Abstract: Shotgun metagenomic sequencing allows us to directly observe the functional genes present in a microbial community. Although the cost of metagenomic sequencing has declined dramatically in recent years, it is still impractical to apply shotgun metagenomics to routine studies involving hundreds to thousands of samples. Metagenomic analysis is also limited by sequencing depth, with genes associated with rare members of the community easily missed. Because microbial functions are often correlated with taxonomy it is possible to supplement metagenomic studies with functional predictions based on taxonomic marker genes such as the 16S rRNA gene. In 2015 we introduced paprica, a tool to analyze microbial community structure and predict the associated metabolic potential. Based on the completed genomes available in the RefSeq database, paprica represents a model of gene distribution in a phylogenetic context. Phylogenetic placement techniques allow us to place amplicon sequence reads on a high-quality reference tree constructed from completed genomes and assign genes and genomic characteristics (e.g., GC content, genome length, 16S rRNA gene copy number). Continued development of paprica has improved the fidelity of these predictions and includes the use of multi-gene alignments for the reference tree and nested reference trees to reduce the number of taxa in each tree. Here I’ll describe the paprica pipeline in greater detail, including extensions for the annotation of metagenomic and metatranscriptomic data. I’ll present new results comparing paprica predictions to completed genomes not contained in RefSeq, and case studies illustrating how we use paprica to improve our ecological interpretations.

Bio: Jeff Bowman is a biological oceanographer and microbial ecologist at Scripps Institution of Oceanography at UC San Diego. His lab is broadly interested in the links between microbial community structure and function and biogeochemical cycles. Current focus areas include mangrove-microbe symbiosis, coastal time-series analysis, astrobiology, and high latitude ecosystem processes.