CONDOP

CONDOP predicts condition-dependent operons from RNA-seq transcriptome profiles and genomic sequence features using a Random Forest classifier to map operon structures in prokaryotic genomes.

Key Features:

• Condition-Specific Operon Prediction: Identifies operons that are expressed under specific environmental or experimental conditions based on transcriptome data.
• Integration of RNA-seq and Genomic Features: Combines RNA-seq transcriptome profiles with genomic sequence features to inform operon prediction.
• Random Forest Classifier: Employs a Random Forest machine learning classifier for operon prediction.
• Training on Confirmed Operons: Trains classifiers using a small set of confirmed operons as the labeled training data.
• Gene-Pair Classification and Operon Mapping: Classifies remaining consecutive gene pairs and links those classified as operons to generate condition-dependent operon maps.

Scientific Applications:

• Understanding Regulatory Networks: Maps conditionally expressed operons to aid elucidation of prokaryotic transcriptional and regulatory organization.
• Bacterial Genomics Research: Generates condition-dependent operon maps for bacterial species including Haemophilus somni, Porphyromonas gingivalis, Escherichia coli, and Salmonella enterica.

Methodology:

Integrates RNA-seq transcriptome profiles with genomic sequence features, trains a Random Forest classifier on a set of confirmed operons, classifies other consecutive gene pairs, and links classified gene pairs to produce condition-dependent operon maps.