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Methods detecting rhythmic gene expression are biologically relevant only for strong signal

The nycthemeral transcriptome embodies all genes displaying a rhythmic variation of their mRNAs periodically every 24 hours, including but not restricted to circadian genes. In this study, we show that the nycthemeral rhythmicity at the gene expression level is biologically functional and that this...

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Autores principales: Laloum, David, Robinson-Rechavi, Marc
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7100990/
https://www.ncbi.nlm.nih.gov/pubmed/32182235
http://dx.doi.org/10.1371/journal.pcbi.1007666
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author Laloum, David
Robinson-Rechavi, Marc
author_facet Laloum, David
Robinson-Rechavi, Marc
author_sort Laloum, David
collection PubMed
description The nycthemeral transcriptome embodies all genes displaying a rhythmic variation of their mRNAs periodically every 24 hours, including but not restricted to circadian genes. In this study, we show that the nycthemeral rhythmicity at the gene expression level is biologically functional and that this functionality is more conserved between orthologous genes than between random genes. We used this conservation of the rhythmic expression to assess the ability of seven methods (ARSER, Lomb Scargle, RAIN, JTK, empirical-JTK, GeneCycle, and meta2d) to detect rhythmic signal in gene expression. We have contrasted them to a naive method, not based on rhythmic parameters. By taking into account the tissue-specificity of rhythmic gene expression and different species comparisons, we show that no method is strongly favored. The results show that these methods designed for rhythm detection, in addition to having quite similar performances, are consistent only among genes with a strong rhythm signal. Rhythmic genes defined with a standard p-value threshold of 0.01 for instance, could include genes whose rhythmicity is biologically irrelevant. Although these results were dependent on the datasets used and the evolutionary distance between the species compared, we call for caution about the results of studies reporting or using large sets of rhythmic genes. Furthermore, given the analysis of the behaviors of the methods on real and randomized data, we recommend using primarily ARS, empJTK, or GeneCycle, which verify expectations of a classical distribution of p-values. Experimental design should also take into account the circumstances under which the methods seem more efficient, such as giving priority to biological replicates over the number of time-points, or to the number of time-points over the quality of the technique (microarray vs RNAseq). GeneCycle, and to a lesser extent empirical-JTK, might be the most robust method when applied to weakly informative datasets. Finally, our analyzes suggest that rhythmic genes are mainly highly expressed genes.
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spelling pubmed-71009902020-04-03 Methods detecting rhythmic gene expression are biologically relevant only for strong signal Laloum, David Robinson-Rechavi, Marc PLoS Comput Biol Research Article The nycthemeral transcriptome embodies all genes displaying a rhythmic variation of their mRNAs periodically every 24 hours, including but not restricted to circadian genes. In this study, we show that the nycthemeral rhythmicity at the gene expression level is biologically functional and that this functionality is more conserved between orthologous genes than between random genes. We used this conservation of the rhythmic expression to assess the ability of seven methods (ARSER, Lomb Scargle, RAIN, JTK, empirical-JTK, GeneCycle, and meta2d) to detect rhythmic signal in gene expression. We have contrasted them to a naive method, not based on rhythmic parameters. By taking into account the tissue-specificity of rhythmic gene expression and different species comparisons, we show that no method is strongly favored. The results show that these methods designed for rhythm detection, in addition to having quite similar performances, are consistent only among genes with a strong rhythm signal. Rhythmic genes defined with a standard p-value threshold of 0.01 for instance, could include genes whose rhythmicity is biologically irrelevant. Although these results were dependent on the datasets used and the evolutionary distance between the species compared, we call for caution about the results of studies reporting or using large sets of rhythmic genes. Furthermore, given the analysis of the behaviors of the methods on real and randomized data, we recommend using primarily ARS, empJTK, or GeneCycle, which verify expectations of a classical distribution of p-values. Experimental design should also take into account the circumstances under which the methods seem more efficient, such as giving priority to biological replicates over the number of time-points, or to the number of time-points over the quality of the technique (microarray vs RNAseq). GeneCycle, and to a lesser extent empirical-JTK, might be the most robust method when applied to weakly informative datasets. Finally, our analyzes suggest that rhythmic genes are mainly highly expressed genes. Public Library of Science 2020-03-17 /pmc/articles/PMC7100990/ /pubmed/32182235 http://dx.doi.org/10.1371/journal.pcbi.1007666 Text en © 2020 Laloum, Robinson-Rechavi 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
Laloum, David
Robinson-Rechavi, Marc
Methods detecting rhythmic gene expression are biologically relevant only for strong signal
title Methods detecting rhythmic gene expression are biologically relevant only for strong signal
title_full Methods detecting rhythmic gene expression are biologically relevant only for strong signal
title_fullStr Methods detecting rhythmic gene expression are biologically relevant only for strong signal
title_full_unstemmed Methods detecting rhythmic gene expression are biologically relevant only for strong signal
title_short Methods detecting rhythmic gene expression are biologically relevant only for strong signal
title_sort methods detecting rhythmic gene expression are biologically relevant only for strong signal
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7100990/
https://www.ncbi.nlm.nih.gov/pubmed/32182235
http://dx.doi.org/10.1371/journal.pcbi.1007666
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