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Protocol to predict gene expression from transcriptomic data using PREDICT.

Ming-Ren Yen1, Kai-Cheng Pan2, Ming-Jung Liu3

  • 1Institute of Plant and Microbial Biology, Academia Sinica, Taipei 115201, Taiwan.

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|April 4, 2026
PubMed
Summary
This summary is machine-generated.

PREDICT is a Python package that helps researchers find DNA regulatory elements and transcription factor binding motifs. This tool aids in understanding gene expression in various organisms.

Keywords:
BioinformaticsGenomicsPlant sciencesRNA-seqSequence analysisSystems biology

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Area of Science:

  • Genomics
  • Computational Biology
  • Molecular Biology

Background:

  • Understanding DNA sequence variation's link to gene expression is crucial, particularly in non-model organisms.
  • Identifying cis-regulatory elements and transcription factor binding motifs is key to deciphering transcriptional programs.

Purpose of the Study:

  • To introduce PREDICT, a modular Python package designed for discovering cis-regulatory elements and transcription factor binding motifs.
  • To provide a robust, k-mer-based method for uncovering regulatory logic in diverse genomic systems.

Main Methods:

  • Enrichment analysis of k-mers from differentially expressed genes.
  • Mapping identified k-mers to known transcription factor binding motifs.
  • Quantifying the impact of motifs on gene expression and visualizing their co-occurrences.

Main Results:

  • PREDICT enables the identification of enriched k-mers associated with differential gene expression.
  • The package facilitates the mapping of these k-mers to known motifs, aiding in the discovery of regulatory elements.
  • PREDICT offers visualization tools for motif co-occurrences, providing insights into regulatory networks.

Conclusions:

  • PREDICT offers a powerful and versatile approach for regulatory genomics research.
  • The package is suitable for diverse genomic systems, including non-model organisms.
  • PREDICT contributes to a deeper understanding of gene regulation by linking DNA variation to transcriptional programs.