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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Animal Parasitic Diseases Laboratory » Research » Publications at this Location » Publication #377990

Research Project: Molecular Approaches to Control Intestinal Parasites that Affect the Microbiome in Swine and Small Ruminants

Location: Animal Parasitic Diseases Laboratory

Title: Temporal dynamic methods for bulk RNA-Seq time series data

item Oh, Sunghee
item Li, Robert

Submitted to: Genes
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/22/2021
Publication Date: 2/27/2021
Citation: Oh, S., Li, R.W. 2021. Temporal dynamic methods for bulk RNA-Seq time series data. Genes. 12(3):352.

Interpretive Summary:

Technical 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 multiple environmental conditions, characterization of gradient biological processes in developmental biology, identification of therapeutic effects in case-control clinical trials, disease-specific progressive models, cell-cycle and circadian periodical data, and meta-framed strategies in temporal dynamics. 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 have been developed and utilized for the detection of early onset responses to various time-dependent stimuli, circadian rhythms, cell-lineage in differentiation, and disease progressions. Here, we comprehensively reviewed the pros and cons of a key set of representative dynamic strategies and discussed current issues associated with the detection of dynamically changing genes. We also provided recommendations for future directions for studying non-periodical, periodical time-course data, and meta dynamic datasets.