1887

Abstract

Microbial organisms have diverse populations, where using a single linear reference sequence in comparative studies introduces reference-bias in downstream analyses, and leads to a failure to account for variability in the population. Recently, pan-genome graphs have emerged as an alternative to the traditional linear reference with many successful applications and a rapid increase in the number of methods available in the literature. Despite this enthusiasm, there has been no attempt at exploring these graph construction methods in depth, demonstrating their practical use. In this study, we aim to develop a general guide to help researchers who may want to incorporate pan-genomes in their analyses of microbial organisms. We evaluated the state-of-the art pan-genome construction tools to model a collection of 70 strains. Our results suggest that all tools produced pan-genome graphs conforming to our expectations based on previous literature, and that their approach to homologue detection is likely to be the most influential in determining the final size and complexity of the pan-genome. The graphs overlapped most in the core pan-genome content while the cloud genes varied significantly among tools. We propose an alternative approach for pan-genome construction by combining two of the tools, Panaroo and Ptolemy, to further exploit them in downstream analyses, and demonstrate the effectiveness of our pipeline for structural variant calling in beta-lactam resistance genes in the same set of isolates, identifying various transposon structures for carbapenem resistance in chromosome, as well as plasmids. We identify a novel plasmid structure in two multidrug-resistant clinical isolates that had previously been studied, and which could be important for their resistance phenotypes.

  • This is an open-access article distributed under the terms of the Creative Commons Attribution NonCommercial License. This article was made open access via a Publish and Read agreement between the Microbiology Society and the corresponding author’s institution.
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2021-11-11
2024-04-29
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References

  1. Yakkala H, Samantarrai D, Gribskov M, Siddavattam D. Comparative genome analysis reveals niche-specific genome expansion in Acinetobacter baumannii strains. PLOS ONE 2019; 14:e0218204 [View Article] [PubMed]
    [Google Scholar]
  2. National Center for Biotechnology Information (NCBI) Bethesda (MD): National Library of Medicine (US), National Center for Biotechnology Information; 1988 https://www.ncbi.nlm.nih.gov/
  3. Yang X, Lee W-P, Ye K, Lee C. One reference genome is not enough. Genome Biol 2019; 20:104
    [Google Scholar]
  4. Marschall T, Marz M, Abeel T, Dijkstra L, Dutilh BE et al. Computational pan-genomics: status, promises and challenges. Brief Bioinformatics 2018; 19:118–135
    [Google Scholar]
  5. Dilthey A, Cox C, Iqbal Z, Nelson MR, McVean G. Improved genome inference in the MHC using a population reference graph. Nat Genet 2015; 47:682–688 [View Article] [PubMed]
    [Google Scholar]
  6. Garrison E, Sirén J, Novak AM, Hickey G, Eizenga JM et al. Variation graph toolkit improves read mapping by representing genetic variation in the reference. Nat Biotechnol 2018; 36:875–879 [View Article] [PubMed]
    [Google Scholar]
  7. Biederstedt E, Oliver JC, Hansen NF, Jajoo A, Dunn N et al. NovoGraph: Human genome graph construction from multiple long-read de novo assemblies. F1000Res 2018; 7:1391 [View Article] [PubMed]
    [Google Scholar]
  8. Li H, Feng X, Chu C. The design and construction of reference pangenome graphs with minigraph. Genome Biol 2020; 21:265 [View Article]
    [Google Scholar]
  9. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol 1990; 215:403–410 [View Article] [PubMed]
    [Google Scholar]
  10. Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods 2014; 12:59–60 [View Article] [PubMed]
    [Google Scholar]
  11. Fu L, Niu B, Zhu Z, Wu S, Li W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 2012; 28:3150–3152 [View Article] [PubMed]
    [Google Scholar]
  12. Enright AJ. An efficient algorithm for large-scale detection of protein families. Nucleic Acids Res 2002; 30:1575–1584 [View Article] [PubMed]
    [Google Scholar]
  13. Steinegger M, Söding J. Clustering huge protein sequence sets in linear time. Nat Commun 2018; 9:1–8 [View Article]
    [Google Scholar]
  14. Zarrilli R, Pournaras S, Giannouli M, Tsakris A. Global evolution of multidrug-resistant Acinetobacter baumannii clonal lineages. Int J Antimicrob Agents 2013; 41:11–19S0924-8579(12)00373-1 [View Article] [PubMed]
    [Google Scholar]
  15. Salto IP, Torres Tejerizo G, Wibberg D, Pühler A, Schlüter A et al. Comparative genomic analysis of Acinetobacter spp. plasmids originating from clinical settings and environmental habitats. Sci Rep 2018; 8:1–12 [View Article]
    [Google Scholar]
  16. Tonkin-Hill G, MacAlasdair N, Ruis C, Weimann A, Horesh G et al. Producing polished prokaryotic pangenomes with the panaroo pipeline. bioRxiv 20202020.01.28.922989
    [Google Scholar]
  17. Page AJ, Cummins CA, Hunt M, Wong VK, Reuter S et al. Roary: rapid large-scale prokaryote pan genome analysis. Bioinformatics 2015; 31:3691–3693 [View Article] [PubMed]
    [Google Scholar]
  18. Salazar AN, Abeel T. Approximate, simultaneous comparison of microbial genome architectures via syntenic anchoring of quiver representations. Bioinformatics 2018; 34:i732–42 [View Article] [PubMed]
    [Google Scholar]
  19. Gautreau G, Bazin A, Gachet M, Planel R, Burlot L et al. PPanGGOLiN: Depicting microbial diversity via a Partitioned Pangenome Graph. bioRxiv 2019836239
    [Google Scholar]
  20. Bayliss SC, Thorpe HA, Coyle NM, Sheppard SK, Feil EJ. PIRATE: A fast and scalable pangenomics toolbox for clustering diverged orthologues in bacteria. GigaScience 2019; 8:598391 [View Article] [PubMed]
    [Google Scholar]
  21. Li H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 2018; 34:3094–3100 [View Article] [PubMed]
    [Google Scholar]
  22. Contreras-Moreira B, Vinuesa P. GET_HOMOLOGUES, a versatile software package for scalable and robust microbial pangenome analysis. Appl Environ Microbiol 2013; 79:7696–7701 [View Article] [PubMed]
    [Google Scholar]
  23. Klopfenstein D, Zhang L, Pedersen BS, Ramírez F, Vesztrocy AW et al. GOATOOLS: A Python library for Gene Ontology analyses. Sci Rep 2018; 8:1–17 [View Article]
    [Google Scholar]
  24. Carbon S, Mungall C. Gene ontology data archive. Dataset on Zenodo 2018
    [Google Scholar]
  25. The UniProt Consortium UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Res 2021; 49:D480–9 [View Article] [PubMed]
    [Google Scholar]
  26. Wick RR, Schultz MB, Zobel J, Holt KE. Bandage: Interactive visualization of de novo genome assemblies. Bioinformatics 2015; 31:3350–3352 [View Article] [PubMed]
    [Google Scholar]
  27. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT et al. Cytoscape: A software Environment for integrated models of biomolecular interaction networks. Genome Res 2003; 13:2498–2504 [View Article] [PubMed]
    [Google Scholar]
  28. Davis JJ, Wattam AR, Aziz RK, Brettin T, Butler R et al. The PATRIC Bioinformatics Resource Center: Expanding data and analysis capabilities. Nucleic Acids Res 2020; 48:D606–D612 [View Article] [PubMed]
    [Google Scholar]
  29. Georgescu CH, Manson AL, Griggs AD, Desjardins CA, Pironti A et al. SynerClust: a highly scalable, synteny-aware orthologue clustering tool. Microb Genom 2018; 4: [View Article] [PubMed]
    [Google Scholar]
  30. Zhao Y, Wu J, Yang J, Sun S, Xiao J et al. PGAP: Pan-genomes analysis pipeline. Bioinformatics 2012; 28:416–418 [View Article] [PubMed]
    [Google Scholar]
  31. Zhou Z, Charlesworth J, Achtman M. Accurate reconstruction of bacterial pan- and core genomes with PEPPAN. Genome Res 2020; 30:1667–1679 [View Article] [PubMed]
    [Google Scholar]
  32. Mangas EL, Rubio A, Álvarez-Marín R, Labrador-Herrera G, Pachón J et al. Pangenome of Acinetobacter baumannii uncovers two groups of genomes, one of them with genes involved in CRISPR/Cas defence systems associated with the absence of plasmids and exclusive genes for biofilm formation. Microb Genom 2019; 5:e000309 [View Article] [PubMed]
    [Google Scholar]
  33. Galac MR, Snesrud E, Lebreton F, Stam J, Julius M et al. A diverse panel of clinical Acinetobacter baumannii for research and development. Antimicrob Agents Chemother (Bethesda) 2020; 64: [View Article]
    [Google Scholar]
  34. Chan AP, Sutton G, DePew J, Krishnakumar R, Choi Y et al. A novel method of consensus pan-chromosome assembly and large-scale comparative analysis reveal the highly flexible pan-genome of Acinetobacter baumannii. Genome Biol 2015; 16:143 [View Article] [PubMed]
    [Google Scholar]
  35. Costa SS, Guimarães LC, Silva A, Soares SC, Baraúna RA. First Steps in the Analysis of Prokaryotic Pan-Genomes. Bioinform Biol Insights 2020; 14:117793222093806 [View Article] [PubMed]
    [Google Scholar]
  36. Chan AP, Sutton G, DePew J, Krishnakumar R, Choi Y et al. A novel method of consensus pan-chromosome assembly and large-scale comparative analysis reveal the highly flexible pan-genome of Acinetobacter baumannii. Genome Biol 2015; 16:143 [View Article] [PubMed]
    [Google Scholar]
  37. Antunes LCS, Visca P, Towner KJ. Acinetobacter baumannii: evolution of a global pathogen. Pathog Dis 2014; 71:292–301 [View Article] [PubMed]
    [Google Scholar]
  38. Poirel L, Figueiredo S, Cattoir V, Carattoli A, Nordmann P. Acinetobacter radioresistens as a silent source of carbapenem resistance for Acinetobacter spp. Antimicrob Agents Chemother (Bethesda) 2008; 52:1252–1256 [View Article]
    [Google Scholar]
  39. Segal H, Jacobson RK, Garny S, Bamford CM, Elisha BG. Extended -10 promoter in ISAba-1 upstream of blaOXA-23 from Acinetobacter baumannii [3]. Antimicrob Agents Chemother (Bethesda) 2007; 51:3040–3041 [View Article]
    [Google Scholar]
  40. Héritier C, Poirel L, Lambert T, Nordmann P. Contribution of acquired carbapenem-hydrolyzing oxacillinases to carbapenem resistance in Acinetobacter baumannii. Antimicrob Agents Chemother (Bethesda) 2005; 49:3198–3202 [View Article]
    [Google Scholar]
  41. Nigro SJ, Hall RM. Structure and context of acinetobacter transposons carrying the oxa23 carbapenemase gene. J Antimicrob Chemother 2016; 71:1135–1147 [View Article] [PubMed]
    [Google Scholar]
  42. Blackwell GA, Hall RM. Mobilisation of a small Acinetobacter plasmid carrying an oriT transfer origin by conjugative RepAci6 plasmids. Plasmid 2019; 103:36–44 [View Article] [PubMed]
    [Google Scholar]
  43. Chen Y, Gao J, Zhang H, Ying C. Spread of the blaOXA-23-containing Tn2008 in carbapenem-resistant Acinetobacter baumannii isolates Grouped in CC92 from China. Front Microbiol 2017; 8:163
    [Google Scholar]
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