Lozupone, C. | bioRxiv Science

bioRxiv2018

AMON: Annotation of metabolite origins via networks to better integrate microbiome and metabolome data

MotivationUntargeted metabolomics of host-associated samples has yielded insights into mechanisms by which microbes modulate health. However, data interpretation is challenged by the complexity of origins of the small molecules measured, which can come from the host, microbes that live with the host, or from other exposures such as diet or the environment.\n\nResultsWe address this challenge through development of AMON: Annotation of Metabolite Origins via Networks. AMON is an open-source bioinformatics application that can be used to determine the degree to which annotated compounds in the metabolome may have been produced by bacteria present, the host, either (i.e. both the bacteria and host are capable of production), or neither (i.e. neither the human or the fecal microbiome are predicted to be capable of producing the observed metabolite).\n\nAvailability and ImplementationThis software is available at https://github.com/lozuponelab/AMON as well as via pip.\n\nContactcatherine.lozupone@ucdenver.edu

bioinformatics↗

bioRxiv2018

VOLARE: Visual analysis of disease-associated microbiome-immune system interplay

BackgroundRelationships between specific microbes and proper immune system development, composition, and function have been reported in a number of studies. However, researchers have discovered only a fraction of the likely relationships. High-dimensional \"omic\" methodologies such as 16S ribosomal RNA (rRNA) sequencing and Time-of-flight mass cytometry (CyTOF) immunophenotyping generate data that support generation of hypotheses, with the potential to identify additional relationships at a level of granularity ripe for further experimentation. Pairwise linear regressions between microbial and host immune features is one approach for quantifying relationships between \"omes\", and the differences in these relationships across study cohorts or arms. This approach yields a top table of candidate results. However, the top table alone lacks the detail that domain experts need to vet candidate results for follow-up experiments.\n\nResultsTo support this vetting, we developed VOLARE (Visualization Of LineAr Regression Elements), a web application that integrates a searchable top table, small in-line graphs illustrating the fitted models, a network summarizing the top table, and on-demand detailed regression plots showing full sample-level detail. We applied VOLARE to three case studies--microbiome:cytokine data from fecal samples in HIV, microbiome:cytokine data in inflammatory bowel disease and spondyloarthritis, and microbiome:immune cell data from gut biopsies in HIV. We present both patient-specific phenomena and relationships that differ by disease state. We also analyzed interaction data from system logs to characterize usage scenarios. This log analysis revealed that, in using VOLARE, domain experts frequently generated detailed regression plots, suggesting that this detail aids the vetting of results.\n\nConclusionsSystematically integrating microbe:immune cell readouts through pairwise linear regressions and presenting the top table in an interactive environment supports the vetting of results for scientific relevance. VOLARE allows domain experts to control the analysis of their results, screening dozens of candidate relationships with ease. This interactive environment transcends the limitations of a static top table.

bioinformatics↗