GPSit: An automated method for evolutionary analysis of nonculturable ciliated microeukaryotes

Area of Science:

• Microbiology
• Bioinformatics
• Evolutionary Biology

Background:

• Microeukaryotes are crucial to global food webs in aquatic and terrestrial ecosystems.
• Understanding microeukaryote roles requires robust evolutionary and classification frameworks.
• Phylogenomic analysis, while powerful, demands significant bioinformatics expertise.

Purpose of the Study:

• To develop an efficient, automated method for microeukaryote phylogenomic analysis.
• To streamline the process of reconstructing evolutionary history and classification.
• To reduce the manual operational time for phylogenetic analyses.

Main Methods:

• Introduction of the Guided Phylogenomic Search in trees (GPSit) automated workflow.
• Integration of essential and optional operations for phylogenomic reconstruction.
• Application of GPSit to analyze single-cell data from marine ciliates and evaluate missing data impacts.

Main Results:

• GPSit significantly reduces manual operation time from days to hours.
• The method effectively reconstructs deep phylogenetic relationships using diverse datasets.
• Bayesian inference (BI) demonstrates less sensitivity to missing data compared to maximum likelihood (ML) when fast-evolving sites are removed.

Conclusions:

• GPSit offers an efficient and adaptable solution for microeukaryote phylogenomic studies.
• The tool enhances accessibility to complex phylogenetic analyses.
• Findings provide insights into the impact of missing data on phylogenetic methods.