Phage hunters: Computational strategies for finding phages in large-scale 'omics datasets

Area of Science:

• Genomics
• Virology
• Bioinformatics

Background:

• Numerous tools exist for phage sequence identification across diverse genomic datasets.
• Distinguishing viral from bacterial signals is difficult due to intertwined phage-host genetics.
• Modern 'omics datasets present computational challenges due to their size, quantity, and fragmentary nature.

Purpose of the Study:

• To evaluate the strengths and weaknesses of current gene-centric and k-mer based tools.
• To assess the utility of these tools for identifying prophage sequences in genomes.
• To examine their application in detecting prophage and viral contigs within metagenomes.

Main Methods:

• Review and analysis of existing gene-centric tools for phage identification.
• Evaluation of k-mer based approaches for viral sequence detection.
• Assessment of tool performance on various 'omics datasets, including genomes and metagenomes.

Main Results:

• Gene-centric and k-mer based methods offer complementary approaches to phage identification.
• Each method has specific advantages and limitations depending on the dataset characteristics.
• The effectiveness of tools varies with the size, quantity, and fragmentation of the data.

Conclusions:

• Current tools provide valuable insights but have limitations in phage sequence identification.
• Understanding the promises and pitfalls of these methods is crucial for accurate analysis.
• Further development is needed to overcome computational challenges in phage genomics and metagenomics.