TreeTuner: A pipeline for minimizing redundancy and complexity in large phylogenetic datasets

Area of Science:

• Bioinformatics
• Computational Biology
• Phylogenetics

Background:

• Manual reduction of operational taxonomic units (OTUs) in phylogenetic datasets is time-consuming.
• Existing bioinformatics protocols often require significant manual intervention for OTU trimming.
• Large protein sequence datasets pose challenges for efficient phylogenetic analysis.

Purpose of the Study:

• To introduce TreeTuner, a semiautomated pipeline for optimizing large protein sequence phylogenetic datasets.
• To enable both coarse and fine-scale tuning of datasets by minimizing OTU redundancy.
• To facilitate preliminary and rigorous downstream analysis of specific OTU subsets.

Main Methods:

• Development of a semiautomated bioinformatics pipeline named TreeTuner.
• Implementation of algorithms for coarse and fine-scale tuning of phylogenetic datasets.
• Focus on minimizing operational taxonomic unit (OTU) redundancy in protein sequence data.

Main Results:

• TreeTuner allows for efficient reduction of OTUs in large phylogenetic datasets.
• The pipeline facilitates both initial exploration and in-depth analysis of specific OTU groups.
• Minimization of OTU redundancy enhances the usability of large phylogenetic datasets.

Conclusions:

• TreeTuner offers a significant advancement in the semiautomated analysis of large phylogenetic datasets.
• The tool streamlines the process of OTU reduction, saving researchers time and effort.
• TreeTuner supports more robust downstream analyses by providing optimized datasets.