Current state of and future opportunities for prediction in microbiome research: Report from the mid-atlantic microbiome meet-up in Baltimore on 9 january 2019

Eric Sakowski, Gherman Uritskiy, Rachel Cooper, Maya Gomes, Michael R. McLaren, Jacquelyn S. Meisel, Rebecca L. Mickol, C. David Mintz, Emmanuel F. Mongodin, Mihai Pop, Mohammad Arifur Rahman, Alvaro Sanchez, Winston Timp, Jeseth Delgado Vela, Carly Muletz Wolz, Joseph P. Zackular, Jessica Chopyk, Seth Commichaux, Meghan Davis, Douglas DluzenSukirth M. Ganesan, Muyideen Haruna, Dan Nasko, Mary J. Regan, Saul Sarria, Nidhi Shah, Brook Stacy, Dylan Taylor, Jocelyne DiRuggiero, Sarah P. Preheim

Research output: Contribution to journalReview articlepeer-review

Abstract

Accurate predictions across multiple fields of microbiome research have far-reaching benefits to society, but there are few widely accepted quantitative tools to make accurate predictions about microbial communities and their functions. More discussion is needed about the current state of microbiome analysis and the tools required to overcome the hurdles preventing development and implementation of predictive analyses. We summarize the ideas generated by participants of the Mid-Atlantic Microbiome Meet-up in January 2019. While it was clear from the presentations that most fields have advanced beyond simple associative and descriptive analyses, most fields lack essential elements needed for the development and application of accurate microbiome predictions. Participants stressed the need for standardization, reproducibility, and accessibility of quantitative tools as key to advancing predictions in microbiome analysis. We highlight hurdles that participants identified and propose directions for future efforts that will advance the use of prediction in microbiome research.

Original languageEnglish (US)
Article numbere00392
JournalmSystems
Volume4
Issue number5
DOIs
StatePublished - 2019

Keywords

  • Bioinformatics
  • Conceptual Models
  • Machine learning
  • Metagenomics
  • Microbiome
  • Prediction
  • Quantitative models

ASJC Scopus subject areas

  • Genetics
  • Ecology, Evolution, Behavior and Systematics
  • Molecular Biology
  • Physiology
  • Biochemistry
  • Computer Science Applications
  • Microbiology
  • Modeling and Simulation

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