Embedding mRNA stability in correlation analysis of time-series gene expression data.

Current methods for the identification of putatively co-regulated genes directly from gene expression time profiles are based on the similarity of the time profile. Such association metrics, despite their central role in gene network inference and machine learning, have largely ignored the impact of...

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Autores principales:	Lorenzo Farina, Alberto De Santis, Samanta Salvucci, Giorgio Morelli, Ida Ruberti
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2008
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/c0a5040f920e4f10b3795595fb6875ed
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spelling	oai:doaj.org-article:c0a5040f920e4f10b3795595fb6875ed2021-11-25T05:41:11ZEmbedding mRNA stability in correlation analysis of time-series gene expression data.1553-734X1553-735810.1371/journal.pcbi.1000141https://doaj.org/article/c0a5040f920e4f10b3795595fb6875ed2008-08-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/18670596/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Current methods for the identification of putatively co-regulated genes directly from gene expression time profiles are based on the similarity of the time profile. Such association metrics, despite their central role in gene network inference and machine learning, have largely ignored the impact of dynamics or variation in mRNA stability. Here we introduce a simple, but powerful, new similarity metric called lead-lag R(2) that successfully accounts for the properties of gene dynamics, including varying mRNA degradation and delays. Using yeast cell-cycle time-series gene expression data, we demonstrate that the predictive power of lead-lag R(2) for the identification of co-regulated genes is significantly higher than that of standard similarity measures, thus allowing the selection of a large number of entirely new putatively co-regulated genes. Furthermore, the lead-lag metric can also be used to uncover the relationship between gene expression time-series and the dynamics of formation of multiple protein complexes. Remarkably, we found a high lead-lag R(2) value among genes coding for a transient complex.Lorenzo FarinaAlberto De SantisSamanta SalvucciGiorgio MorelliIda RubertiPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 4, Iss 8, p e1000141 (2008)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Lorenzo Farina Alberto De Santis Samanta Salvucci Giorgio Morelli Ida Ruberti Embedding mRNA stability in correlation analysis of time-series gene expression data.
description	Current methods for the identification of putatively co-regulated genes directly from gene expression time profiles are based on the similarity of the time profile. Such association metrics, despite their central role in gene network inference and machine learning, have largely ignored the impact of dynamics or variation in mRNA stability. Here we introduce a simple, but powerful, new similarity metric called lead-lag R(2) that successfully accounts for the properties of gene dynamics, including varying mRNA degradation and delays. Using yeast cell-cycle time-series gene expression data, we demonstrate that the predictive power of lead-lag R(2) for the identification of co-regulated genes is significantly higher than that of standard similarity measures, thus allowing the selection of a large number of entirely new putatively co-regulated genes. Furthermore, the lead-lag metric can also be used to uncover the relationship between gene expression time-series and the dynamics of formation of multiple protein complexes. Remarkably, we found a high lead-lag R(2) value among genes coding for a transient complex.
format	article
author	Lorenzo Farina Alberto De Santis Samanta Salvucci Giorgio Morelli Ida Ruberti
author_facet	Lorenzo Farina Alberto De Santis Samanta Salvucci Giorgio Morelli Ida Ruberti
author_sort	Lorenzo Farina
title	Embedding mRNA stability in correlation analysis of time-series gene expression data.
title_short	Embedding mRNA stability in correlation analysis of time-series gene expression data.
title_full	Embedding mRNA stability in correlation analysis of time-series gene expression data.
title_fullStr	Embedding mRNA stability in correlation analysis of time-series gene expression data.
title_full_unstemmed	Embedding mRNA stability in correlation analysis of time-series gene expression data.
title_sort	embedding mrna stability in correlation analysis of time-series gene expression data.
publisher	Public Library of Science (PLoS)
publishDate	2008
url	https://doaj.org/article/c0a5040f920e4f10b3795595fb6875ed
work_keys_str_mv	AT lorenzofarina embeddingmrnastabilityincorrelationanalysisoftimeseriesgeneexpressiondata AT albertodesantis embeddingmrnastabilityincorrelationanalysisoftimeseriesgeneexpressiondata AT samantasalvucci embeddingmrnastabilityincorrelationanalysisoftimeseriesgeneexpressiondata AT giorgiomorelli embeddingmrnastabilityincorrelationanalysisoftimeseriesgeneexpressiondata AT idaruberti embeddingmrnastabilityincorrelationanalysisoftimeseriesgeneexpressiondata
_version_	1718414541774127104

Embedding mRNA stability in correlation analysis of time-series gene expression data.

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