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Identification and correction of time-series transcriptomic anomalies.

Sophia A Campione1,2, Christina M Kelliher3, Cullen Roth1

  • 1Department of Biology, Duke University, Durham, NC. 27708 United States.

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|June 30, 2025
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Summary
This summary is machine-generated.

Transcriptomic artifacts (STRIPEs) are technical errors in time-series data. This study introduces a computational method to detect and correct STRIPEs, improving downstream biological analysis and gene periodicity identification.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Time-series transcriptomic analyses are crucial for understanding dynamic biological processes.
  • Transcriptomic artifacts (STRIPEs) can manifest as large, non-biological changes between time points.
  • STRIPEs are observed across species and experimental replicates, complicating data interpretation.

Purpose of the Study:

  • To demonstrate the prevalence of STRIPEs in diverse time-series transcriptomic datasets.
  • To develop and validate a computational method for detecting STRIPEs.
  • To present and evaluate methods for correcting STRIPEs to improve data accuracy.

Main Methods:

  • Demonstration of STRIPEs across five distinct time-series transcriptomic datasets.
  • Application of the Kolmogorov-Smirnov statistical test for STRIPE detection.
  • Implementation and assessment of three distinct STRIPE correction strategies.

Main Results:

  • STRIPEs were confirmed in multiple species, biological contexts, and timescales.
  • A user-friendly computational method for STRIPE detection was established.
  • STRIPE correction significantly impacted downstream analysis, altering periodicity labeling for nearly 600 genes.

Conclusions:

  • STRIPEs represent a significant challenge in time-series transcriptomic studies.
  • The developed detection and correction methods offer a robust solution for data artifact removal.
  • Accurate STRIPE correction is essential for reliable downstream analyses, particularly for identifying gene periodicity.