Detection of viral contamination in cell lines using ViralCellDetector

Area of Science:

• Biomedical research
• Genomics
• Bioinformatics

Background:

• Cell lines are crucial for biomedical research but susceptible to viral contamination.
• Detecting viral contamination in cell lines is challenging compared to bacterial or mycoplasma.
• Contaminated cell lines can compromise experimental results and reproducibility.

Purpose of the Study:

• To develop a robust tool for detecting viral contamination in RNA-sequencing (RNA-seq) data.
• To provide a method for identifying viral infections in cell lines across various host species.
• To enhance the reliability of cell line-based research by enabling accurate contamination detection.

Main Methods:

• Developed ViralCellDetector, a tool that maps RNA-seq reads to a viral genome library.
• Employs a two-step alignment process: first to the host genome, then to the viral database.
• Incorporates a machine learning model trained on host gene expression markers for enhanced viral infection classification.

Main Results:

• Approximately 10% of tested MCF7 cell line RNA-seq datasets showed viral contamination.
• ViralCellDetector demonstrated high sensitivity in detecting viral sequences.
• The machine learning model achieved an AUC of 0.91 and 0.93 accuracy in distinguishing infected human samples.

Conclusions:

• The mapping-based approach offers robust viral contamination detection across diverse host organisms.
• The marker-based machine learning model accurately identifies viral infections specifically in human cell lines.
• The tool aids researchers in avoiding contaminated cell lines, thereby improving experimental reliability.