An API for dynamic estimation of reference intervals for functional abundances of gut microbiota

The human microbiome is a complex ecosystem that plays a crucial role in maintaining human health and well-being. The gut microbiome, in particular, has been extensively studied due to its critical functions in nutrient extraction, immune system regulation, and mental health. However, the vast varia...

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Veröffentlicht in:Biológia Jg. 79; H. 1; S. 343 - 353
Hauptverfasser: Binokay, Leman, Oktay, Yavuz, Karakülah, Gökhan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cham Springer International Publishing 01.01.2024
Springer Nature B.V
Schlagworte:
Abundance
Algorithms
Application programming interface
Bacteria
Biomedical and Life Sciences
Cell Biology
Datasets
Disease
Estimation
Gut microbiota
Immune system
Intervals
Intestinal microflora
Life Sciences
Metabolism
Microbial activity
Microbiology
Microbiomes
Microbiota
Microorganisms
Original Article
Plant Sciences
Populations
Well being
Zoology
API (Application Programming Interface)
Reference data set
Bioinformatics
16S rRNA sequencing
Microbiome
ISSN:1336-9563, 0006-3088, 1336-9563
Online-Zugang:Volltext
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Zusammenfassung:The human microbiome is a complex ecosystem that plays a crucial role in maintaining human health and well-being. The gut microbiome, in particular, has been extensively studied due to its critical functions in nutrient extraction, immune system regulation, and mental health. However, the vast variability in the composition of microbial communities across individuals and populations poses a challenge in microbiome research and is an obstacle for the development of interventions. Reference microbiome data sets and standardized pipelines for the analysis of microbiome data have been developed to overcome this challenge, however, they are largely static and cannot be improved with user-generated data. Here, we introduce an application programming interface (API) that allows for the dynamic estimation of reference intervals for gut microbiota functional abundances using the refineR algorithm. Importantly, our API can continuously adapt to changes in gut microbiota profiles stored in a local database and allows the improvement of existing reference data for a given condition over time by the incorporation of new data from different individuals or populations. We demonstrate the usefulness of our API by generating a basal gut microbiota profile of a healthy Turkish population and estimating reference intervals for the functional abundance of manually selected Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Our study highlights the potential of APIs in facilitating microbiome research by providing a dynamic and customizable tool for estimating reference intervals for gut microbiota functional abundances.
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ISSN:1336-9563
0006-3088
1336-9563
DOI:10.1007/s11756-023-01556-7