Considerations for Optimization of High-Throughput Sequencing Bioinformatics Pipelines for Virus Detection

Area of Science:

• Bioinformatics
• Virology
• Genomics

Background:

• High-throughput sequencing (HTS) enables broad virus detection, identifying known and novel viruses across clinical, environmental, and biological samples.
• Optimizing HTS bioinformatics pipelines is essential for balancing sensitivity with computational efficiency in virus detection.

Purpose of the Study:

• To discuss critical considerations for optimizing HTS data analysis pipelines for virus detection.
• To address challenges in achieving adequate sensitivity at acceptable computational costs.

Main Methods:

• Review of critical bioinformatics pipeline steps: data quality assessment, trimming/cleaning, and assembly.
• Discussion on database curation, sequence alignment strategies, and host sequence removal.
• Emphasis on automated and manual evaluation for result verification and risk assessment.

Main Results:

• Identified key parameters for HTS pipeline optimization, including database completeness and alignment specificity.
• Highlighted the importance of efficient host sequence removal and robust reporting formats.
• Stressed the need for both automated and manual verification of HTS results.

Conclusions:

• Optimized HTS bioinformatics pipelines are vital for accurate and efficient broad virus detection.
• Careful selection of parameters, databases, and analysis methods ensures reliable results.
• Comprehensive evaluation strategies are necessary to validate findings and assess potential risks.