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Releases: SergioAlias/fusariumid-train

FUSARIUM-ID v3.0 for QIIME 2 version 2024.2

28 Mar 10:54
@SergioAlias SergioAlias
v3.0_2024.2
aed88e6

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FUSARIUM-ID v3.0 for QIIME 2 version 2024.2 Latest
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⚠️ I am not affiliated to the FUSARIUM-ID team. Classifiers are provided as is, without any guarantees or warranties.

Naive Bayes Classifiers for FUSARIUM-ID v3.0 trained for QIIME 2 version 2024.2.

📝 Changelog

  • First release! 🎉
  • Updated FUSARIUM-ID to version 3.0.
  • Updated TEF1 sequences of non-Fusarium fungi and other eukaryotes: downloaded 93,977 sequences from NCBI GenBank on March 26, 2025 using the query.

📊 Classifiers

We provide different classifiers according to whether we extract the amplicon region with PCR primers:

  • full_length (not recommended!): Full length sequences as they were downloaded from NCBI GenBank and FUSARIUM-ID. Note that sequences in the range of 100-3000 bp were downloaded from GenBank (see the query, (100[SLEN]:3000[SLEN])).

  • Amplicon region extracted with primer sequences, ideally the same primer sequences that you used for your PCR / sequencing:

    • EFseqF350_EFseqR: Amplicon region extracted with custom primers we use in the lab.

    • EF1-F2_EF1-R3: Amplicon region extracted with primers used in Boutigny et al. (2019).

    • 🤔 Should I add other common primers? Open an issue!

You can check primer sequences here.

We also provide two types of classifiers according to the information we keep from FUSARIUM-ID metadata:

  • until_sp: Fusarium taxonomy only contains the species name in the s__ level.

  • all_info: Fusarium taxonomy contains the species name and the Species Complex in the s__ level.

🔍 Why is this relevant? (click to show)

FUSARIUM-ID contains some undescribed Fusarium entries. These are annotated at the species level with the Species Complex to which the sequence belongs inmediately followed by the string "undesc" (e.g. FFSCundesc). However, when available, informal ad hoc nomenclature (Species Complex designation followed by a number) is included for those undescribed species (e.g. FSAMSC1).

If you plan to collapse your ASV table at the species level, you may want to consider all the undescribed Fusarium from a given Species Complex as the same "species" (using until_sp classifiers) or you may want to keep the ad hoc separation made by FUSARIUM-ID curators (i.e., use all_info classifiers). If you work at the ASV level, also note that sequences may vary slightly between these classifiers because there is a de-replication step that may remove undescribed sequences from the same Species Complex that are 100% identical.

🤝 References

If you use the classifiers, you should cite the following papers (click to show each section):

⚙️ Workflow methods

Bokulich, N. A., Kaehler, B. D., Rideout, J. R., Dillon, M., Bolyen, E., Knight, R., Huttley, G. A., & Caporaso, J. G. (2018). Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome, 6(1), 90. https://doi.org/10.1186/s40168-018-0470-z

Bolyen, E., Rideout, J. R., Dillon, M. R., Bokulich, N. A., Abnet, C. C., Al-Ghalith, G. A., Alexander, H., Alm, E. J., Arumugam, M., Asnicar, F., Bai, Y., Bisanz, J. E., Bittinger, K., Brejnrod, A., Brislawn, C. J., Brown, C. T., Callahan, B. J., Caraballo-Rodríguez, A. M., Chase, J., … Caporaso, J. G. (2019). Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nature Biotechnology, 37(8), 852–857. https://doi.org/10.1038/s41587-019-0209-9

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, É. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12(Oct), 2825–2830.

Robeson, M. S., O'Rourke, D. R., Kaehler, B. D., Ziemski, M., Dillon, M. R., Foster, J. T., & Bokulich, N. A. (2021). RESCRIPt: Reproducible sequence taxonomy reference database management. PLoS Computational Biology. https://doi.org/10.1371/journal.pcbi.1009581

Rognes, T., Flouri, T., Nichols, B., Quince, C., & Mahé, F. (2016). VSEARCH: a versatile open source tool for metagenomics. PeerJ, 4, e2584. https://doi.org/10.7717/peerj.2584

Wes McKinney. (2010). Data Structures for Statistical Computing in Python . In S. van der Walt & Jarrod Millman (Eds.), Proceedings of the 9th Python in Science Conference (pp. 51–56).

🧬 NCBI GenBank

Benson, D. A., Cavanaugh, M., Clark, K., Karsch-Mizrachi, I., Lipman, D. J., Ostell, J., & Sayers, E. W. (2012). GenBank. Nucleic Acids Research, 41(D1), D36–D42.

Coordinators, N. R. (2018). Database resources of the national center for biotechnology information. Nucleic Acids Research, 46(D1), D8–D13. https://doi.org/10.1093/nar/gkx1095

🍄 FUSARIUM-ID v3.0

Torres-Cruz, T. J., Whitaker, B. K., Proctor, R. H., Broders, K., Laraba, I., Kim, H. S., ... & Geiser, D. M. (2022). FUSARIUM-ID v. 3.0: An updated, downloadable resource for Fusarium species identification. Plant disease, 106(6), 1610-1616. https://doi.org/10.1094/PDIS-09-21-2105-SR

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