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Updated: May 25, 2026

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

Accounting for alignment uncertainty in phylogenomics.

Martin Wu¹, Sourav Chatterji, Jonathan A Eisen

¹Department of Biology, University of Virginia, Charlottesville, Virginia, United States of America. mw4yv@virginia.edu

|January 25, 2012

Summary

This summary is machine-generated.

Uncertainty in protein sequence alignments affects phylogenetic tree accuracy. ZORRO, a new masking program, assigns confidence scores to alignment positions, significantly reducing uncertainty and improving tree accuracy.

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Last Updated: May 25, 2026

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

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Published on: August 14, 2018

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Published on: August 16, 2017

Area of Science:

Bioinformatics
Computational Biology
Phylogenetics

Background:

Multiple sequence alignment (MSA) uncertainty significantly impacts phylogenetic analysis accuracy.
Assessing the quality of individual positions within protein sequence alignments is crucial for reliable phylogenetic tree construction.
Limited tools exist for evaluating alignment position quality and addressing alignment uncertainty.

Purpose of the Study:

To introduce ZORRO, a novel probabilistic masking program designed to quantify alignment uncertainty.
To evaluate the effectiveness of ZORRO in improving phylogenetic tree accuracy by assigning confidence scores to alignment positions.

Main Methods:

Development of ZORRO, a probabilistic masking program.
Assigning confidence scores to each position within protein sequence alignments.
Utilizing the BALIBASE database and simulation studies for validation.

Main Results:

ZORRO effectively quantifies uncertainty at individual alignment positions.
Masking alignment uncertainty with ZORRO significantly improves the accuracy of phylogenetic trees.
Demonstrated improvement in tree accuracy using BALIBASE and simulation data.

Conclusions:

ZORRO provides a robust method for assessing and mitigating alignment uncertainty in phylogenetic analyses.
The program enhances the reliability of phylogenetic inference by focusing on high-confidence alignment positions.
This approach represents a significant advancement in improving the accuracy of evolutionary reconstructions from protein sequences.