DExTER

DExTER detects and characterizes long regulatory elements (LREs), including CpG islands, polydA:dT tracts, and AU-rich elements, in eukaryotic genomes to analyze their roles in gene regulation and evolutionary conservation.

Key Features:

• Identification and characterization: Identifies and characterizes long regulatory elements (LREs) such as CpG islands, polydA:dT tracts, and AU-rich elements across eukaryotic genomes.
• Genome-wide systematic detection: Performs systematic detection of regulatory regions across diverse eukaryotic genomes.
• Expression prediction: Predicts gene expression levels based solely on the sequence of identified long regulatory regions.
• Evolutionary conservation analysis: Detects and reports evolutionary conservation of LREs across species.
• Species- and stage-specific analysis: Characterizes variation in LRE behavior across species, developmental stages, and life cycle conditions, including dynamic regulation in Apicomplexa such as Plasmodium falciparum and consistent involvement across tissues in multicellular eukaryotes.
• Regulatory level assessment: Assesses contributions of LREs to transcriptomic and post-transcriptomic regulation.
• Implementation: Implemented in Python.

Scientific Applications:

• Gene regulation studies: Analyzes the role of LREs in gene regulation across eukaryotic species.
• Expression modeling: Enables prediction of gene expression from long regulatory regions.
• Evolutionary studies: Investigates conservation and divergence of regulatory elements across species.
• Plasmodium falciparum research: Applied to Plasmodium falciparum to reveal conserved and dynamic LREs and to study regulation in a genome depleted of transcription factors.
• Developmental and life-cycle analysis: Studies developmental dynamics and life-cycle–specific regulation, including embryonic development and stage-specific regulation in Apicomplexa.
• Experimental validation support: Supports integration with experimental validation such as in vivo reporter assays (used to validate biological relevance in P. falciparum).

Methodology:

Implemented in Python and applies a systematic computational approach to detect and analyze LREs and to predict gene expression from these regions.