Simple approach to bacterial genomes comparison based on Average Nucleotide Identity (ANI) using fastANI and ANIclustermap

Klaudia Musiał; Leon Petruńko; Dawid Gmiter

doi:10.18778/1730-2366.18.10

Authors

Klaudia Musiał Jan Kochanowski University of Kielce, Faculty of Natural Sciences, Institute of Biology, Department of Microbiology, Poland https://orcid.org/0009-0001-0657-2304
Leon Petruńko Jan Kochanowski University of Kielce, Faculty of Natural Sciences, Institute of Biology, Department of Microbiology, Poland https://orcid.org/0009-0004-8346-5440
Dawid Gmiter Jan Kochanowski University of Kielce, Faculty of Natural Sciences, Institute of Biology, Department of Microbiology, Poland https://orcid.org/0000-0001-8663-129X

DOI:

https://doi.org/10.18778/1730-2366.18.10

Keywords:

bacterial genomes comparison, bacterial phylogeny, Average Nucleotide Identity (ANI), fastANI, ANIclustermap

Abstract

The Average Nucleotide Identity (ANI) was proposed as a standard for taxonomic affiliation of newly sequenced bacterial genomes. However, usage of ANI value as a means of strains phenotypic diversity offers a relatively easy way for studding bacterial phylogeny. Here we present a simple approach to bacterial genomes comparison based on ANI using fastANI and ANIclustermap. Both programs are available as an open-source tools and can be run using simple command lines. We present protocol for programs installation as a conda packages, that facilitate it utilization. Further, we explain how to prepare commands to perform the analysis. We believed our work could be useful for young scientist that begin their experience with bioinformatics.

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References

Arahal, D.R. 2014. Whole-genome analyses: Average nucleotide identity. [In:] Methods in Microbiology, Vol. 41, pp. 103–122.

Buermans, H.P.J., den Dunnen, J.T. 2014. Next generation sequencing technology: Advances and applications. Biochimica et Biophysica Acta – Molecular Basis of Disease, 1842(10), 1932–1941.

Deurenberg, R H., Bathoorn, E., Chlebowicz, M.A., Couto, N., Ferdous, M., García-Cobos, S., Kooistra-Smid, A.M. D., Raangs, E.C., Rosema, S., Veloo, A.C. M., Zhou, K., Friedrich, A.W., Rossen, J.W.A. 2017. Application of next generation sequencing in clinical microbiology and infection prevention. Journal of Biotechnology, 243, 16–24.

Edwards, D.J., Holt, K.E. 2013. Beginner’s guide to comparative bacterial genome analysis using next-generation sequence data. Microbial Informatics and Experimentation, 3(1), 2.

Figueras, M.J., Beaz-Hidalgo, R., Hossain, M.J., Liles, M.R. 2014. Taxonomic affiliation of new genomes should be verified using Average Nucleotide Identity and Multilocus Phylogenetic Analysis. Genome Announcements, 2(6), e00927-14-e00927-14.

Gmiter, D., Nawrot, S., Pacak, I., Zegadło, K., Kaca, W. 2021. Towards a better understanding of the bacterial pan-genome. Acta Universitatis Lodziensis. Folia Biologica et Oecologica, 17, 84–96.

Goris, J., Konstantinidis, K.T., Klappenbach, J.A., Coenye, T., Vandamme, P., Tiedje, J.M. 2007. DNA-DNA hybridization values and their relationship to whole-genome sequence similarities. International Journal of Systematic and Evolutionary Microbiology, 57(1), 81–91.

Hodkinson, B.P., Grice, E.A. 2015. Next-Generation Sequencing: A Review of technologies and tools for wound microbiome research. Advances in Wound Care, 4(1), 50–58.

Jain, C., Rodriguez-R, L.M., Phillippy, A.M., Konstantinidis, K.T., Aluru, S. 2018. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nature Communications, 9(1), 1–8.

Kobras, C.M., Fenton, A.K., Sheppard, S.K. 2021. Next-generation microbiology: from comparative genomics to gene function. In Genome Biology (Vol. 22, Issue 1). BioMed Central Ltd. DOI: https://doi.org/10.1186/s13059-021-02344-9