Skip to content
1887

Abstract

The diversity of microbial insertion sequences, crucial mobile genetic elements in generating diversity in microbial genomes, needs to be better represented in current microbial databases. Identification of these sequences in microbiome communities presents some significant problems that have led to their underrepresentation. Here, we present a bioinformatics pipeline called Palidis that recognizes insertion sequences in metagenomic sequence data rapidly by identifying inverted terminal repeat regions from mixed microbial community genomes. Applying Palidis to 264 human metagenomes identifies 879 unique insertion sequences, with 519 being novel and not previously characterized. Querying this catalogue against a large database of isolate genomes reveals evidence of horizontal gene transfer events across bacterial classes. We will continue to apply this tool more widely, building the Insertion Sequence Catalogue, a valuable resource for researchers wishing to query their microbial genomes for insertion sequences.

  • This is an open-access article distributed under the terms of the Creative Commons Attribution License.
Loading

Article metrics loading...

/content/journal/mgen/10.1099/mgen.0.000917
2023-03-10
2025-04-30
Loading full text...

Full text loading...

/deliver/fulltext/mgen/9/3/mgen000917.html?itemId=/content/journal/mgen/10.1099/mgen.0.000917&mimeType=html&fmt=ahah

References

  1. Roberts AP, Mullany P. Tn916-like genetic elements: a diverse group of modular mobile elements conferring antibiotic resistance. FEMS Microbiol Rev 2011; 35:856–871 [View Article] [PubMed]
    [Google Scholar]
  2. Frost LS, Leplae R, Summers AO, Toussaint A. Mobile genetic elements: the agents of open source evolution. Nat Rev Microbiol 2005; 3:722–732 [View Article] [PubMed]
    [Google Scholar]
  3. Partridge SR, Kwong SM, Firth N, Jensen SO. Mobile genetic elements associated with antimicrobial resistance. Clin Microbiol Rev 2018; 31:e00088-17 [View Article]
    [Google Scholar]
  4. Mahillon J, Chandler M. Insertion sequences. Microbiol Mol Biol Rev 1998; 62:725–774 [View Article] [PubMed]
    [Google Scholar]
  5. Aziz RK, Breitbart M, Edwards RA. Transposases are the most abundant, most ubiquitous genes in nature. Nucleic Acids Res 2010; 38:4207–4217 [View Article] [PubMed]
    [Google Scholar]
  6. Tansirichaiya S, Mullany P, Roberts AP. PCR-based detection of composite transposons and translocatable units from oral metagenomic DNA. FEMS Microbiol Lett 2016; 363:fnw195 [View Article]
    [Google Scholar]
  7. Siguier P, Perochon J, Lestrade L, Mahillon J, Chandler M. ISfinder: the reference centre for bacterial insertion sequences. Nucleic Acids Res 2006; 34:D32–6 [View Article] [PubMed]
    [Google Scholar]
  8. Rice P, Longden I, Bleasby A. EMBOSS: the European Molecular Biology Open Software Suite. Trends Genet 2000; 16:276–277 [View Article]
    [Google Scholar]
  9. Kamoun C, Payen T, Hua-Van A, Filée J. Improving prokaryotic transposable elements identification using a combination of de novo and profile HMM methods. BMC Genomics 2013; 14:700 [View Article] [PubMed]
    [Google Scholar]
  10. Treangen TJ, Salzberg SL. Repetitive DNA and next-generation sequencing: computational challenges and solutions. Nat Rev Genet 2011; 13:36–46 [View Article] [PubMed]
    [Google Scholar]
  11. Brisaboa NR, Puglisi SJ. String Processing and Information Retrieval. In Processing S, Retrieval I. eds COBS: A Compact Bit-Sliced Signature Index Cham: Springer International Publishing; 2019 pp 285–303 [View Article]
    [Google Scholar]
  12. Blackwell GA, Hunt M, Malone KM, Lima L, Horesh G et al. Exploring bacterial diversity via a curated and searchable snapshot of archived DNA sequences. PLOS Biol 2021; 19:e3001421 [View Article]
    [Google Scholar]
  13. Khiste N, Ilie L. E-MEM: efficient computation of maximal exact matches for very large genomes. Bioinformatics 2015; 31:509–514 [View Article] [PubMed]
    [Google Scholar]
  14. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods 2012; 9:357–359 [View Article] [PubMed]
    [Google Scholar]
  15. 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]
  16. 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]
  17. Jones P, Binns D, Chang H-Y, Fraser M, Li W et al. InterProScan 5: genome-scale protein function classification. Bioinformatics 2014; 30:1236–1240 [View Article] [PubMed]
    [Google Scholar]
  18. Turnbaugh PJ, Ley RE, Hamady M, Fraser-Liggett CM, Knight R et al. The human microbiome project. Nature 2007; 449:804–810 [View Article] [PubMed]
    [Google Scholar]
  19. Carr VR, Witherden EA, Lee S, Shoaie S, Mullany P et al. Abundance and diversity of resistomes differ between healthy human oral cavities and gut. Nat Commun 2020; 11:693 [View Article] [PubMed]
    [Google Scholar]
/content/journal/mgen/10.1099/mgen.0.000917
Loading
/content/journal/mgen/10.1099/mgen.0.000917
Loading

Data & Media loading...

Supplements

Supplementary material 1

Supplementary material 2

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error