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Identifying biologically relevant putative mechanisms in a given phenotype comparison
A major challenge in life science research is understanding the mechanism involved in a given phenotype. The ability to identify the correct mechanisms is needed in order to understand fundamental and very important phenomena such as mechanisms of disease, immune systems responses to various challen...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5423614/ https://www.ncbi.nlm.nih.gov/pubmed/28486531 http://dx.doi.org/10.1371/journal.pone.0176950 |
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author | Hanoudi, Samer Donato, Michele Draghici, Sorin |
author_facet | Hanoudi, Samer Donato, Michele Draghici, Sorin |
author_sort | Hanoudi, Samer |
collection | PubMed |
description | A major challenge in life science research is understanding the mechanism involved in a given phenotype. The ability to identify the correct mechanisms is needed in order to understand fundamental and very important phenomena such as mechanisms of disease, immune systems responses to various challenges, and mechanisms of drug action. The current data analysis methods focus on the identification of the differentially expressed (DE) genes using their fold change and/or p-values. Major shortcomings of this approach are that: i) it does not consider the interactions between genes; ii) its results are sensitive to the selection of the threshold(s) used, and iii) the set of genes produced by this approach is not always conducive to formulating mechanistic hypotheses. Here we present a method that can construct networks of genes that can be considered putative mechanisms. The putative mechanisms constructed by this approach are not limited to the set of DE genes, but also considers all known and relevant gene-gene interactions. We analyzed three real datasets for which both the causes of the phenotype, as well as the true mechanisms were known. We show that the method identified the correct mechanisms when applied on microarray datasets from mouse. We compared the results of our method with the results of the classical approach, showing that our method produces more meaningful biological insights. |
format | Online Article Text |
id | pubmed-5423614 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-54236142017-05-15 Identifying biologically relevant putative mechanisms in a given phenotype comparison Hanoudi, Samer Donato, Michele Draghici, Sorin PLoS One Research Article A major challenge in life science research is understanding the mechanism involved in a given phenotype. The ability to identify the correct mechanisms is needed in order to understand fundamental and very important phenomena such as mechanisms of disease, immune systems responses to various challenges, and mechanisms of drug action. The current data analysis methods focus on the identification of the differentially expressed (DE) genes using their fold change and/or p-values. Major shortcomings of this approach are that: i) it does not consider the interactions between genes; ii) its results are sensitive to the selection of the threshold(s) used, and iii) the set of genes produced by this approach is not always conducive to formulating mechanistic hypotheses. Here we present a method that can construct networks of genes that can be considered putative mechanisms. The putative mechanisms constructed by this approach are not limited to the set of DE genes, but also considers all known and relevant gene-gene interactions. We analyzed three real datasets for which both the causes of the phenotype, as well as the true mechanisms were known. We show that the method identified the correct mechanisms when applied on microarray datasets from mouse. We compared the results of our method with the results of the classical approach, showing that our method produces more meaningful biological insights. Public Library of Science 2017-05-09 /pmc/articles/PMC5423614/ /pubmed/28486531 http://dx.doi.org/10.1371/journal.pone.0176950 Text en © 2017 Hanoudi et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Hanoudi, Samer Donato, Michele Draghici, Sorin Identifying biologically relevant putative mechanisms in a given phenotype comparison |
title | Identifying biologically relevant putative mechanisms in a given phenotype comparison |
title_full | Identifying biologically relevant putative mechanisms in a given phenotype comparison |
title_fullStr | Identifying biologically relevant putative mechanisms in a given phenotype comparison |
title_full_unstemmed | Identifying biologically relevant putative mechanisms in a given phenotype comparison |
title_short | Identifying biologically relevant putative mechanisms in a given phenotype comparison |
title_sort | identifying biologically relevant putative mechanisms in a given phenotype comparison |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5423614/ https://www.ncbi.nlm.nih.gov/pubmed/28486531 http://dx.doi.org/10.1371/journal.pone.0176950 |
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