Microbiome Analysis: State of the Art and Future Trends

Mitch Fernandez, Vanessa Aguiar-Pulido, Juan Riveros, Wenrui Huang, Jonathan Segal, Erliang Zeng, Michael A Campos, Kalai Mathee, Giri Narasimhan

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

The Human Microbiome Project (HMP) focuses on the study of microbial communities that inhabit the healthy human body. Human microbiome studies have revealed that diseases and disorders are strongly correlated with changes in microbial community profiles. Molecular approaches to microbiome studies involve extracting microbial DNA from a sample followed by a process of determining the profile of the microbial community present. Exploiting sequence heterogeneity is clearly a more informative approach than using length heterogeneity. More recent methods involve the use of next-generation sequencing. A large number of open-source analytical tools for metagenomics have emerged in recent years. These include MG-RAST, MOTHUR, QIIME, CloVR, VAMPS, and others. Metagenomic studies require an efficient PCR amplification of the DNA. A study has revealed that the gene content of the bacterial community is more constant than the phylogenetic content. Bacterial communities are intricate collections of bacterial species that each provide functions which contribute to the stability of the community.

Original languageEnglish (US)
Title of host publicationComputational Methods for Next Generation Sequencing Data Analysis
Publisherwiley
Pages401-424
Number of pages24
ISBN (Electronic)9781119272182
ISBN (Print)9781118169483
DOIs
StatePublished - Sep 6 2016

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Keywords

  • Bacterial communities
  • Human Microbiome Project
  • Microbial communities
  • Open-source analytical tools
  • PCR amplification
  • Sequence heterogeneity

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Fernandez, M., Aguiar-Pulido, V., Riveros, J., Huang, W., Segal, J., Zeng, E., Campos, M. A., Mathee, K., & Narasimhan, G. (2016). Microbiome Analysis: State of the Art and Future Trends. In Computational Methods for Next Generation Sequencing Data Analysis (pp. 401-424). wiley. https://doi.org/10.1002/9781119272182.ch18