Distribution-based modeling of Sequence space time dynamics (DISSEQT)

Distribution-based modeling of Sequence space time dynamics (DISSEQT) models the evolution of heterogeneous biological populations in multidimensional genetic spaces to analyze, visualize, and predict population dynamics from deep sequencing and high-throughput data.

Key Features:

• Population-based modeling of sequencing data: Handles population-level analyses of deep sequencing and high-throughput data to characterize heterogeneous biological populations.
• Dimensionality and model reduction: Employs advanced dimension and model reduction algorithms to simplify complex genotypic data while preserving evolutionary signals.
• Integration of phenotypic data: Incorporates phenotypic features to enable exploration of dynamic genotype-phenotype maps.
• Visualization of sequence-space trajectories and fitness landscapes: Visualizes evolutionary trajectories in sequence space and genotype-phenotype fitness landscapes to reveal population dynamics.
• Empirical reconstruction of evolutionary trajectories: Reconstructs empirical evolutionary paths of populations within their genetic spaces.
• Incorporation of minority variants for improved prediction: Integrates minority genotypes/variants into empirical fitness landscapes to enhance phenotype prediction accuracy from genotypic data.
• Identification of low-dimensional genetic spaces: Detects low-dimensional representations within complex genetic systems to pinpoint critical factors driving evolution.

Scientific Applications:

• RNA virus population dynamics: Analyzes rapidly evolving RNA virus populations that exhibit high genetic heterogeneity using sequencing data.
• Microbial evolution and virology: Investigates evolutionary processes and drivers in microbial communities relevant to virology and microbial evolution research.
• Genotype-phenotype mapping and prediction: Supports mapping genotype-phenotype relationships and predicting phenotypes from genotypes, including contributions of minority variants.

Methodology:

Workflow steps explicitly include read alignment through result visualization.