Impact of Data Error on Phylogenetic Network Inference from Gene Trees Under the Multispecies Network Coalescent

Area of Science:

• Evolutionary biology
• Computational biology
• Bioinformatics

Background:

• Phylogenetic networks model complex evolutionary events like hybridization and horizontal gene transfer.
• Data errors (sequencing, alignment, gene tree estimation) pose a significant challenge to accurate phylogenetic network inference.
• Phylogenetic networks are prone to overfitting, especially with complex datasets and numerous parameters.

Purpose of the Study:

• To systematically examine the influence of various data error sources on phylogenetic network inference.
• To identify widely used methods vulnerable to data distortions and overfitting.
• To provide practical recommendations for minimizing the impact of data error on network inference.

Main Methods:

• Systematic examination of different data error sources.
• Analysis of the impact of gene tree estimation and sequence alignment inaccuracies.
• Evaluation of method performance when inferred reticulations exceed true reticulations.

Main Results:

• Many common phylogenetic network inference methods are vulnerable to data errors.
• Inaccuracies in gene tree estimation and sequence alignment significantly degrade network reliability.
• Overfitting occurs when algorithms infer more reticulations than are present in the true evolutionary history.

Conclusions:

• Data error is a critical factor that can lead to misrepresentation of evolutionary history in phylogenetic networks.
• Accounting for data error and optimizing data processing pipelines are essential for reliable network inference.
• Integrating realistic error models into species network inference methods is necessary for their improved accuracy and applicability.