A Primer on Microbial Bioinformatics for Nonbioinformaticians

Clin Microbiol Infect. 2018 Apr;24(4):342-349. doi: 10.1016/j.cmi.2017.12.015. Epub 2018 Jan 5.

Abstract

Background: Presently, the bottleneck in the deployment of high-throughput sequencing technology is the ability to analyse the increasing amount of data produced in a fit-for-purpose manner. The field of microbial bioinformatics is thriving and quickly adapting to technological changes, which creates difficulties for nonbioinformaticians in following the complexity and increasingly obscure jargon of this field.

Aims: This review is directed towards nonbioinformaticians who wish to gain understanding of the overall microbial bioinformatic processes, from raw data obtained from sequencers to final outputs.

Sources: The software and analytical strategies reviewed are based on the personal experience of the authors.

Content: The bioinformatic processes of transforming raw reads to actionable information in a clinical and epidemiologic context is explained. We review the advantages and limitations of two major strategies currently applied: read mapping, which is the comparison with a predefined reference genome, and de novo assembly, which is the unguided assembly of the raw data. Finally, we discuss the main analytical methodologies and the most frequently used freely available software and its application in the context of bacterial infectious disease management.

Implications: High-throughput sequencing technologies are overhauling outbreak investigation and epidemiologic surveillance while creating new challenges due to the amount and complexity of data generated. The continuously evolving field of microbial bioinformatics is required for stakeholders to fully harness the power of these new technologies.

Keywords: Bioinformatics software; Genomic epidemiology; High-throughput sequencing; Microbial bioinformatics; Microbial typing.

Publication types

  • Review

MeSH terms

  • Computational Biology / methods*
  • Humans
  • Microbiological Techniques / methods*
  • Molecular Epidemiology / methods*
  • Sequence Analysis, DNA / methods*