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Efficient identification of phylogenetically informative alignment sites via sparse learning.
1Department of Genetics, Federal University of Rio de Janeiro, RJ, Brazil.
We developed a new method using sparse learning to identify key sites in genetic data for accurate evolutionary tree reconstruction. This approach efficiently pinpoints phylogenetically informative sites, improving phylogenomic analyses.
Area of Science:
- Phylogenetics and Evolutionary Biology
- Computational Biology
- Genomics
Background:
- Accurate phylogenetic tree reconstruction relies on identifying phylogenetically informative sites in multiple sequence alignments.
- Current methods often depend on predefined topologies or heuristics, limiting their applicability and interpretability.
Purpose of the Study:
- To develop a topology-agnostic framework for quantifying site-wise phylogenetic information.
- To identify the minimal subset of sites crucial for phylogenetic signal using sparse learning.
Main Methods:
- Employed sparse learning via Lasso (Least Absolute Shrinkage and Selection Operator) regression.
- Modeled site log-likelihoods as predictors of tree likelihood across random topologies.
- Validated using simulated and empirical mammalian datasets.
Main Results:
- Lasso-selected sites produced tree topologies nearly identical to those from full alignments.
- An entropy-based proxy effectively approximated Lasso results for computational efficiency.
- Demonstrated the identification of a minimal subset of phylogenetically informative sites.
Conclusions:
- Sparse learning offers a principled, scalable, and practical method for assessing and optimizing phylogenetic data.
- The developed framework provides an objective metric for phylogenetically informative sites.
- This approach enhances efficiency and accuracy in phylogenomic analyses.

