Prophage Hunter

Prophage Hunter identifies active prophages within bacterial whole genome assemblies to extract prophage genomes, evaluate prophage activity, and support studies of phage–bacteria coevolution, phage physiology and biochemistry, and phage engineering.

Key Features:

• Integrative scoring system: Combines sequence similarity-based matching with genetic features-based machine learning classification to predict active prophages.
• Customized phage parts library: Uses a curated phage parts library for sequence similarity matching to identify and evaluate prophage genomes.
• Machine learning classification: Applies machine learning on genetic features associated with prophage activity to improve prediction precision.
• Novelty discovery option: Provides an option to skip sequence similarity matching to increase detection of phages absent from existing databases.
• Prophage genome extraction: Extracts prophage genome sequences from bacterial whole genome assemblies.
• Prophage activity evaluation: Evaluates prophage activity using the integrated scoring approach.
• Phylogenetic identification and protein annotation: Identifies phylogenetically related phages and annotates phage protein functions.

Scientific Applications:

• Coevolution studies: Identifies active prophages to investigate evolutionary dynamics between phages and their bacterial hosts.
• Phage physiology and biochemistry: Supports analysis of biological and chemical properties of phages through prophage identification and annotation.
• Phage engineering: Informs design and development of therapeutic, industrial, or environmental phages by characterizing active prophages.

Methodology:

Performs sequence similarity-based matching against a customized phage parts library, applies genetic features-based machine learning classification, and integrates these analyses using a scoring system, with an option to omit similarity matching to detect novel phages.