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Temporal Dynamic Methods for Bulk RNA-Seq Time Series Data

Dynamic studies in time course experimental designs and clinical approaches have been widely used by the biomedical community. These applications are particularly relevant in stimuli-response models under environmental conditions, characterization of gradient biological processes in developmental bi...

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Detalles Bibliográficos
Autores principales: Oh, Vera-Khlara S., Li, Robert W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7997275/
https://www.ncbi.nlm.nih.gov/pubmed/33673721
http://dx.doi.org/10.3390/genes12030352
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author Oh, Vera-Khlara S.
Li, Robert W.
author_facet Oh, Vera-Khlara S.
Li, Robert W.
author_sort Oh, Vera-Khlara S.
collection PubMed
description Dynamic studies in time course experimental designs and clinical approaches have been widely used by the biomedical community. These applications are particularly relevant in stimuli-response models under environmental conditions, characterization of gradient biological processes in developmental biology, identification of therapeutic effects in clinical trials, disease progressive models, cell-cycle, and circadian periodicity. Despite their feasibility and popularity, sophisticated dynamic methods that are well validated in large-scale comparative studies, in terms of statistical and computational rigor, are less benchmarked, comparing to their static counterparts. To date, a number of novel methods in bulk RNA-Seq data have been developed for the various time-dependent stimuli, circadian rhythms, cell-lineage in differentiation, and disease progression. Here, we comprehensively review a key set of representative dynamic strategies and discuss current issues associated with the detection of dynamically changing genes. We also provide recommendations for future directions for studying non-periodical, periodical time course data, and meta-dynamic datasets.
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spelling pubmed-79972752021-03-27 Temporal Dynamic Methods for Bulk RNA-Seq Time Series Data Oh, Vera-Khlara S. Li, Robert W. Genes (Basel) Review Dynamic studies in time course experimental designs and clinical approaches have been widely used by the biomedical community. These applications are particularly relevant in stimuli-response models under environmental conditions, characterization of gradient biological processes in developmental biology, identification of therapeutic effects in clinical trials, disease progressive models, cell-cycle, and circadian periodicity. Despite their feasibility and popularity, sophisticated dynamic methods that are well validated in large-scale comparative studies, in terms of statistical and computational rigor, are less benchmarked, comparing to their static counterparts. To date, a number of novel methods in bulk RNA-Seq data have been developed for the various time-dependent stimuli, circadian rhythms, cell-lineage in differentiation, and disease progression. Here, we comprehensively review a key set of representative dynamic strategies and discuss current issues associated with the detection of dynamically changing genes. We also provide recommendations for future directions for studying non-periodical, periodical time course data, and meta-dynamic datasets. MDPI 2021-02-27 /pmc/articles/PMC7997275/ /pubmed/33673721 http://dx.doi.org/10.3390/genes12030352 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ).
spellingShingle Review
Oh, Vera-Khlara S.
Li, Robert W.
Temporal Dynamic Methods for Bulk RNA-Seq Time Series Data
title Temporal Dynamic Methods for Bulk RNA-Seq Time Series Data
title_full Temporal Dynamic Methods for Bulk RNA-Seq Time Series Data
title_fullStr Temporal Dynamic Methods for Bulk RNA-Seq Time Series Data
title_full_unstemmed Temporal Dynamic Methods for Bulk RNA-Seq Time Series Data
title_short Temporal Dynamic Methods for Bulk RNA-Seq Time Series Data
title_sort temporal dynamic methods for bulk rna-seq time series data
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7997275/
https://www.ncbi.nlm.nih.gov/pubmed/33673721
http://dx.doi.org/10.3390/genes12030352
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