Enhanced Viral Metagenomics with Lazypipe 2

Area of Science:

• Virology
• Bioinformatics
• Genomics

Background:

• Emerging infectious diseases are primarily caused by viruses, necessitating robust detection methods.
• Metagenomic next-generation sequencing (mNGS) offers a powerful approach for unbiased virus discovery across various sample types.
• Effective bioinformatic analysis is crucial for interpreting complex mNGS data and identifying viral sequences.

Purpose of the Study:

• To introduce Lazypipe 2, an improved bioinformatics pipeline for mNGS data analysis.
• To enhance code stability, transparency, and functionality for virus detection.
• To benchmark Lazypipe 2's performance against existing pipelines and evaluate different detection strategies.

Main Methods:

• Development and update of the Lazypipe 2 mNGS bioinformatics pipeline.
• Benchmarking using simulated canine metagenomes and real-world samples with varying viral loads.
• Evaluation of virus detection accuracy using nucleotide and amino acid homology searches.

Main Results:

• Lazypipe 2 demonstrates significant improvements in code stability and transparency over its predecessor.
• The pipeline achieves high precision and recall in virus detection, even with low viral genetic material proportions.
• Nucleotide-based annotation in Lazypipe 2 shows near-perfect detection for eukaryotic viruses, outperforming other pipelines.

Conclusions:

• Lazypipe 2 provides a stable, transparent, and highly accurate bioinformatics solution for virus detection via mNGS.
• Nucleotide-based approaches are highly effective for detecting known eukaryotic viruses.
• Amino acid homology searches are vital for discovering novel and highly divergent viruses.