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Big Data in Gastroenterology Research

Studying individual data types in isolation provides only limited and incomplete answers to complex biological questions and particularly falls short in revealing sufficient mechanistic and kinetic details. In contrast, multi-omics approaches to studying health and disease permit the generation and...

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Autores principales: Alizadeh, Madeline, Sampaio Moura, Natalia, Schledwitz, Alyssa, Patil, Seema A., Ravel, Jacques, Raufman, Jean-Pierre
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9916510/
https://www.ncbi.nlm.nih.gov/pubmed/36768780
http://dx.doi.org/10.3390/ijms24032458
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author Alizadeh, Madeline
Sampaio Moura, Natalia
Schledwitz, Alyssa
Patil, Seema A.
Ravel, Jacques
Raufman, Jean-Pierre
author_facet Alizadeh, Madeline
Sampaio Moura, Natalia
Schledwitz, Alyssa
Patil, Seema A.
Ravel, Jacques
Raufman, Jean-Pierre
author_sort Alizadeh, Madeline
collection PubMed
description Studying individual data types in isolation provides only limited and incomplete answers to complex biological questions and particularly falls short in revealing sufficient mechanistic and kinetic details. In contrast, multi-omics approaches to studying health and disease permit the generation and integration of multiple data types on a much larger scale, offering a comprehensive picture of biological and disease processes. Gastroenterology and hepatobiliary research are particularly well-suited to such analyses, given the unique position of the luminal gastrointestinal (GI) tract at the nexus between the gut (mucosa and luminal contents), brain, immune and endocrine systems, and GI microbiome. The generation of ‘big data’ from multi-omic, multi-site studies can enhance investigations into the connections between these organ systems and organisms and more broadly and accurately appraise the effects of dietary, pharmacological, and other therapeutic interventions. In this review, we describe a variety of useful omics approaches and how they can be integrated to provide a holistic depiction of the human and microbial genetic and proteomic changes underlying physiological and pathophysiological phenomena. We highlight the potential pitfalls and alternatives to help avoid the common errors in study design, execution, and analysis. We focus on the application, integration, and analysis of big data in gastroenterology and hepatobiliary research.
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spelling pubmed-99165102023-02-11 Big Data in Gastroenterology Research Alizadeh, Madeline Sampaio Moura, Natalia Schledwitz, Alyssa Patil, Seema A. Ravel, Jacques Raufman, Jean-Pierre Int J Mol Sci Review Studying individual data types in isolation provides only limited and incomplete answers to complex biological questions and particularly falls short in revealing sufficient mechanistic and kinetic details. In contrast, multi-omics approaches to studying health and disease permit the generation and integration of multiple data types on a much larger scale, offering a comprehensive picture of biological and disease processes. Gastroenterology and hepatobiliary research are particularly well-suited to such analyses, given the unique position of the luminal gastrointestinal (GI) tract at the nexus between the gut (mucosa and luminal contents), brain, immune and endocrine systems, and GI microbiome. The generation of ‘big data’ from multi-omic, multi-site studies can enhance investigations into the connections between these organ systems and organisms and more broadly and accurately appraise the effects of dietary, pharmacological, and other therapeutic interventions. In this review, we describe a variety of useful omics approaches and how they can be integrated to provide a holistic depiction of the human and microbial genetic and proteomic changes underlying physiological and pathophysiological phenomena. We highlight the potential pitfalls and alternatives to help avoid the common errors in study design, execution, and analysis. We focus on the application, integration, and analysis of big data in gastroenterology and hepatobiliary research. MDPI 2023-01-27 /pmc/articles/PMC9916510/ /pubmed/36768780 http://dx.doi.org/10.3390/ijms24032458 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Alizadeh, Madeline
Sampaio Moura, Natalia
Schledwitz, Alyssa
Patil, Seema A.
Ravel, Jacques
Raufman, Jean-Pierre
Big Data in Gastroenterology Research
title Big Data in Gastroenterology Research
title_full Big Data in Gastroenterology Research
title_fullStr Big Data in Gastroenterology Research
title_full_unstemmed Big Data in Gastroenterology Research
title_short Big Data in Gastroenterology Research
title_sort big data in gastroenterology research
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9916510/
https://www.ncbi.nlm.nih.gov/pubmed/36768780
http://dx.doi.org/10.3390/ijms24032458
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