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INTERNATIONAL JOURNAL OF SYSTEMATIC AND EVOLUTIONARY MICROBIOLOGY logo

Volume 70, Issue 4

Systematic review of descriptions of novel bacterial species: evaluation of the twenty-first century taxonomy through text mining Free

Abstract

Although described bacterial species increased in the twenty-first century, they correspond to a tiny fraction of the actual number of species living on our planet. The volume of textual data of these descriptions constitutes valuable information for revealing trends that in turn could support strategies for improvement of bacterial taxonomy. In this study, a text mining approach was used to generate bibliometric data to verify the state-of-art of bacterial taxonomy. Around 9700 abstracts of bacterial classification containing the expression ‘sp. nov.’ and published between 2001 and 2018 were downloaded from PubMed and analysed. Most articles were from PR China and the Republic of Korea, and published in the . From about 10 800 species names detected, 93.33 % were considered valid according to the rules of the Bacterial Code, and they corresponded to 82.98 % of the total number of species validated between 2001 and 2018. , and each had more than 200 species described in the period. However, almost 40 % of all species were from the phylum . Most bacteria were Gram-stain-negative, bacilli and isolated from soil. Thirteen species and one genus homonyms were found. With respect to methodologies of bacterial characterization, the use of terms related to 16S rRNA and polar lipids increased along these years, and terms related to genome metrics only began to appear from 2009 onward, although at a relatively lower frequency. Bacterial taxonomy is known as a conservative discipline, but it gradually changed in terms of players and practices. With the advent of the mandatory use of genomic analyses for species description, we are probably witnessing a turning point in the evolution of bacterial taxonomy.

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Keyword(s): bacterial taxonomy , bibliometrics , Proteobacteria , pubmed and text mining
© 2020 The Authors
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2020-02-25
2024-04-23
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References

  1. Yarza P, Yilmaz P, Pruesse E, Glöckner FO, Ludwig W et al. Uniting the classification of cultured and uncultured bacteria and archaea using 16S rRNA gene sequences. Nat Rev Microbiol 2014; 12:635–645 [View Article]
     [Google Scholar]
  2. Goodwin S, McPherson JD, McCombie WR. Coming of age: ten years of next-generation sequencing technologies. Nat Rev Genet 2016; 17:333–351 [View Article]
     [Google Scholar]
  3. Garrity GM. A new genomics-driven taxonomy of bacteria and archaea: are we there yet?. J Clin Microbiol 2016; 54:1956–1963 [View Article]
     [Google Scholar]
  4. Chun J, Oren A, Ventosa A, Christensen H, Arahal DR et al. Proposed minimal standards for the use of genome data for the taxonomy of prokaryotes. Int J Syst Evol Microbiol 2018; 68:461–466 [View Article]
     [Google Scholar]
  5. De Vos P, Thompson F, Thompson C, Swings J. Chapter 2 – a flavor of prokaryotic taxonomy: systematics revisited. Microb Res 201729–44
     [Google Scholar]
  6. Pautasso M. Publication growth in biological sub-fields: patterns, predictability and sustainability. Sustainability 2012; 4:3234–3247 [View Article]
     [Google Scholar]
  7. Redfern J, Cobo MJ, Herrera-Viedma E. Editorial: Mapping microbiology with scientometrics - help provide a clearer vision of microbiology research around the globe. FEMS Microbiol Lett 2018; 365: [View Article]
     [Google Scholar]
  8. Moore ERB, Mihaylova SA, Vandamme P, Krichevsky MI, Dijkshoorn L. Microbial systematics and taxonomy: relevance for a microbial commons. Res Microbiol 2010; 161:430–438 [View Article]
     [Google Scholar]
  9. Hearst MA. Untangling text data mining. Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics (ACL '99) 1999 pp 3–10
     [Google Scholar]
  10. Kans J. Entrez Direct: E-utilities on the UNIX Command Line. Entrez Programming Utilities Help [Internet] 2010 Bethesda MD: National Center for Biotechnology Information (US); 2013
     [Google Scholar]
  11. Cock PJA, Antao T, Chang JT, Chapman BA, Cox CJ et al. Biopython: freely available python tools for computational molecular biology and bioinformatics. Bioinformatics 2009; 25:1422–1423 [View Article]
     [Google Scholar]
  12. McKinney W. Data structures for statistical computing in phyton. Proceedings of the 9th Python in Science Conference 2010 pp 51–56
     [Google Scholar]
  13. Hunter JD. Matplotlib: a 2D graphics environment. Computing in Science & Engineering 9 2007 pp 90–95 [View Article]
     [Google Scholar]
  14. Bird S, Loper E, Klein E. Natural Language Processing with Phyton O’Reilly Media Inc; 2009
     [Google Scholar]
  15. Tamames J, Rosselló-Móra R. On the fitness of microbial taxonomy. Trends Microbiol 2012; 20:514–516 [View Article]
     [Google Scholar]
  16. Oren A, Garrity GM. Then and now: a systematic review of the systematics of prokaryotes in the last 80 years. Antonie van Leeuwenhoek 2014; 106:43–56 [View Article]
     [Google Scholar]
  17. Ball P. China’s great leap forward in science. The Guardian 2018
     [Google Scholar]
  18. Yeom HW. South Korean science needs restructuring. Nature 2018; 558:511–513 [View Article]
     [Google Scholar]
  19. Whitman WB, Coleman DC, Wiebe WJ. Prokaryotes: the unseen majority. Proc Natl Acad Sci USA 1998; 95:6578–6583 [View Article]
     [Google Scholar]
  20. Schloss PD, Girard RA, Martin T, Edwards J, Thrash JC. Status of the archaeal and bacterial census: an update. mBio 2016; 7:e00201–00216 [View Article]
     [Google Scholar]
  21. Stenfors Arnesen LP, Fagerlund A, Granum PE. From soil to gut: Bacillus cereus and its food poisoning toxins. FEMS Microbiol Rev 2008; 32:579–606 [View Article]
     [Google Scholar]
  22. Chater KF. Recent advances in understanding Streptomyces . F1000Res 2016; 5:2795 [View Article]
     [Google Scholar]
  23. Grady EN, MacDonald J, Liu L, Richman A, Yuan Z-C. Current knowledge and perspectives of Paenibacillus: a review. Microb Cell Fact 2016; 15:203 [View Article]
     [Google Scholar]
  24. Guo Y, Zheng W, Rong X, Huang Y. A multilocus phylogeny of the Streptomyces griseus 16S rRNA gene clade: use of multilocus sequence analysis for streptomycete systematics. Int J Syst Evol Microbiol 2008; 58:149–159 [View Article]
     [Google Scholar]
  25. Castanheira NL, Dourado AC, Pais I, Semedo J, Scotti-Campos P et al. Colonization and beneficial effects on annual ryegrass by mixed inoculation with plant growth promoting bacteria. Microbiol Res 2017; 198:47–55 [View Article]
     [Google Scholar]
  26. Liu D, Yang Q, Ge K, Hu X, Qi G et al. Promotion of iron nutrition and growth on peanut by Paenibacillus illinoisensis and Bacillus sp. strains in calcareous soil. Braz J Microbiol 2017; 48:656–670 [View Article]
     [Google Scholar]
  27. Abdallah Y, Yang M, Zhang M, Masum MMI, Ogunyemi SO et al. Plant growth promotion and suppression of bacterial leaf blight in rice by Paenibacillus polymyxa Sx3. Lett Appl Microbiol 2019; 68:423429 [View Article]
     [Google Scholar]
  28. Labeda DP, Dunlap CA, Rong X, Huang Y, Doroghazi JR et al. Phylogenetic relationships in the family Streptomycetaceae using multi-locus sequence analysis. Antonie van Leeuwenhoek 2017; 110:563–583 [View Article]
     [Google Scholar]
  29. Nouioui I, Carro L, García-López M, Meier-Kolthoff JP, Woyke T et al. Genome-based taxonomic classification of the phylum Actinobacteria . Front Microbiol 2018; 9:2007 [View Article]
     [Google Scholar]
  30. Wiegel J. Distinction between the gram reaction and the gram type bacteria. Int J Syst Bacteriol 1981; 88:
     [Google Scholar]
  31. Delgado-Baquerizo M, Oliverio AM, Brewer TE, Benavent-González A, Eldridge DJ et al. A global atlas of the dominant bacteria found in soil. Science 2018; 359:320–325 [View Article]
     [Google Scholar]
  32. Hwang CY, Cho BC. Ponticoccus litoralis gen. nov., sp. nov., a marine bacterium in the family Rhodobacteraceae. Int J Syst Evol Microbiol 2008; 58:1332–1338 [View Article]
     [Google Scholar]
  33. Lee DW, Lee SD. Ponticoccus gilvus gen. nov., sp. nov., a novel member of the family Propionibacteriaceae from seawater. J Microbiol 2008; 46:508–512 [View Article]
     [Google Scholar]
  34. Tindall BJ, Kämpfer P, Euzéby JP, Oren A. Valid publication of names of prokaryotes according to the rules of nomenclature: past history and current practice. Int J Syst Evol Microbiol 2006; 56:2715–2720 [View Article]
     [Google Scholar]
  35. Oren A, Garrity GM, Parte AC. Why are so many effectively published names of prokaryotic taxa never validated?. Int J Syst Evol Microbiol 2018; 68:2125–2129 [View Article]
     [Google Scholar]
  36. Sutcliffe IC, Trujillo ME, Goodfellow M. A call to arms for systematists: revitalising the purpose and practises underpinning the description of novel microbial taxa. Antonie van Leeuwenhoek 2012; 101:13–20 [View Article]
     [Google Scholar]
  37. Vandamme P, Pot B, Gillis M, de Vos P, Kersters K et al. Polyphasic taxonomy, a consensus approach to bacterial systematics. Microbiol Rev 1996; 60:407–438 [View Article]
     [Google Scholar]
  38. Hayashi Sant'Anna F, Bach E, Porto RZ, Guella F, Hayashi Sant'Anna E et al. Genomic metrics made easy: what to do and where to go in the new era of bacterial taxonomy. Crit Rev Microbiol 2019; 45:182–200 [View Article]
     [Google Scholar]
  39. Goris J, Konstantinidis KT, Klappenbach JA, Coenye T, Vandamme P et al. DNA-DNA hybridization values and their relationship to whole-genome sequence similarities. Int J Syst Evol Microbiol 2007; 57:8191 [View Article]
     [Google Scholar]
  40. Meier-Kolthoff JP, Auch AF, Klenk H-P, Göker M. Genome sequence-based species delimitation with confidence intervals and improved distance functions. BMC Bioinformatics 2013; 14:60 [View Article]
     [Google Scholar]
  41. Lee I, Ouk Kim Y, Park S-C, Chun J. OrthoANI: an improved algorithm and software for calculating average nucleotide identity. Int J Syst Evol Microbiol 2016; 66:11001103 [View Article]
     [Google Scholar]
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Systematic review of descriptions of novel bacterial species: evaluation of the twenty-first century taxonomy through text mining
Int J Syst Evol Microbiol 70, 2925 (2020); https://doi.org/10.1099/ijsem.0.004070
/content/journal/ijsem/10.1099/ijsem.0.004070
/content/journal/ijsem/10.1099/ijsem.0.004070
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