A comprehensive overview of microbiome data in the light of machine learning applications: categorization, accessibility, and future directions

• DOI: 10.3389/fmicb.2024.1343572
• Publication Date: 2024-02-13T04:23:43Z
• Authors: Bablu Kumar; Erika Lorusso; Bruno Fosso; Graziano Pesole
• Keywords: Metaproteomics; Leverage (statistics)

Metagenomics, Metabolomics, and Metaproteomics have significantly advanced our knowledge of microbial communities by providing culture-independent insights into their composition functional potential. However, a critical challenge in this field is the lack standard comprehensive metadata associated with raw data, hindering ability to perform robust data stratifications consider confounding factors. In review, we categorize publicly available microbiome five types: shotgun sequencing, amplicon metatranscriptomic, metabolomic, metaproteomic data. We explore importance for reuse address challenges collecting standardized metadata. also, assess limitations collection existing public repositories metagenomic This review emphasizes vital role interpreting comparing datasets highlights need protocols fully leverage data's Furthermore, future directions implementation Machine Learning (ML) retrieval, offering promising avenues deeper understanding ecological roles. Leveraging these tools will enhance capabilities dynamics diverse ecosystems. Finally, emphasize crucial ML models development.