virSearcher

virSearcher identifies bacteriophage sequences in metagenomic assemblies by encoding coding and non-coding regions into word-embedding codes and applying convolutional neural networks combined with gene hit-ratio information to improve detection across short and long contigs.

Key Features:

• Integration of Convolutional Neural Networks (CNN): Input sequences are encoded to reflect coding versus non-coding regions, transformed into word embedding codes via a dedicated layer, and analyzed by CNNs for pattern recognition.
• Gene Information Utilization: The method evaluates complete and incomplete genes and integrates gene-derived features, including the hit ratio of virus-specific genes, with CNN outputs to refine predictions.
• Performance Optimization: Empirical evaluation on multiple metagenomic datasets improves identification accuracy for short contigs while maintaining efficacy on longer sequences.

Scientific Applications:

• Phage ecology and diversity: Identification of bacteriophages in human and environmental metagenomes to study phage ecological and biological roles.
• Microbial community analysis: Detection of phages to assess their influence on microbial community composition and dynamics.
• Host–pathogen interaction studies: Recovery of phage sequences to investigate phage–host interactions.
• Biotechnology and medical applications: Recovery of phage sequences to support applications in biotechnology and medicine.

Methodology:

Input sequences are encoded to reflect coding versus non-coding regions, transformed into word embedding codes via a dedicated layer, processed by convolutional neural network analysis, and combined with gene hit-ratio information from complete and incomplete genes.