Separation and assembly of deep sequencing data into discrete sub-population genomes

Area of Science:

• Virology
• Bioinformatics
• Genomics

Background:

• RNA viruses exhibit sequence heterogeneity, forming diverse sub-populations or quasispecies.
• Existing short-read assembly methods often overlook this crucial viral diversity.
• Understanding viral heterogeneity is vital for diagnostics and treatment.

Purpose of the Study:

• To develop a novel algorithm for simultaneous assembly of multiple discrete viral genomes from mixed populations.
• To assess the algorithm's sensitivity in detecting low-frequency viral sub-populations.
• To enable comprehensive genomic analysis of heterogeneous viral samples.

Main Methods:

• Development of a novel bioinformatics algorithm for simultaneous genome assembly.
• In silico testing with simulated data to determine detection limits and reconstruction accuracy.
• Application of the algorithm to high-throughput sequencing data from sewage and patient samples.

Main Results:

• The algorithm successfully detected viral populations at frequencies as low as 0.1%.
• Complete global genome reconstruction was achieved with high accuracy, detecting up to 16 resolved sequences in a single sample.
• Multiple sub-populations and recombination events were identified in complex environmental and clinical samples.

Conclusions:

• The novel algorithm effectively reconstructs diverse viral genomes from mixed populations.
• High sensitivity allows for accurate detection of intra-host viral diversity, supporting personalized medicine.
• This approach is critical for advanced diagnostics, drug therapy monitoring, and clinical decision-making.