PREDICT

PREDICT harnesses a k-mer pipeline to predict gene expression and identify transcription factor binding motifs in plants under diverse abiotic stress conditions.

Key Features:

• K-mer-based motif identification: Identifies both un-gapped and gapped cis-regulatory elements (CREs) and consensus transcription factor (TF) motifs.
• Gene expression prediction: Addresses sequence-to-expression prediction problems across diverse abiotic stresses in bryophytic and vascular plants, including monocots and dicots.
• Gene regulatory network analysis: Couples CRE characterization with GRN analyses to pinpoint key transcription factors within transcriptional cascades.
• Transcription factor binding motif prediction: Identifies transcription factor binding motifs (TFBMs) for temporal and condition-specific gene expressions in plants.

Scientific Applications:

• Plant stress response research: Predicts gene expression dynamics and identifies regulatory elements underlying plant adaptation to environmental stimuli.
• Cis-regulatory code elucidation: Facilitates understanding of the cis-regulatory code landscape that shapes gene regulation in plant adaptation.
• Cross-species comparative analysis: Enables k-mer identification and expression prediction across bryophytic and vascular plant lineages.

Methodology:

PREDICT implements a k-mer pipeline for sequence-to-expression prediction, utilizing k-mer approaches to identify CRE sites and consensus TF motifs, coupled with gene regulatory network analyses.