Refinement of the Reference Viral Database (RVDB) for improving bioinformatics analysis of virus detection by...

Area of Science:

• Virology
• Bioinformatics
• Biotechnology

Background:

• Biological products require rigorous testing for adventitious viruses (AVs).
• Current in vitro and in vivo virus detection assays are time-consuming, laborious, and face limitations in broad detection.
• The 3Rs initiative discourages animal use, necessitating alternative methods.

Purpose of the Study:

• To describe improvements in the Reference Viral Database (RVDB) for enhanced high-throughput sequencing (HTS) based adventitious virus detection.
• To refine HTS bioinformatics pipelines for more accurate and efficient virus detection in biological products.
• To provide an updated, comprehensive viral sequence database for safety testing.

Main Methods:

• Transitioned RVDB production scripts from Python 2 to Python 3.
• Updated the semantic pipeline to remove misannotated and irrelevant sequences, including phages.
• Implemented quality checks for SARS-CoV-2 genomes and enhanced website search functionalities.

Main Results:

• The refined RVDB demonstrates reduced cellular sequence content and improved annotation accuracy.
• Updated production scripts facilitate reliable and timely database generation.
• Enhanced bioinformatics pipelines are expected to reduce computational time and increase accuracy in virus detection.

Conclusions:

• Refined RVDB enhances HTS capabilities for adventitious virus detection in biological products.
• The updated database and bioinformatics pipeline offer a more accurate and efficient alternative to traditional methods.
• These advancements support the development of safer biological products and align with the 3Rs initiative.