Computational framework for next-generation sequencing of heterogeneous viral populations using combinatorial pooling

Area of Science:

• Genomics
• Virology
• Bioinformatics

Background:

• Next-generation sequencing (NGS) enables large-scale molecular surveillance of viral diseases.
• Traditional NGS protocols are cost and labor-intensive for analyzing numerous samples.
• Combinatorial pooling offers a cost-effective alternative but is challenging for heterogeneous viral populations.

Purpose of the Study:

• To develop a cost-effective and reliable protocol for sequencing viral samples.
• To address the limitations of existing pooling strategies for highly heterogeneous viral populations.
• To reduce the cost and labor associated with large-scale viral molecular surveillance.

Main Methods:

• Developed a protocol combining NGS with barcoding and combinatorial pooling.
• Created a computational framework with algorithms for optimal virus-specific pool design.
• Implemented algorithms for deconvolution of individual viral samples from pooled sequencing data.

Main Results:

• The developed framework substantially reduces sequencing costs for viral samples.
• The protocol allows for accurate deconvolution of individual viral populations from pooled data.
• Evaluation on hepatitis C virus data demonstrated high accuracy and cost-effectiveness.

Conclusions:

• The novel NGS protocol and computational framework offer a significant advancement in viral surveillance.
• This approach provides a cost-effective and reliable solution for analyzing complex viral populations.
• The methodology is applicable to large-scale molecular surveillance of viral diseases.