A comprehensive overview of microbiome data in the light of machine learning applications: categorization, accessibility, and future directions.
disease prediction
machine learning
metadata
metagenome
shotgun sequencing
Journal
Frontiers in microbiology
ISSN: 1664-302X
Titre abrégé: Front Microbiol
Pays: Switzerland
ID NLM: 101548977
Informations de publication
Date de publication:
2024
2024
Historique:
received:
23
11
2023
accepted:
29
01
2024
medline:
29
2
2024
pubmed:
29
2
2024
entrez:
29
2
2024
Statut:
epublish
Résumé
Metagenomics, Metabolomics, and Metaproteomics have significantly advanced our knowledge of microbial communities by providing culture-independent insights into their composition and functional potential. However, a critical challenge in this field is the lack of standard and comprehensive metadata associated with raw data, hindering the ability to perform robust data stratifications and consider confounding factors. In this comprehensive review, we categorize publicly available microbiome data into five types: shotgun sequencing, amplicon sequencing, metatranscriptomic, metabolomic, and metaproteomic data. We explore the importance of metadata for data reuse and address the challenges in collecting standardized metadata. We also, assess the limitations in metadata collection of existing public repositories collecting metagenomic data. This review emphasizes the vital role of metadata in interpreting and comparing datasets and highlights the need for standardized metadata protocols to fully leverage metagenomic data's potential. Furthermore, we explore future directions of implementation of Machine Learning (ML) in metadata retrieval, offering promising avenues for a deeper understanding of microbial communities and their ecological roles. Leveraging these tools will enhance our insights into microbial functional capabilities and ecological dynamics in diverse ecosystems. Finally, we emphasize the crucial metadata role in ML models development.
Identifiants
pubmed: 38419630
doi: 10.3389/fmicb.2024.1343572
pmc: PMC10900530
doi:
Types de publication
Journal Article
Review
Langues
eng
Pagination
1343572Informations de copyright
Copyright © 2024 Kumar, Lorusso, Fosso and Pesole.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.