A new study published by researchers from the CUNY Institute for Implementation Science in Population Health (ISPH) at CUNY SPH and colleagues presents BugSigDB, a community-editable database of manually curated microbial signatures from published studies.
The database records essential methods and results to enable high-throughput analysis of similarity of microbial signatures identified by independent studies, of co-occurrence and co-exclusion of individual microbes and of consensus signatures conserved across multiple studies of similar health outcomes and exposures. It allows assessment of microbiome differential abundance within and across experimental conditions, environments or body sites.
First author Ludwig Geistlinger started the project as a postdoctoral student at CUNY SPH. He is now associate director of computational biology at the Center for Computational Biomedicine at Harvard Medical School. "BugSigDB is the first comprehensive collection of published microbial signatures that can be used to compare host-associated differential microbial abundance across independent studies," says Dr. Geistlinger."It helped us to uncover reproducible patterns of differential microbial abundance within and across health outcomes that we couldn't notice from just reading the published literature without standardizing it.
"Having the opportunity to work with and mentor the BugSigDB interns—many of whom were CUNY SPH students—has been truly amazing," says recent CUNY SPH doctoral graduate Chloe Mirzayi, the study's second author."Since the project started, I have gotten to see these bright and motivated students contribute to BugSigDB and grow as researchers as they have developed skills in critically reading literature, interpreting study results and performing secondary data analysis.
"This is the most significant project I've ever undertaken," says Professor Levi Waldron, the study's senior author."It's the product of four years of work by nearly 60
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