QuasiRecomb

QuasiRecomb analyzes viral quasispecies of RNA viruses to infer strain composition and detect recombination from next-generation sequencing data.

Key Features:

• Jumping Hidden Markov Model (JHMM): Employs a jumping hidden Markov model that uses position-specific probability tables over the sequence alphabet to model generation of viral quasispecies.
• Recombination Event Detection: Detects recombination events as state changes in the model, allowing attribution of a single observed read to multiple source sequences.
• Parameter Inference Using Next-Generation Sequencing Data: Infers population parameters and strain prevalence from next-generation sequencing (NGS) data.
• Expectation Maximization (EM) Algorithm Implementation: Implements an expectation maximization algorithm to compute maximum a posteriori (MAP) estimates of model parameters.

Scientific Applications:

• Virology research (RNA viruses): Characterizes genetic diversity and evolutionary dynamics of RNA virus quasispecies by modeling both point mutations and recombination.
• Clinical sample analysis (HIV): Applied to clinical samples such as HIV to estimate strain composition and detect recombinant sequences.
• Model validation with simulated data: Validates the importance of explicitly modeling recombination processes using simulated sequencing data.

Methodology:

Uses a jumping hidden Markov model with position-specific probability tables and state changes for recombination detection, and applies an expectation maximization algorithm to NGS data to obtain maximum a posteriori parameter estimates.