Digital Microbe: A Genome-Informed Data Integration Framework for Collaborative Research on Emerging Model Organisms

Author:

Veseli IvaORCID,Cooper Zachary S.,DeMers Michelle A.,Schechter Matthew S.ORCID,Miller Samuel,Weber Laura,Smith Christa B.,Rodriguez Lidimarie T.,Schroer William F.,McIlvin Matthew R.,Lopez Paloma Z.,Saito Makoto,Dyhrman Sonya,Eren A. MuratORCID,Moran Mary AnnORCID,Braakman Rogier

Abstract

AbstractThe remarkable pace of genomic data generation focused on the physiology and ecology of microbes is rapidly transforming our understanding of life at the micron scale. Yet this data stream has also created challenges for finding interoperable and extensible modes of analysis. From our own experience, a single microbe often has multiple versions of its genome architecture, functional gene annotation, and gene naming system, without a straightforward mechanism for collating information and preserving crucial advances in annotation. These dispersed data sources raise barriers to collaborations, and more generally hinder community coalescence around shared datasets of model organisms. Here, we describe the “Digital Microbe” data product which provides a framework for interoperability, reproducibility, and collaborative microbial science. A Digital Microbe is an open source, community-curated data package built on a (pan)genome foundation, which is housed within an integrative software environment. Using Digital Microbes ensures real-time alignment of research efforts within collaborative teams, and, as new layers of ’omic, experimental, or modeling data are added, facilitates the generation of novel scientific insights. We describe two Digital Microbes, one for the model heterotrophic marine bacteriumRuegeria pomeroyiDSS-3 which includes >100 transcriptomic datasets from lab and field studies; and another for the pangenome of the cosmopolitan heterotrophic marine bacterial genusAlteromonasrepresented by 339 genomes. Examples are provided to demonstrate how an integrated framework that collates public (pan)genome-informed data can generate novel and reproducible findings.

Publisher

Cold Spring Harbor Laboratory

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