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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

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Constrained incremental tree building: new absolute fast converging phylogeny estimation methods with improved

Qiuyi Zhang¹, Satish Rao², Tandy Warnow³

¹1Department of Mathematics, University of California Berkeley, Evans Hall, Berkeley, CA 94720 USA.

Algorithms for Molecular Biology : AMB

|March 7, 2019

Summary

This study introduces a new, scalable method for phylogenetic tree estimation. The approach is absolute fast converging (AFC), uses polynomial time and space, and improves accuracy for large datasets.

Keywords:

Absolute fast converging methods Maximum likelihood Neighbor joining Phylogeny estimation Sample complexity Short quartets

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Area of Science:

Computational Biology
Bioinformatics
Evolutionary Biology

Background:

Absolute fast converging (AFC) phylogeny estimation methods guarantee accurate tree recovery with sufficient sequence length.
Existing AFC methods, like the one from SODA 2001, use neighbor joining (NJ) on subsets but struggle with scalability due to supertree reliance.
Current supertree methods lack the accuracy and scalability required for large phylogenetic datasets.

Purpose of the Study:

To develop a novel, scalable approach for large-scale phylogeny estimation.
To create an AFC method that bypasses the limitations of supertree methods.
To enhance the accuracy and applicability of phylogenetic tree estimation for ultra-large datasets.

Main Methods:

A new AFC phylogeny estimation approach is presented, designed for scalability.
The method is proven to operate in polynomial time and space.
Variations incorporate leaf-disjoint constraint trees (e.g., from maximum likelihood) for improved accuracy.

Main Results:

The new approach is demonstrated to be absolute fast converging (AFC).
The method achieves polynomial time and space complexity, enabling scalability.
Incorporating constraint trees shows potential for even greater accuracy.

Conclusions:

A generalizable technique for large-scale phylogeny estimation has been developed.
The method significantly improves scalability for ultra-large datasets.
This approach is versatile, applicable to unaligned sequences and species tree estimation from gene trees.