Using flux theory in dynamic omics data sets to identify differentially changing signals using DPoP
While we apply this method to time dynamic transcriptomic and phosphoproteomic datasets, it is a numerically generalizable technique that can be applied to any organism and any field interested in time series data analysis. The app described in this work enables omics researchers with no computer science background to apply derivative profiling to their data sets, while also allowing multidisciplined users to build on the nascent idea of profiling derivatives in omics.
Authors
- Mark R. Marten (ORCID: https://orcid.org/0000-0002-1863-8956)
- Ranjan Srivastava (ORCID: https://orcid.org/0000-0003-4309-605X)
- Alexander G. Doan (ORCID: https://orcid.org/0009-0008-6972-1865)
- Steven D. Harris (ORCID: https://orcid.org/0000-0001-5196-1977)
- Harley Edwards (ORCID: https://orcid.org/0000-0003-4110-1687)
- Walker Huso
- Caton Silbiger
- Joseph Zavorskas
Institutions
- University of Connecticut (US)
- Iowa State University (US)
- University of Maryland, Baltimore County (US)
Publication Details
- Journal
- BMC Bioinformatics
- Published
- 2024-09-27
- DOI
- https://doi.org/10.1186/s12859-024-05938-9
- Citations
- 1
- Primary Topic
- Bioinformatics and Genomic Networks
- Type
- article
- Field-Weighted Citation Impact
- 0.23
Funders
- National Science Foundation
- University of Maryland, Baltimore County