Addressing the Shortcomings of Three Recent Bayesian Methods for Detecting Interspecific Recombination in DNA Sequence Alignments
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Dirk Husmeier
We address a potential shortcoming of three probabilistic models for detecting interspecific recombination in DNA sequence alignments: the multiple change-point model (MCP) of Suchard et al. (2003), the dual multiple change-point model (DMCP) of Minin et al. (2005), and the phylogenetic factorial hidden Markov model (PFHMM) of Husmeier (2005). These models are based on the Bayesian paradigm, which requires the solution of an integral over the space of branch lengths. To render this integration analytically tractable, all three models make the same assumption that the vectors of branch lengths of the phylogenetic tree are independent among sites. While this approximation reduces the computational complexity considerably, we show that it leads to the systematic prediction of spurious topology changes in the Felsenstein zone, that is, the area in the branch lengths configuration space where maximum parsimony consistently infers the wrong topology due to long-branch attraction. We apply two Bayesian hypothesis tests, based on an inter- and an intra-model approach to estimating the marginal likelihood. We then propose a revised model that addresses these shortcomings, and compare it with the aforementioned models on a set of synthetic DNA sequence alignments systematically generated around the Felsenstein zone.
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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Articles in the same Issue
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- Self-Organizing Maps with Statistical Phase Synchronization (SOMPS) for Analyzing Cell Cycle-Specific Gene Expression Data
- Coalescent Time Distributions in Trees of Arbitrary Size
- Quantifying the Association between Gene Expressions and DNA-Markers by Penalized Canonical Correlation Analysis
- Nonparametric Functional Mapping of Quantitative Trait Loci Underlying Programmed Cell Death
- Accommodating Uncertainty in a Tree Set for Function Estimation
- Drifting Markov Models with Polynomial Drift and Applications to DNA Sequences
- Comparing the Characteristics of Gene Expression Profiles Derived by Univariate and Multivariate Classification Methods
- Calculating Confidence Intervals for Prediction Error in Microarray Classification Using Resampling
- Structure Learning in Nested Effects Models
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- Adapting Prediction Error Estimates for Biased Complexity Selection in High-Dimensional Bootstrap Samples
- Adaptive Choice of the Number of Bootstrap Samples in Large Scale Multiple Testing
- Re-Cracking the Nucleosome Positioning Code
- Semi-Parametric Differential Expression Analysis via Partial Mixture Estimation
- A SNP Streak Model for the Identification of Genetic Regions Identical-by-descent
- Detecting Two-Locus Gene-Gene Effects Using Monotonisation of the Penetrance Matrix
- Modeling DNA Methylation in a Population of Cancer Cells
- Phenotyping Genetic Diseases Using an Extension of µ-Scores for Multivariate Data
- The Estimator of the Optimal Measure of Allelic Association: Mean, Variance and Probability Distribution When the Sample Size Tends to Infinity
- Predicting Protein Concentrations with ELISA Microarray Assays, Monotonic Splines and Monte Carlo Simulation
- A Comparison of Normalization Techniques for MicroRNA Microarray Data
- Collapsing SNP Genotypes in Case-Control Genome-Wide Association Studies Increases the Type I Error Rate and Power
- Estimating Number of Clusters Based on a General Similarity Matrix with Application to Microarray Data
- Data Distribution of Short Oligonucleotide Expression Arrays and Its Application to the Construction of a Generalized Intellectual Framework
- Approximately Sufficient Statistics and Bayesian Computation
- A Composite-Conditional-Likelihood Approach for Gene Mapping Based on Linkage Disequilibrium in Windows of Marker Loci
- Statistical Methods in Integrative Analysis for Gene Regulatory Modules
- Reducing Spatial Flaws in Oligonucleotide Arrays by Using Neighborhood Information
- Pattern Classification of Phylogeny Signals
- A Unification of Multivariate Methods for Meta-Analysis of Genetic Association Studies
- Importance Sampling for the Infinite Sites Model
- Supervised Distance Matrices
- Addressing the Shortcomings of Three Recent Bayesian Methods for Detecting Interspecific Recombination in DNA Sequence Alignments
- A Sparse PLS for Variable Selection when Integrating Omics Data
- Software Communication
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