SEP 02, 2015 10:30 AM PDT

Control Experiments for Reproducible Microbiome Research

Presented At Microbiology
  • Associate Professor, Department of Statistical Sciences and Operations, Research Department of Supply Chain Management and Analytics, Virginia Commonwealth University
      Paul Brooks is the Statistics and Analysis Team Leader for the NIH-funded Multi-omic Microbiome Study-Pregnancy Initiative (MOMS-PI, and is a fellow of the Center for the Study of Biological Complexity at Virginia Commonwealth University (VCU). He recently returned to VCU after serving as a visiting research fellow at the Statistical and Applied Mathematical Sciences Institute (SAMSI) where he led working groups in developing methods for analyzing microbiome data as part of the Beyond Bioinformatics program. He received a Ph.D. in operations research from Georgia Tech, and in his research has used optimization to design new data analysis methods and has applied them to biomedical data. Recently, he has worked to bring perspectives from industrial statistics to bear on interpretation of life sciences data.


    Rapid advances in sequencing technology and spectroscopy have led to new measurements of the microbiome. Labs make choices based on their environment of interest and the questions they wish to answer. Understanding the effects of these choices has been under-appreciated, with an implicit assumption that further advances in technology will address all of the current shortcomings. Positive and negative control experiments can provide insight into technical variation, bias, and contamination. They can help investigators make quantitative and qualitative adjustments to data and enhance interpretation. Understanding these aspects is crucial to detecting true signal from noise. In this presentation, we discuss how to design control experiments and leverage information from them for the interpretation of environmental samples. Learning Objectives 1. To understand how analysis of mock communities can provide insight into technical variation, bias, and contamination in microbiome measurements. 2. To learn steps that labs conducting microbiome research can take to understand the effects of their choice of protocols.

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