Efficient detection of viral transmissions with Next-Generation Sequencing data

Area of Science:

• Epidemiology
• Molecular Biology
• Bioinformatics

Background:

• Hepatitis C virus (HCV) poses a significant global public health challenge.
• Investigating HCV outbreaks is complicated by transmission chains and the presence of minority variants within infected individuals.
• Next-Generation Sequencing enhances detection sensitivity but presents computational challenges for analyzing vast datasets.

Purpose of the Study:

• To develop a computational strategy for efficient and accurate detection of HCV transmission links.
• To address the computational burden associated with analyzing large-scale sequencing data in HCV outbreak investigations.

Main Methods:

• A three-step filtering strategy was developed, incorporating a k-mer bloom filter, a Levenshtein filter, and an identical sequence filter.
• This approach was applied to a diverse set of HCV samples, representing various genetic relationships and subtypes.
• The filters were designed to rapidly reduce the number of pairwise sample and sequence comparisons.

Main Results:

• The three-step filtering strategy effectively removed 85.1% of pairwise sample comparisons and 91.0% of pairwise sequence comparisons.
• The method accurately identified HCV sample pairs falling below a defined relatedness threshold for transmission.
• Significant reduction in computational load was achieved while maintaining accuracy in transmission link establishment.

Conclusions:

• A fast and efficient three-step filtering strategy has been presented for analyzing HCV sequence data.
• This workflow substantially reduces sequence comparisons, enabling accurate identification of transmission links.
• The improved efficiency enhances molecular detection capacity, facilitating a faster response to viral transmissions.