Cargando…
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...
Autores principales: | , , |
---|---|
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 |
_version_ | 1784653724160884736 |
---|---|
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. |
format | Online Article Text |
id | pubmed-8855827 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT cervantesgraciakarla integrativeomicsdatadrivenprocedureusingaderivatizedmetaanalysisapproach AT chahwanrichard integrativeomicsdatadrivenprocedureusingaderivatizedmetaanalysisapproach AT husiholger integrativeomicsdatadrivenprocedureusingaderivatizedmetaanalysisapproach |