Temporal Dynamic Methods for Bulk RNA-Seq Time Series Data.

Vera-Khlara S Oh, Robert W Li
Author Information
  1. Vera-Khlara S Oh: Animal Genomics and Improvement Laboratory, United States Department of Agriculture, Agricultural Research Service, Beltsville, MD 20705, USA.
  2. Robert W Li: Animal Genomics and Improvement Laboratory, United States Department of Agriculture, Agricultural Research Service, Beltsville, MD 20705, USA. ORCID

Abstract

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.

Keywords

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MeSH Term

Cluster Analysis
Computational Biology
Databases, Nucleic Acid
Humans
RNA-Seq

Word Cloud

Created with Highcharts 10.0.0timediseasedynamicmethodsRNA-SeqDynamicstudiescourseclinicalmodelscircadiandataprogressionexperimentaldesignsapproacheswidelyusedbiomedicalcommunityapplicationsparticularlyrelevantstimuli-responseenvironmentalconditionscharacterizationgradientbiologicalprocessesdevelopmentalbiologyidentificationtherapeuticeffectstrialsprogressivecell-cycleperiodicityDespitefeasibilitypopularitysophisticatedwellvalidatedlarge-scalecomparativetermsstatisticalcomputationalrigorlessbenchmarkedcomparingstaticcounterpartsdatenumbernovelbulkdevelopedvarioustime-dependentstimulirhythmscell-lineagedifferentiationcomprehensivelyreviewkeysetrepresentativestrategiesdiscusscurrentissuesassociateddetectiondynamicallychanginggenesalsoproviderecommendationsfuturedirectionsstudyingnon-periodicalperiodicalmeta-dynamicdatasetsTemporalMethodsBulkTimeSeriesDatadeepmachinelearningdifferentialexpressionanalysesmetadynamicstemporalseriesunsupervisedclustering

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