Treemble: a graphical tool to generate Newick strings from phylogenetic tree images.

Area of Science:

• Computational Biology
• Bioinformatics
• Evolutionary Biology

Background:

• Phylogenetic trees are crucial in biology but often published as inaccessible diagrams.
• Manual conversion to Newick format is time-consuming and error-prone.
• Existing methods struggle with complex tree diagrams and annotations.

Purpose of the Study:

• To introduce Treemble, a user-friendly desktop application for generating Newick strings from tree images.
• To overcome limitations of previous semi-automated tree image analysis.
• To enable follow-up analyses and supertree construction from published phylogenetic trees.

Main Methods:

• Treemble utilizes a deep learning-based node detection tool.
• Users mark node locations, and the algorithm assembles the tree structure.
• The application automatically reads tip name labels for rectangular and circular trees.

Main Results:

• Treemble successfully generates machine-readable Newick strings from tree images.
• The deep learning approach assists in accurate node detection.
• Tip labels are automatically extracted, streamlining data capture.

Conclusions:

• Treemble provides an efficient solution for converting visual phylogenetic trees into Newick format.
• This facilitates wider accessibility and usability of published tree data.
• The application supports diverse tree visualizations, enhancing its utility in evolutionary studies.