1128/AAC.00483-19
  23. Feldgarden, M., et al. (2021). AMRFinderPlus and the Reference Gene Catalog facilitate examination of the genomic links among antimicrobial resistance, stress response, and virulence. Scientific Reports, 11, 12728. https://doi.org/10.1038/s41598-021-91456-0
  24. Grüning, B., et al. (2018). Bioconda: sustainable and comprehensive software distribution for the life sciences. Nature Methods, 15, 475–476. https://doi.org/10.1038/s41592-018-0046-7
  25. Guo, J., et al. (2021). VirSorter2: a multi-classifier, expert-guided approach to detect diverse DNA and RNA viruses. Microbiome, 9(1), 37. https://doi.org/10.1186/s40168-020-00990-y
  26. Gupta, S. K., et al. (2014). ARG-ANNOT, a new bioinformatic tool to discover antibiotic resistance genes in bacterial genomes. Antimicrobial Agents and Chemotherapy, 58(1), 212–220. https://doi.org/10.1128/AAC.01310-13
  27. Gurevich, A., Saveliev, V., Vyahhi, N., & Tesler, G. (2013). QUAST: quality assessment tool for genome assemblies. Bioinformatics, 29(8), 1072–1075. https://doi.org/10.1093/bioinformatics/btt086
  28. Huerta-Cepas, J., et al. (2019). eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Research, 47(D1), D309–D314. https://doi.org/10.1093/nar/gky1085
  29. Ingle, D. J., et al. (2016). In silico serotyping of E. coli from short read data identifies limited novel O-loci but extensive diversity of O:H serotype combinations within and between pathogenic lineages. Microbial Genomics, 2(7), e000064. https://doi.org/10.1099/mgen.0.000064 (the ecoh database)
  30. Jia, B., et al. (2017). CARD 2017: expansion and model-centric curation of the Comprehensive Antibiotic Resistance Database. Nucleic Acids Research, 45(D1), D566–D573. https://doi.org/10.1093/nar/gkw1004
  31. Kirsch, J. M., Hryckowian, A. J., & Duerkop, B. A. (2024). A metagenomics pipeline reveals insertion sequence-driven evolution of the microbiota. Cell Host & Microbe, 32(5), 739–754.e4. https://doi.org/10.1016/j.chom.2024.03.005 (pseudoR, the source of ISOSDB)
  32. Kolmogorov, M., Yuan, J., Lin, Y., & Pevzner, P. A. (2019). Assembly of long, error-prone reads using repeat graphs. Nature Biotechnology, 37(5), 540–546. https://doi.org/10.1038/s41587-019-0072-8 (Flye)
  33. Laetsch, D. R., & Blaxter, M. L. (2017). BlobTools: interrogation of genome assemblies. F1000Research, 6, 1287. https://doi.org/10.12688/f1000research.12232.1
  34. Langmead, B., & Salzberg, S. L. (2012). Fast gapped-read alignment with Bowtie 2. Nature Methods, 9(4), 357–359. https://doi.org/10.1038/nmeth.1923
  35. Li, H. (2018). Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics, 34(18), 3094–3100. https://doi.org/10.1093/bioinformatics/bty191
  36. Li, H., & Durbin, R. (2009). Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics, 25(14), 1754–1760. https://doi.org/10.1093/bioinformatics/btp324 (BWA, the aligner Polypolish reads)
  37. Mölder, F., et al. (2021). Sustainable data analysis with Snakemake. F1000Research, 10, 33. https://doi.org/10.12688/f1000research.29032.2
  38. Nayfach, S., Camargo, A. P., Schulz, F., Eloe-Fadrosh, E., Roux, S., & Kyrpides, N. C. (2021). CheckV assesses the quality and completeness of metagenome-assembled viral genomes. Nature Biotechnology, 39(5), 578–585. https://doi.org/10.1038/s41587-020-00774-7
  39. Néron, B., Denise, R., Coluzzi, C., Touchon, M., Rocha, E. P. C., & Abby, S. S. (2023). MacSyFinder v2: improved modelling and search engine to identify molecular systems in genomes. Peer Community Journal, 3, e28. https://doi.org/10.24072/pcjournal.250
  40. Okonechnikov, K., Conesa, A., & García-Alcalde, F. (2016). Qualimap 2: advanced multi-sample quality control for high-throughput sequencing data. Bioinformatics, 32(2), 292–294. https://doi.org/10.1093/bioinformatics/btv566
  41. Oxford Nanopore Technologies. Medaka. https://github.com/nanoporetech/medaka
  42. Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P., & Tyson, G. W. (2015). CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Research, 25(7), 1043–1055. https://doi.org/10.1101/gr.186072.114
  43. Parks, D. H., Chuvochina, M., Rinke, C., Mussig, A. J., Chaumeil, P.-A., & Hugenholtz, P. (2022). GTDB: an ongoing census of bacterial and archaeal diversity through a phylogenetically consistent, rank normalized and complete genome-based taxonomy. Nucleic Acids Research, 50(D1), D785–D794. https://doi.org/10.1093/nar/gkab776 (cite the release you ran — BacFlux 2.0.0 expects R232)
  44. Ross, K., et al. (2021). TnCentral: a prokaryotic transposable element database and web portal for transposon analysis. mBio, 12(5), e02060-21. https://doi.org/10.1128/mBio.02060-21
  45. Schwengers, O., Barth, P., Falgenhauer, L., Hain, T., Chakraborty, T., & Goesmann, A. (2020). Platon: identification and characterization of bacterial plasmid contigs in short-read draft assemblies exploiting protein sequence-based replicon distribution scores. Microbial Genomics, 6(10), mgen000398. https://doi.org/10.1099/mgen.0.000398
  46. Schwengers, O., Jelonek, L., Dieckmann, M. A., Beyvers, S., Blom, J., & Goesmann, A. (2021). Bakta: rapid and standardized annotation of bacterial genomes via alignment-free sequence identification. Microbial Genomics, 7(11), 000685. https://doi.org/10.1099/mgen.0.000685
  47. Seemann, T. (2020). ABRicate: mass screening of contigs for antimicrobial and virulence genes. https://github.com/tseemann/abricate
  48. Seemann, T. Snippy: rapid haploid variant calling and core genome alignment. https://github.com/tseemann/snippy
  49. Wang, M., et al. (2024). ICEberg 3.0: functional categorization and analysis of the integrative and conjugative elements in bacteria. Nucleic Acids Research, 52(D1), D732–D737. https://doi.org/10.1093/nar/gkad935 (also the paper to cite for ICEfinder2 / ICEscan)
  50. Wick, R. R. Filtlong: quality filtering tool for long reads. https://github.com/rrwick/Filtlong
  51. Wick, R. R., & Holt, K. E. (2022). Polypolish: short-read polishing of long-read bacterial genome assemblies. PLoS Computational Biology, 18(1), e1009802. https://doi.org/10.1371/journal.pcbi.1009802
  52. Xie, Z., & Tang, H. (2017). ISEScan: automated identification of insertion sequence elements in prokaryotic genomes. Bioinformatics, 33(21), 3340–3347. https://doi.org/10.1093/bioinformatics/btx433
  53. Zankari, E., et al. (2012). Identification of acquired antimicrobial resistance genes. Journal of Antimicrobial Chemotherapy, 67(11), 2640–2644. https://doi.org/10.1093/jac/dks261 (ResFinder)
  54. Zheng, J., Ge, Q., Yan, Y., Zhang, X., Huang, L., & Yin, Y. (2023). dbCAN3: automated carbohydrate-active enzyme and substrate annotation. Nucleic Acids Research, 51(W1), W115–W121. https://doi.org/10.1093/nar/gkad328

Design influence

Work that shaped the mobilome module without contributing code to it; the distinction is spelled out on Licensing.

  • EBI Mobilome Annotation Pipeline (Apache-2.0). https://github.com/EBI-Metagenomics/mobilome-annotation-pipeline — from which BacFlux adopted decisions rather than code: that ICEscan exists and is worth running, the Sequence Ontology terms for the mobilome GFF, the contig_id|mge_type-start:end element ID format, the discard-with-reason file, the 500 bp minimum element size and the 0.9 CDS-coverage rule. The two pipelines answer different questions: theirs goes deep on the mobilome of metagenomes and MAGs, BacFlux covers reads through to AMR mobility for single isolates.

  • ICEfinder2, whose variable-length repeat search — rather than a fixed-length probe — is why BacFlux's att-site search works the way it does. Established by reading it, then implemented independently.

  • Puterová, J. & Martínek, T. (2021) digIS: towards detecting distant and putative novel insertion sequence elements in prokaryotic genomes. BMC Bioinformatics 22:258. https://doi.org/10.1186/s12859-021-04177-6 — benchmarked here for the IS false-discovery figures quoted on the mobilome pages, not run by BacFlux.

Licence

BacFlux is released under the MIT Licence. The tools, model sets and databases it invokes are distributed under their own terms, which are listed on Licensing.