Mathematical-based microbiome analytics for clinical translation

• Published in: Computational and structural biotechnology journal Vol. 19; pp. 6272 - 6281
• Main Authors: Narayana, Jayanth Kumar, Mac Aogáin, Micheál, Goh, Wilson Wen Bin, Xia, Kelin, Tsaneva-Atanasova, Krasimira, Chotirmall, Sanjay H.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.01.2021; Research Network of Computational and Structural Biotechnology; Elsevier
• Subjects: antibiotic resistance; bioinformatics; biotechnology; chronic diseases; data collection; human diseases; humans; Integration; Machine learning; Mathematical modelling; Microbial association analysis; microbiology; Microbiome; Review; species; Topological data analysis; topology; Integration; Mathematical modelling; Topological data analysis; Microbial association analysis; Machine learning; Microbiome
• ISSN: 2001-0370; 2001-0370
• DOI: 10.1016/j.csbj.2021.11.029

[Display omitted] Traditionally, human microbiology has been strongly built on the laboratory focused culture of microbes isolated from human specimens in patients with acute or chronic infection. These approaches primarily view human disease through the lens of a single species and its relevant clinical setting however such approaches fail to account for the surrounding environment and wide microbial diversity that exists in vivo. Given the emergence of next generation sequencing technologies and advancing bioinformatic pipelines, researchers now have unprecedented capabilities to characterise the human microbiome in terms of its taxonomy, function, antibiotic resistance and even bacteriophages. Despite this, an analysis of microbial communities has largely been restricted to ordination, ecological measures, and discriminant taxa analysis. This is predominantly due to a lack of suitable computational tools to facilitate microbiome analytics. In this review, we first evaluate the key concerns related to the inherent structure of microbiome datasets which include its compositionality and batch effects. We describe the available and emerging analytical techniques including integrative analysis, machine learning, microbial association networks, topological data analysis (TDA) and mathematical modelling. We also present how these methods may translate to clinical settings including tools for implementation. Mathematical based analytics for microbiome analysis represents a promising avenue for clinical translation across a range of acute and chronic disease states.