Page 11 - MDC Abstract Book & Guide

Abstract
As vast amounts of high-throughput data are generated from sequencing the microbiome, suitable analysis approaches are needed to effectively evaluate the data and reach robust conclusions to power studies incorporating microbiome research questions. Sequencing data are often represented as parts of the total sequencing effort, and therefore retain relative information to the other parts of the whole sequenced population. For microbiome data, sequenced reads are often assigned to annotations or are binned by sequence similarity, and the measurement of one taxon or gene is influenced by the other components measured. Due to this compositional nature of the data, common statistical analyses can produce misleading interpretations, and so compositionally- aware methodologies have emerged. We have adapted a compositional analysis framework into the evaluation and support of data generated in microbiome studies, and show applications of these approaches for evaluating product performance, and deriving insights from diverse sample types. We show these approaches, along with measures of biological variability and effect, can generate reliable conclusions from challenging conditions such as low biomass, rare taxa, or highly variable and sparse data.
Nikos Kyrpides Joint Genome Institute
Nikos Kyrpides received his PhD in Molecular Biology from University of Crete and pursued his postdoctoral studies with Carl Woese at University of Illinois in Urbana-Champaign. Since 2004 he is at the Joint Genome Institute where he leads the Prokaryotic Super Program. His work is focusing on Microbiome Research with an emphasis on Microbiome Data Science. He has published more than 600 research papers, has received several prestigious awards, he is an elected fellow of the American Academy of Microbiology and he is in the list of the world’s most highly cited scientists since 2014.
Presentation Title
Microbiome Data Science: From Products to Data
Abstract
Microbiome research is rapidly transitioning into Data Science. The unprecedented volume of microbiome data being generated pose significant challenges with respect to standards and management strategies, but also bear great new opportunities that can fuel discovery. Computational analysis of microbiome samples involving previously uncultured organisms, is currently advancing our understanding of the structure and function of entire microbial communities and expanding our knowledge of genetic and functional diversity of individual micro- organisms. I will describe some of our computational approaches and will emphasize the value of data processing integration in enabling the exploration of large metagenomic datasets and the discovery of novelty. I will discuss current approaches and success stories for the discovery of novel phylogenetic lineages as well as the exploration of the viral dark matter.