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

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 ac...

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Detalles Bibliográficos
Autores principales: Sengupta, Pratyay, Sivabalan, Shobhan Karthick Muthamilselvi, Mahesh, Amrita, Palanikumar, Indumathi, Kuppa Baskaran, Dinesh Kumar, Raman, Karthik
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer India 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10073628/
https://www.ncbi.nlm.nih.gov/pubmed/37362854
http://dx.doi.org/10.1007/s41745-023-00370-z
Descripción
Sumario: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.