QS-Net

QS-Net reconstructs phylogenetic networks from quartet and sextet information to elucidate evolutionary relationships involving reticulate events such as horizontal gene transfer, hybridization, recombination, and reassortment.

Key Features:

• Quartet and Sextet-Based Approach: Integrates quartet (four-taxon) and sextet (six-taxon) information to infer phylogenetic networks.
• Reticulate Event Modeling: Explicitly targets reticulate events including horizontal gene transfer, hybridization, recombination, and reassortment.
• Performance in Complex Networks: Maintains high accuracy as network complexity increases, including scenarios with multiple simultaneous reticulate events.
• Comparative Accuracy: Demonstrates comparable accuracy to Neighbor-Joining, Split-Decomposition, Neighbor-Net, and Quartet-Net for tree-like histories and superior accuracy for networks with reticulate events.
• Data Compatibility: Operates on both artificial sequence data simulated from evolutionary trees and networks and on real biological whole-genome sequence datasets.

Scientific Applications:

• Artificial Data Evaluation: Tested on artificial sequence data derived from an evolutionary tree, a network with three reticulate events, and a network with five reticulate events to assess robustness.
• Bacterial Taxonomy Analysis: Applied to a bacterial taxonomy dataset comprising 36 species, recovering established taxonomies and revealing novel reticulate signals.
• Influenza A H7N9 Genomics: Applied to whole-genome sequences of 22 H7N9 influenza A viruses to infer evolutionary patterns and identify novel reticulate events.

Methodology:

Integrates quartet and sextet data to infer phylogenetic networks and is applied to both simulated artificial sequence data and real biological sequence datasets.