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The analysis of microbiome data from biased high-throughput sequencing
Amy Willis, University of Washington, USA
Abstract: The composition of a microbiome is an important parameter to estimate given the critical role that microbiomes play in human and environmental health. However, profiling the composition of a microbial community using high throughput sequencing distorts the true composition of the community. Sequencing mock communities -- artificially constructed microbiomes of known composition -- clearly illustrates that observed composition is a biased estimate of true composition, with certain taxa consistently overobserved or underobserved compared to their true relative abundance. We propose a statistical model for bias in compositional data, illustrating its performance on data from the Vaginal Microbiome Consortium, and illustrate the effect of compositional bias on the replicability of human microbiome studies using data from the Microbiome Quality Control Project. We conclude with recommendations for the design and analysis of microbiome studies.
Assembling and modelling complex microbiomes mediating host-pathogen interactions
Niranjan Nagarajan, Genome Institute of Singapore and National University of Singapore, Singapore
Abstract: Human and environmental microbial communities mediating host-pathogen interactions often have complex genetic architectures and dynamics. Unravelling these needs new approaches for metagenome assembly at the strain level and microbiome modelling from limited relative abundance profiles. We propose a hybrid assembly framework that leverages long read sequencing to generate high-quality, near-complete strain genomes from complex metagenomes (OPERA-MS ). Applying this approach to human and environmental communities enabled recovery of 100s of novel genomes, plasmid and phage sequences, direct analysis of transmission patterns and investigation of antibiotic resistance gene combinations [1, 2]. Furthermore, we show how microbial community dynamics can be modelled accurately from sparse relative abundance data (BEEM ), providing insights into pathogen-commensal interactions in skin dermotypes. Data from several studies tracking the transmission of multi-drug resistant pathogens across environmental and human microbiomes will be used to illustrate the utility of these methods.
 Bertrand et al. “Hybrid metagenomic assembly enables high-resolution analysis of resistance determinants and mobile elements in human microbiomes." 2019 Nature Biotechnology 37 (8), 937-944