16S rRNA amplicon sequencing provides a relatively inexpensive culture-independent method for studying microbial communities. Although thousands of such studies have examined diverse habitats, it is difficult for researchers to use this vast trove of experiments when interpreting their own findings in a broader context. To bridge this gap, we introduce dbBact – a novel pan-microbiome resource. dbBact combines manually curated information from studies across diverse habitats, creating a collaborative central repository of 16S rRNA amplicon sequence variants (ASVs), which are assigned multiple ontology-based terms. To date dbBact contains information from more than 1000 studies, which include 1500000 associations between 360000 ASVs and 6500 ontology terms. Importantly, dbBact offers a set of computational tools allowing users to easily query their own datasets against the database. To demonstrate how dbBact augments standard microbiome analysis we selected 16 published papers, and reanalyzed their data via dbBact. We uncovered novel inter-host similarities, potential intra-host sources of bacteria, commonalities across different diseases and lower host-specificity in disease-associated bacteria. We also demonstrate the ability to detect environmental sources, reagent-borne contaminants, and identify potential cross-sample contaminations. These analyses demonstrate how combining information across multiple studies and over diverse habitats leads to better understanding of underlying biological processes.

16S rRNA amplicon sequencing provides a relatively cheap culture-independent method for studying the microbial world. Thousands of studies have examined microbial populations in various habitats, yet a global pan-microbiome perspective integrating these studies is still missing. Here we introduce dbBact, an open wiki-like bacterial knowledge base that combines information from hundreds of studies across diverse habitats, creating a collaborative central repository amenable to subsequent analysis. 16S rRNA amplicon sequence variants (ASVs) are manually extracted from each study and assigned multiple ontology-based terms. dbBact currently contains about 850 studies, covering more than 1,300,000 associations between 315,000 ASVs and 3,500 ontology terms. We demonstrate how dbBact can lead to better understanding of underlying bacterial communities and the formulation of novel biological hypotheses. dbBact can be accessed using a website (http://dbbact.org), plugins for qiime2 and Calour, and programmatically, using a REST-API.