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Title: Mathematical-based microbiome analytics for clinical translation
Authors: Narayana, Jayanth Kumar
Mac Aogáin, Micheál
Goh, Wilson Wen Bin
Xia, Kelin
Tsaneva-Atanasova, Krasimira
Chotirmall, Sanjay Haresh
Keywords: Science::Biological sciences
Issue Date: 2021
Source: Narayana, J. K., Mac Aogáin, M., Goh, W. W. B., Xia, K., Tsaneva-Atanasova, K. & Chotirmall, S. H. (2021). Mathematical-based microbiome analytics for clinical translation. Computational and Structural Biotechnology Journal, 19, 6272-6281.
Project: MOH-000141 
Journal: Computational and Structural Biotechnology Journal 
Abstract: 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.
ISSN: 2001-0370
DOI: 10.1016/j.csbj.2021.11.029
Schools: Lee Kong Chian School of Medicine (LKCMedicine) 
School of Biological Sciences 
School of Physical and Mathematical Sciences 
Organisations: Tan Tock Seng Hospital
Rights: © 2021 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY license (
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:LKCMedicine Journal Articles
SBS Journal Articles
SPMS Journal Articles

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