4.3 Article

Microbial taxonomy in the era of OMICS: application of DNA sequences, computational tools and techniques

Publisher

SPRINGER
DOI: 10.1007/s10482-017-0928-1

Keywords

Bacterial taxonomy; DNA-DNA hybridization; 16S rRNA gene; Genomics; Metagenomics

Categories

Funding

  1. Government of India under project from Department of Biotechnology (DBT)
  2. National Bureau of Agriculturally Important Microorganisms-Indian Council of Agricultural Research (NBAIM-ICAR)
  3. University of Delhi Research and Development (DU-RD) Grant
  4. ICAR
  5. DU-DST Promotion of University Research and Scientific Excellence (PURSE)
  6. Council of Scientific and Industrial Research
  7. University Grants Commission (UGC)
  8. UGC-DSK PDF
  9. DBT
  10. NBAIM
  11. DU
  12. Indian Council of Medical Research

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The current prokaryotic taxonomy classifies phenotypically and genotypically diverse microorganisms using a polyphasic approach. With advances in the next-generation sequencing technologies and computational tools for analysis of genomes, the traditional polyphasic method is complemented with genomic data to delineate and classify bacterial genera and species as an alternative to cumbersome and error-prone laboratory tests. This review discusses the applications of sequence-based tools and techniques for bacterial classification and provides a scheme for more robust and reproducible bacterial classification based on genomic data. The present review highlights promising tools and techniques such as ortho-Average Nucleotide Identity, Genome to Genome Distance Calculator and Multi Locus Sequence Analysis, which can be validly employed for characterizing novel microorganisms and assessing phylogenetic relationships. In addition, the review discusses the possibility of employing metagenomic data to assess the phylogenetic associations of uncultured microorganisms. Through this article, we present a review of genomic approaches that can be included in the scheme of taxonomy of bacteria and archaea based on computational and in silico advances to boost the credibility of taxonomic classification in this genomic era.

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