Evidence map›Paper›PMID 39333869›Full record

ArticleBMC bioinformatics2024

Using flux theory in dynamic omics data sets to identify differentially changing signals using DPoP.

Harley Edwards, Joseph Zavorskas, Walker Huso, Alexander G Doan, Caton Silbiger, Steven Harris, Ranjan Srivastava, Mark R Marten

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In one paragraph

Article in BMC bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Harley EdwardsDepartment of Chemical, Biochemical, and Environmental Engineering, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD, 21250, USA.
Joseph ZavorskasDepartment of Chemical and Biomolecular Engineering, University of Connecticut, Storrs, CT, USA.
Walker HusoDepartment of Chemical, Biochemical, and Environmental Engineering, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD, 21250, USA.
Alexander G DoanDepartment of Chemical, Biochemical, and Environmental Engineering, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD, 21250, USA.
Caton SilbigerDepartment of Chemical, Biochemical, and Environmental Engineering, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD, 21250, USA.
Steven HarrisDepartment of Plant Pathology, Entomology, and Microbiology, Iowa State University, Ames, IA, USA.
Ranjan SrivastavaDepartment of Chemical and Biomolecular Engineering, University of Connecticut, Storrs, CT, USA.
Mark R MartenDepartment of Chemical, Biochemical, and Environmental Engineering, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD, 21250, USA. marten@umbc.edu.

Funding

NSF 2006189
6 · The paper itself

Abstract

backgroundDerivative profiling is a novel approach to identify differential signals from dynamic omics data sets. This approach applies variable step-size differentiation to time dynamic omics data. This work assumes that there is a general omics derivative that is a useful and descriptive feature of dynamic omics experiments. We assert that this omics derivative, or omics flux, is a valuable descriptor that can be used instead of, or with, fold change calculations.

resultsThe results of derivative profiling are compared to established methods such as Multivariate Adaptive Regression Splines, significance versus fold change analysis (Volcano), and an adjusted ratio over intensity (M/A) analysis to find that there is a statistically significant similarity between the results. This comparison is repeated for transcriptomic and phosphoproteomic expression profiles previously characterized in Aspergillus nidulans. This method has been packaged in an open-source, GUI-based MATLAB app, the Derivative Profiling omics Package (DPoP). Gene Ontology (GO) term enrichment has been included in the app so that a user can automatically/programmatically describe the over/under-represented GO terms in the derivative profiling results using domain specific knowledge found in their organism's specific GO database file. The advantage of the DPoP analysis is that it is computationally inexpensive, it does not require fold change calculations, it describes both instantaneous as well as overall behavior, and it achieves statistical confidence with signal trajectories of a single bio-replicate over four or more points.

conclusionsWhile 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.

Indexed as

Aspergillus nidulansAlgorithmsComputational BiologyGene Expression ProfilingGene OntologyGenomicsProteomicsSoftwareTranscriptomeDifferential signal identificationDynamic omics analysisPhosphoproteomicProteomicTime series analysisTranscriptomic

Identifiers

PMID39333869
PMCPMC11437665

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.