BugSigDB captures patterns of differential abundance across a broad range of host-associated microbial signatures

Mirzayi, Chloe; Zohra, Fatima; Azhar, Rimsha; Elsafoury, Shaimaa; Grieve, Claire; Wokaty, Jennifer; Gamboa-Tuz, Samuel David; Sengupta, Pratyay; Hecht, Isaac; Ravikrishnan, Aarthi; Gonçalves, Rafael S.; Franzosa, Eric A.; Raman, Karthik; Carey, Vincent J.; Dowd, Jennifer Beam; Jones, Heidi E.; Davis, Sean; Segata, Nicola; Huttenhower, Curtis

doi:10.1101/2022.10.24.22281483

Cited by 4 publications

(3 citation statements)

References 110 publications

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“…Additionally, gutMDisorder has been used to validate polysaccharides identified to have a regulatory effect in disease through microbe-disease relationships in the form of a network (Hu et al, 2022;Wei et al, 2023). BugSigDB demonstrates the value in having a heterogeneous resource to explore patterns of microbial composition across studies, examines the commonly co-occurring or mutually exclusive individual or groups of microbes, and evaluates differences in microbial communities across body sites (Geistlinger et al, 2022). However, despite these highly useful applications of manually curated, correlative knowledge bases, there are key challenges that contribute to their limited use.…”

Section: Contextualizing Experimental Findingsmentioning

confidence: 99%

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

Santangelo,

Apgar,

Colorado

et al. 2024

Front. Microbiol.

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Advances in high-throughput technologies have enhanced our ability to describe microbial communities as they relate to human health and disease. Alongside the growth in sequencing data has come an influx of resources that synthesize knowledge surrounding microbial traits, functions, and metabolic potential with knowledge of how they may impact host pathways to influence disease phenotypes. These knowledge bases can enable the development of mechanistic explanations that may underlie correlations detected between microbial communities and disease. In this review, we survey existing resources and methodologies for the computational integration of broad classes of microbial and host knowledge. We evaluate these knowledge bases in their access methods, content, and source characteristics. We discuss challenges of the creation and utilization of knowledge bases including inconsistency of nomenclature assignment of taxa and metabolites across sources, whether the biological entities represented are rooted in ontologies or taxonomies, and how the structure and accessibility limit the diversity of applications and user types. We make this information available in a code and data repository at: https://github.com/lozuponelab/knowledge-source-mappings. Addressing these challenges will allow for the development of more effective tools for drawing from abundant knowledge to find new insights into microbial mechanisms in disease by fostering a systematic and unbiased exploration of existing information.

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Section: Contextualizing Experimental Findingsmentioning

confidence: 99%

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

Santangelo,

Apgar,

Colorado

et al. 2024

Front. Microbiol.

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“…The collected data can be harnessed to predict the characteristic microbial signature or biomarker associated with the physiological condition of the host using graph-based 61 , constraint-based 46 or machine-learning-based approaches 74 . Databases such as BugSigDB 41 , Disbiome 49 and Amadis 64 curated the microbial association with various disease conditions based on experimental evidence and differential abundance studies. These comprehensive databases could help the research community to understand the context-specific effects of a microorganism in microbiota.…”

Section: Microbiome Databasesmentioning

confidence: 99%

Big Data for a Small World: A Review on Databases and Resources for Studying Microbiomes

et al. 2023

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Microorganisms are ubiquitous in nature and form complex community networks to survive in various environments. This community structure depends on numerous factors like nutrient availability, abiotic factors like temperature and pH as well as microbial composition. Categorising accessible biomes according to their habitats would help in understanding the complexity of the environment-specific communities. Owing to the recent improvements in sequencing facilities, researchers have started to explore diverse microbiomes rapidly and attempts have been made to study microbial crosstalk. However, different metagenomics sampling, preprocessing, and annotation methods make it difficult to compare multiple studies and hinder the recycling of data. Huge datasets originating from these experiments demand systematic computational methods to extract biological information beyond microbial compositions. Further exploration of microbial co-occurring patterns across the biomes could help us in designing cross-biome experiments. In this review, we catalogue databases with system-specific microbiomes, discussing publicly available common databases as well as specialised databases for a range of microbiomes. If the new datasets generated in the future could maintain at least biome-specific annotation, then researchers could use those contemporary tools for relevant and bias-free analysis of complex metagenomics data.

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“…com/waldronlab/BugSigDB. Statistical analysis was performed using R 125 and Bioconductor 126 and is reproducible using the code provided on GitHub 127 .…”

Section: Articlementioning

confidence: 99%

BugSigDB captures patterns of differential abundance across a broad range of host-associated microbial signatures

Geistlinger,

Mirzayi,

Zohra

et al. 2023

Nat Biotechnol

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The literature of human and other host-associated microbiome studies is expanding rapidly, but systematic comparisons among published results of host-associated microbiome signatures of differential abundance remain difficult. We present BugSigDB, a community-editable database of manually curated microbial signatures from published differential abundance studies accompanied by information on study geography, health outcomes, host body site and experimental, epidemiological and statistical methods using controlled vocabulary. The initial release of the database contains >2,500 manually curated signatures from >600 published studies on three host species, enabling high-throughput analysis of signature similarity, taxon enrichment, co-occurrence and coexclusion and consensus signatures. These data allow assessment of microbiome differential abundance within and across experimental conditions, environments or body sites. Database-wide analysis reveals experimental conditions with the highest level of consistency in signatures reported by independent studies and identifies commonalities among disease-associated signatures, including frequent introgression of oral pathobionts into the gut.

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BugSigDB captures patterns of differential abundance across a broad range of host-associated microbial signatures

Cited by 4 publications

References 110 publications

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

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

Big Data for a Small World: A Review on Databases and Resources for Studying Microbiomes

BugSigDB captures patterns of differential abundance across a broad range of host-associated microbial signatures

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