Achieving pan-microbiome biological insights via the dbBact knowledge base

Author:

Shental Noam1

Affiliation:

1. Open University of Israel

Abstract

Microbiome 16S rRNA studies have examined diverse habitats, including oceans, soil, plants, animals, and large cross-sectional human studies. However, finding commonalities between different experiments, habitats, or conditions is very difficult due to technical issues related to using different primer pairs and relying on taxonomy for scientific reporting. Hence, grosso modo, the 16S rRNA microbiome literature has not been integrated into a collaborative body of knowledge. It often comprises effectively isolated studies, where researchers may be completely unaware of commonalities between their findings and those in other studies, especially across different niches or habitats. In the past five years, we have created dbBact (www.dbbact.org), a knowledge base, to bridge this gap and lay the groundwork for gaining core pan-microbiome insights. Briefly, amplicon sequence variants (ASVs) from datasets of published papers are manually analyzed, and bacterial sequences associated with experimental conditions are uploaded to dbBact, together with ontology-based characterizations (e.g., “the abundance of sequence ACTGGA… was higher in fecal samples of horses with colitis compared to healthy controls in California”). To date, dbBact contains data from more than 1000 published studies across diverse habitats, spanning  370,000 unique 16S rRNA ASVs, with approximately 1,500,000 sequence-to-phenotype associations. We demonstrated how dbBact provides many types of novel hypotheses that cannot be formulated by standard methods (Amir et al., Nucleic Acids Research, 2023). We will show how dbBact may be (1) applied as part of microbiome research in any niche; (2) allow a new type of meta-analysis; and (3) be harnessed to answer ecological questions. Link to publication: https://academic.oup.com/nar/article/51/13/6593/7199329?login=true

Publisher

Cassyni

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