DISSEQT-DIStribution-based modeling of SEQuence space Time dynamics.

Area of Science:

• Microbial evolution
• Computational biology
• Population genetics

Background:

• Modeling rapidly evolving microbial populations is challenging due to their dynamic and complex nature.
• High-throughput sequencing data generates vast amounts of genetic information that requires sophisticated analysis tools.

Purpose of the Study:

• To develop a computational pipeline (DISSEQT) for analyzing, visualizing, and predicting the evolution of heterogeneous biological populations.
• To enable population-based modeling of deep sequencing and high-throughput data for microbial evolution studies.

Main Methods:

• The DISSEQT pipeline integrates read alignment, dimension reduction, and model reduction algorithms.
• It analyzes genotypic data and incorporates phenotypic features to create dynamic genotype-phenotype maps.
• The pipeline was applied to evolving RNA virus populations to reconstruct evolutionary trajectories.

Main Results:

• DISSEQT successfully analyzes heterogeneous microbial populations and visualizes evolutionary dynamics.
• The relevant genetic space for evolving microbial populations was found to be intrinsically low-dimensional.
• Minority genotypes contributing to evolution were identified, and empirical fitness landscapes accurately predicted phenotype from genotype.

Conclusions:

• The DISSEQT pipeline provides a robust framework for studying microbial evolution from complex genetic data.
• Understanding the low-dimensional genetic space is key to modeling microbial population dynamics.
• Incorporating minority variants into fitness landscape reconstruction enhances predictive accuracy.