Poster Presentation Society for Molecular Biology and Evolution Conference 2016

Inferring symbiotic innovation through a shotgun metagenomics lens: progress and pitfalls of the latest techniques (#562)

Amanda M.V. Brown 1 , Dana K. Howe 1 , Dee R. Denver 1
  1. Oregon State University, Corvallis, OR, United States

Genome and transcriptome sequencing provide a window into the world of beneficial interactions between unculturable microbes and their hosts. Yet, we still face major roadblocks when trying to extract meaning from this mass of data. Missassembly and a large proportion of uncharacterized genes are some of the biggest challenges. These problems are especially common in host-associated symbionts sampled from nature, where divergent strains may exist. Researchers must often choose between expensive high-coverage low-sample sequencing, shallow-coverage limited-diversity sequencing, or smaller-target (fewer loci) sequencing approaches. Using data from several microbial communities associated with below- and above-ground plant pests (specifically, plant-parasitic nematodes and sap-feeding insects), we explored several new approaches to gather meaningful insights into symbiosis from shotgun genome and transcriptome data. Results show larger library inserts and longer reads increase coverage evenness to offset the cost in coverage magnitude, helping with both assembly fragmentation and chimeric assembly, but have limited benefit for untangling strains. Combatting strain diversity by two opposite methods, collapse versus separation, illuminates the need for a sophisticated clustering algorithm to bin strains meaningfully before functional analysis. Finally, one of the most promising facets of this study was the benefit of a comparative approach using shared gene sets and pangenomes to better-handle the proportion of shared, conserved, and sometimes co-expressed but functionally unannotated genes. In summary, we outline a promising, simplified series of steps to bridge some of the formidable rifts in shotgun metagenomics for host-associated microbes.