Classifying the Lifestyle of Metagenomically-Derived Phages Sequences Using Alignment-Free Methods

Area of Science:

• Virology
• Bioinformatics
• Computational Biology

Background:

• Bacteriophages (phages) are classified as temperate or lytic based on their replication strategies.
• Metaviromic sequencing generates fragmented viral genomes, hindering experimental lifestyle determination.
• Existing alignment-based methods struggle with incomplete genomic data and databases.

Purpose of the Study:

• To develop and evaluate computational methods for predicting phage lifestyles from fragmented metagenomic data.
• To compare the efficacy of various alignment-free dissimilarity measures for phage lifestyle classification.
• To identify the optimal alignment-free approach for analyzing metavirome-derived phage contigs.

Main Methods:

• Simulated fragmentation of temperate and lytic phage genomes into non-overlapping sequences.
• Comprehensive comparison of nine alignment-free dissimilarity measures.
• Systematic evaluation of different k-mer lengths and Markov orders.
• Assessment of predictive performance for classifying phage lifestyles.

Main Results:

• The alignment-free dissimilarity measure, denoted as $$, demonstrated superior performance compared to other measures.
• This method effectively predicted phage lifestyles across various fragment lengths and k-mer parameters.
• The proposed method showed robustness in classifying phage contigs derived from simulated metagenomic data.

Conclusions:

• Alignment-free methods, particularly using the $$ dissimilarity measure, are suitable for predicting phage lifestyles from fragmented metagenomic data.
• This computational approach overcomes limitations of experimental methods and alignment-based tools.
• The findings provide a valuable tool for analyzing viral communities in complex environments.