Mathematical-based microbiome analytics for clinical translation

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 howe...

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Autores principales:	Jayanth Kumar Narayana, Micheál Mac Aogáin, Wilson Wen Bin Goh, Kelin Xia, Krasimira Tsaneva-Atanasova, Sanjay H. Chotirmall
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Microbiome Integration Mathematical modelling Microbial association analysis Topological data analysis Machine learning Biotechnology TP248.13-248.65
Acceso en línea:	https://doaj.org/article/fec00b2004194b0386ec087b1d2bf53e
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spelling	oai:doaj.org-article:fec00b2004194b0386ec087b1d2bf53e2021-11-30T04:15:26ZMathematical-based microbiome analytics for clinical translation2001-037010.1016/j.csbj.2021.11.029https://doaj.org/article/fec00b2004194b0386ec087b1d2bf53e2021-01-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2001037021004943https://doaj.org/toc/2001-0370Traditionally, 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.Jayanth Kumar NarayanaMicheál Mac AogáinWilson Wen Bin GohKelin XiaKrasimira Tsaneva-AtanasovaSanjay H. ChotirmallElsevierarticleMicrobiomeIntegrationMathematical modellingMicrobial association analysisTopological data analysisMachine learningBiotechnologyTP248.13-248.65ENComputational and Structural Biotechnology Journal, Vol 19, Iss , Pp 6272-6281 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Microbiome Integration Mathematical modelling Microbial association analysis Topological data analysis Machine learning Biotechnology TP248.13-248.65
spellingShingle	Microbiome Integration Mathematical modelling Microbial association analysis Topological data analysis Machine learning Biotechnology TP248.13-248.65 Jayanth Kumar Narayana Micheál Mac Aogáin Wilson Wen Bin Goh Kelin Xia Krasimira Tsaneva-Atanasova Sanjay H. Chotirmall Mathematical-based microbiome analytics for clinical translation
description	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.
format	article
author	Jayanth Kumar Narayana Micheál Mac Aogáin Wilson Wen Bin Goh Kelin Xia Krasimira Tsaneva-Atanasova Sanjay H. Chotirmall
author_facet	Jayanth Kumar Narayana Micheál Mac Aogáin Wilson Wen Bin Goh Kelin Xia Krasimira Tsaneva-Atanasova Sanjay H. Chotirmall
author_sort	Jayanth Kumar Narayana
title	Mathematical-based microbiome analytics for clinical translation
title_short	Mathematical-based microbiome analytics for clinical translation
title_full	Mathematical-based microbiome analytics for clinical translation
title_fullStr	Mathematical-based microbiome analytics for clinical translation
title_full_unstemmed	Mathematical-based microbiome analytics for clinical translation
title_sort	mathematical-based microbiome analytics for clinical translation
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/fec00b2004194b0386ec087b1d2bf53e
work_keys_str_mv	AT jayanthkumarnarayana mathematicalbasedmicrobiomeanalyticsforclinicaltranslation AT michealmacaogain mathematicalbasedmicrobiomeanalyticsforclinicaltranslation AT wilsonwenbingoh mathematicalbasedmicrobiomeanalyticsforclinicaltranslation AT kelinxia mathematicalbasedmicrobiomeanalyticsforclinicaltranslation AT krasimiratsanevaatanasova mathematicalbasedmicrobiomeanalyticsforclinicaltranslation AT sanjayhchotirmall mathematicalbasedmicrobiomeanalyticsforclinicaltranslation
_version_	1718406841965215744

Mathematical-based microbiome analytics for clinical translation

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