Cargando…

Footprint-based functional analysis of multiomic data

Omic technologies allow us to generate extensive data, including transcriptomic, proteomic, phosphoproteomic and metabolomic. These data can be used to study signal transduction, gene regulation and metabolism. In this review, we summarise resources and methods to analysis these types of data. We fo...

Descripción completa

Detalles Bibliográficos
Autores principales: Dugourd, Aurelien, Saez-Rodriguez, Julio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Ltd 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7357600/
https://www.ncbi.nlm.nih.gov/pubmed/32685770
http://dx.doi.org/10.1016/j.coisb.2019.04.002
_version_ 1783558711555391488
author Dugourd, Aurelien
Saez-Rodriguez, Julio
author_facet Dugourd, Aurelien
Saez-Rodriguez, Julio
author_sort Dugourd, Aurelien
collection PubMed
description Omic technologies allow us to generate extensive data, including transcriptomic, proteomic, phosphoproteomic and metabolomic. These data can be used to study signal transduction, gene regulation and metabolism. In this review, we summarise resources and methods to analysis these types of data. We focus on methods developed to recover functional insights using footprints. Footprints are signatures defined by the effect of molecules or processes of interest. They integrate information from multiple measurements whose abundances are under the influence of a common regulator. For example, transcripts controlled by a transcription factor or peptides phosphorylated by a kinase. Footprints can also be generalised across multiple types of omic data. Thus, we also present methods to integrate multiple types of omic data and features (such as the ones derived from footprints) together. We highlight some examples of studies that leverage such approaches to discover new biological mechanisms.
format Online
Article
Text
id pubmed-7357600
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher Elsevier Ltd
record_format MEDLINE/PubMed
spelling pubmed-73576002020-07-17 Footprint-based functional analysis of multiomic data Dugourd, Aurelien Saez-Rodriguez, Julio Curr Opin Syst Biol Article Omic technologies allow us to generate extensive data, including transcriptomic, proteomic, phosphoproteomic and metabolomic. These data can be used to study signal transduction, gene regulation and metabolism. In this review, we summarise resources and methods to analysis these types of data. We focus on methods developed to recover functional insights using footprints. Footprints are signatures defined by the effect of molecules or processes of interest. They integrate information from multiple measurements whose abundances are under the influence of a common regulator. For example, transcripts controlled by a transcription factor or peptides phosphorylated by a kinase. Footprints can also be generalised across multiple types of omic data. Thus, we also present methods to integrate multiple types of omic data and features (such as the ones derived from footprints) together. We highlight some examples of studies that leverage such approaches to discover new biological mechanisms. Elsevier Ltd 2019-06 /pmc/articles/PMC7357600/ /pubmed/32685770 http://dx.doi.org/10.1016/j.coisb.2019.04.002 Text en © 2019 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Dugourd, Aurelien
Saez-Rodriguez, Julio
Footprint-based functional analysis of multiomic data
title Footprint-based functional analysis of multiomic data
title_full Footprint-based functional analysis of multiomic data
title_fullStr Footprint-based functional analysis of multiomic data
title_full_unstemmed Footprint-based functional analysis of multiomic data
title_short Footprint-based functional analysis of multiomic data
title_sort footprint-based functional analysis of multiomic data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7357600/
https://www.ncbi.nlm.nih.gov/pubmed/32685770
http://dx.doi.org/10.1016/j.coisb.2019.04.002
work_keys_str_mv AT dugourdaurelien footprintbasedfunctionalanalysisofmultiomicdata
AT saezrodriguezjulio footprintbasedfunctionalanalysisofmultiomicdata