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Integrative OMICS Data-Driven Procedure Using a Derivatized Meta-Analysis Approach

The wealth of high-throughput data has opened up new opportunities to analyze and describe biological processes at higher resolution, ultimately leading to a significant acceleration of scientific output using high-throughput data from the different omics layers and the generation of databases to st...

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Autores principales: Cervantes-Gracia, Karla, Chahwan, Richard, Husi, Holger
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8855827/
https://www.ncbi.nlm.nih.gov/pubmed/35186042
http://dx.doi.org/10.3389/fgene.2022.828786
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author Cervantes-Gracia, Karla
Chahwan, Richard
Husi, Holger
author_facet Cervantes-Gracia, Karla
Chahwan, Richard
Husi, Holger
author_sort Cervantes-Gracia, Karla
collection PubMed
description The wealth of high-throughput data has opened up new opportunities to analyze and describe biological processes at higher resolution, ultimately leading to a significant acceleration of scientific output using high-throughput data from the different omics layers and the generation of databases to store and report raw datasets. The great variability among the techniques and the heterogeneous methodologies used to produce this data have placed meta-analysis methods as one of the approaches of choice to correlate the resultant large-scale datasets from different research groups. Through multi-study meta-analyses, it is possible to generate results with greater statistical power compared to individual analyses. Gene signatures, biomarkers and pathways that provide new insights of a phenotype of interest have been identified by the analysis of large-scale datasets in several fields of science. However, despite all the efforts, a standardized regulation to report large-scale data and to identify the molecular targets and signaling networks is still lacking. Integrative analyses have also been introduced as complementation and augmentation for meta-analysis methodologies to generate novel hypotheses. Currently, there is no universal method established and the different methods available follow different purposes. Herein we describe a new unifying, scalable and straightforward methodology to meta-analyze different omics outputs, but also to integrate the significant outcomes into novel pathways describing biological processes of interest. The significance of using proper molecular identifiers is highlighted as well as the potential to further correlate molecules from different regulatory levels. To show the methodology’s potential, a set of transcriptomic datasets are meta-analyzed as an example.
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spelling pubmed-88558272022-02-19 Integrative OMICS Data-Driven Procedure Using a Derivatized Meta-Analysis Approach Cervantes-Gracia, Karla Chahwan, Richard Husi, Holger Front Genet Genetics The wealth of high-throughput data has opened up new opportunities to analyze and describe biological processes at higher resolution, ultimately leading to a significant acceleration of scientific output using high-throughput data from the different omics layers and the generation of databases to store and report raw datasets. The great variability among the techniques and the heterogeneous methodologies used to produce this data have placed meta-analysis methods as one of the approaches of choice to correlate the resultant large-scale datasets from different research groups. Through multi-study meta-analyses, it is possible to generate results with greater statistical power compared to individual analyses. Gene signatures, biomarkers and pathways that provide new insights of a phenotype of interest have been identified by the analysis of large-scale datasets in several fields of science. However, despite all the efforts, a standardized regulation to report large-scale data and to identify the molecular targets and signaling networks is still lacking. Integrative analyses have also been introduced as complementation and augmentation for meta-analysis methodologies to generate novel hypotheses. Currently, there is no universal method established and the different methods available follow different purposes. Herein we describe a new unifying, scalable and straightforward methodology to meta-analyze different omics outputs, but also to integrate the significant outcomes into novel pathways describing biological processes of interest. The significance of using proper molecular identifiers is highlighted as well as the potential to further correlate molecules from different regulatory levels. To show the methodology’s potential, a set of transcriptomic datasets are meta-analyzed as an example. Frontiers Media S.A. 2022-02-04 /pmc/articles/PMC8855827/ /pubmed/35186042 http://dx.doi.org/10.3389/fgene.2022.828786 Text en Copyright © 2022 Cervantes-Gracia, Chahwan and Husi. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Cervantes-Gracia, Karla
Chahwan, Richard
Husi, Holger
Integrative OMICS Data-Driven Procedure Using a Derivatized Meta-Analysis Approach
title Integrative OMICS Data-Driven Procedure Using a Derivatized Meta-Analysis Approach
title_full Integrative OMICS Data-Driven Procedure Using a Derivatized Meta-Analysis Approach
title_fullStr Integrative OMICS Data-Driven Procedure Using a Derivatized Meta-Analysis Approach
title_full_unstemmed Integrative OMICS Data-Driven Procedure Using a Derivatized Meta-Analysis Approach
title_short Integrative OMICS Data-Driven Procedure Using a Derivatized Meta-Analysis Approach
title_sort integrative omics data-driven procedure using a derivatized meta-analysis approach
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8855827/
https://www.ncbi.nlm.nih.gov/pubmed/35186042
http://dx.doi.org/10.3389/fgene.2022.828786
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