Seeker

Seeker identifies bacteriophage genomes in metagenomic datasets using Long Short-Term Memory (LSTM) deep learning models in an alignment-free, reference-free approach.

Key Features:

• Reference-Free Identification: Classifies sequences without reliance on reference genomes, enabling detection independent of existing databases.
• Alignment-Free Methodology: Operates without sequence alignment, reducing computational overhead and enabling rapid analysis of raw sequence data.
• LSTM Deep Learning: Utilizes Long Short-Term Memory (LSTM) recurrent neural networks to capture sequence patterns that distinguish phage and bacterial genomes.
• Sensitivity to Divergent Phages: Detects novel and highly divergent bacteriophage sequences with low similarity to known phage families.
• Scalable Processing: Alignment-free LSTM models enable efficient processing of large metagenomic datasets.

Scientific Applications:

• Metagenomic Phage Discovery: Identification of bacteriophage sequences within environmental and host-associated metagenomes.
• Viral Diversity and Ecology: Characterization of phage diversity, ecology, and evolutionary relationships.
• Clinical and Environmental Surveillance: Detection of rapidly evolving viral sequences in clinical or environmental samples.
• Biotechnology and Medical Research: Support for discovery of phages relevant to biotechnology and therapeutic development.

Methodology:

Seeker employs Long Short-Term Memory (LSTM) deep learning models trained on DNA sequence data to identify phage sequences in an alignment-free, reference-free manner.