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A conceptual framework to address challenges in applying standards to metadata collected in microbiome studies

Friday, September 16, 2016 — Poster Session IV

12:00 p.m. – 1:30 p.m.
FAES Terrace
NIAID
COMPBIO-14

Authors

  • M Quiñones
  • L Kim
  • D Liou
  • I Misner
  • C Shyu
  • N Weber
  • D Hurt

Abstract

Interest in the microbiome has skyrocketed in recent years. Researchers are not only characterizing microbial communities, but also understanding effects on their hosts to elucidate how the microbiota could be leveraged to improve health. This comes with challenges, such as the need for advanced analytics, integration of multiple 'omics, and reproducibility of results. The Human Microbiome (HMP) and microbiome quality control (MBQC) projects have navigated the establishment of standard protocols for data collection, which normalizes technical bias and allows for comparability of measurements. Use of standards from the Genomics Standards Consortium, especially the Minimal Information about a Marker Gene Sequence (MIMARKS), facilitates consistent use of variables and vocabulary terms. While standards are crucial for cross-study comparisons, many researchers find the current manual annotation process very cumbersome. In collaboration with the NIAID Microbiome Project, a Microbial Clinical Genomics System (mCGS) is being designed to address application of standardized metadata more easily and consistently. The mCGS consists of: a) user-friendly web table submission form for easy collection and validation of metadata that will be integrated with ontologies and include a diagram for easy retrieval of vocabulary terms; b) an engine for automation of data and metadata submission to NCBI’s SRA repository; and c) a portal to manage studies and analyze data using specific metadata variables. The ultimate goal is to build a system that will streamline data annotation, enable reproducibility of results, and facilitate comparative analyses between any microbiome datasets that are generated using the same standards.

Category: Computational Biology