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

Harley Edwards1, Joseph Zavorskas2, Walker Huso1

  • 1Department of Chemical, Biochemical, and Environmental Engineering, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD, 21250, USA.

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Summary

Derivative profiling offers a novel method to analyze dynamic omics data, identifying differential signals using omics flux. This computationally inexpensive approach provides statistically significant results and is accessible via the open-source Derivative Profiling omics Package (DPoP) app.

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Differential signal identificationDynamic omics analysisPhosphoproteomicProteomicTime series analysisTranscriptomic

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Area of Science:

  • Omics data analysis
  • Bioinformatics
  • Computational biology

Background:

  • Derivative profiling is a novel approach for identifying differential signals in dynamic omics datasets.
  • It utilizes variable step-size differentiation to analyze time-series omics data, proposing 'omics flux' as a descriptive feature.
  • This method can complement or replace traditional fold change calculations.

Purpose of the Study:

  • To introduce and validate derivative profiling as a robust method for analyzing dynamic omics data.
  • To develop an accessible, open-source tool for applying derivative profiling.
  • To demonstrate the utility of derivative profiling across different omics types and organisms.

Main Methods:

  • Application of variable step-size differentiation to time-series omics data.
  • Development of the Derivative Profiling omics Package (DPoP), a GUI-based MATLAB application.
  • Integration of Gene Ontology (GO) term enrichment analysis within the DPoP app.

Main Results:

  • Derivative profiling results show statistically significant similarity to established methods like MARS, Volcano, and M/A analysis.
  • The method was validated on transcriptomic and phosphoproteomic datasets from Aspergillus nidulans.
  • DPoP is computationally inexpensive, does not require fold change calculations, and can achieve statistical confidence with single bio-replicates.

Conclusions:

  • Derivative profiling is a numerically generalizable technique applicable to any organism and time-series data analysis.
  • The DPoP app empowers omics researchers, regardless of computer science background, to utilize derivative profiling.
  • This work facilitates further development in the field of omics derivative profiling.