Achieving pan-microbiome biological insights via the dbBact knowledge base

Abstract

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.

Integrating biological knowledge for mechanistic inference in the host-associated microbiome

“…With over 1,400 unique taxa represented, BugSigDB is rich in metadata, experimental conditions, and design of each experiment and is well standardized to a range of ontologies. Another knowledge base, dbBact, includes over 900 experiments and supports similar use cases aligning results across many studies ( Amir et al, 2023 ). These literature-based databases support easy access to information in a context dependent manner.…”

A Comprehensive View of Food Microbiota: Introducing FoodMicrobionet v5

“…In addition, several web-based database and tools [ 22 ] provide access to sequences and to processing pipelines. Notable examples are QIITA ( , last accessed on 18 April 2024) [ 23 ], MGnify ( , last accessed on 18 April 2024) [ 24 ], IMNGS ( , last accessed on 18 April 2024) [ 25 ], DBbact ( , last accessed on 18 April 2024) [ 26 ], and the Microbe Atlas ( , last accessed on 18 April 2024) [ 27 ]. However, only recently have Minimum Information about MARKer gene Sequence (MIMARKS [ 28 ]) standards become compulsory for sample metadata.…”