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Efficient approximations for learning phylogenetic HMM models from data.

Vladimir Jojic¹, Nebojsa Jojic, Chris Meek

¹Microsoft Research, Redmond, WA 98052, USA. vjojic@psi.toronto.edu

Bioinformatics (Oxford, England)

|July 21, 2004

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Variational methods accurately approximate phylogenetic-HMM model likelihoods, offering a lower bound. A specific variational approach identified a CpG effect in mammalian DNA sequence evolution.

Area of Science:

Computational Biology
Evolutionary Biology
Genomics

Background:

Phylogenetic-HMM models are crucial for inferring evolutionary trees from DNA sequence data.
These models generalize classical probabilistic models but are computationally intractable.
Existing approximations include Siepel and Haussler's method, loopy belief propagation, and variational methods.

Purpose of the Study:

To evaluate and compare different approximation methods for computing the likelihood of phylogenetic-HMM models.
To identify accurate and reliable methods for phylogenetic inference.
To apply an accurate approximation to real biological data.

Main Methods:

Investigated several approximation techniques for phylogenetic-HMM model likelihood computation.

Related Experiment Videos

Focused on variational methods, including a novel 'best-one' approximation.

Applied the best variational approximation to DNA sequence data from nine eutherian mammals.

Main Results:

Variational methods provide accurate lower bounds for phylogenetic-HMM model likelihoods.
The 'best-one' variational approximation, using the Neyman-Felsenstein model, proved most effective.
Analysis of mammalian DNA sequences revealed a significant CpG effect.

Conclusions:

Variational methods are a viable and accurate approach for intractable phylogenetic-HMM likelihood computations.
The identified 'best-one' approximation facilitates robust phylogenetic inference.
The study highlights the utility of these methods in uncovering biological patterns, such as CpG effects in mammalian evolution.