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Pseudo-likelihood for Non-reversible Nucleotide Substitution Models with Neighbour Dependent Rates

  • Ole F. Christensen
Published/Copyright: July 31, 2006
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Statistical Applications in Genetics and Molecular Biology
From the journal Statistical Applications in Genetics and Molecular Biology Volume 5 Issue 1

In the field of molecular evolution genome substitution models with neighbour dependent substitution rates have recently received much attention. It is well-known that substitution of nucleotides does not occur independently of neighbouring nucleotides, but there has been less focus on the phenomenon that this substitution process is also not time-reversible. In this paper I construct a pseudo-likelihood type method for inference in non-reversible substitution models with neighbour dependent substitution rates. I also construct an EM-algorithm for maximising the pseudo-likelihood. For human-mouse aligned sequence data a number of different models are investigated, where I show that strand-symmetric models are appropriate, and that overlapping di-nucleotide models do not fit the data well.

Keywords: EM-algorithm; Markov processes; maximum likelihood; pseudo-likelihood
Published Online: 2006-7-31

©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston

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  16. Model Selection for Mixtures of Mutagenetic Trees
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  18. A Method to Increase the Power of Multiple Testing Procedures Through Sample Splitting
  19. Bayesian Hierarchical Model for Correcting Signal Saturation in Microarrays Using Pixel Intensities
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  25. Numerical Solutions for Patterns Statistics on Markov Chains
  26. A Heuristic Bayesian Method for Segmenting DNA Sequence Alignments and Detecting Evidence for Recombination and Gene Conversion
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  28. Validation in Genomics: CpG Island Methylation Revisited
  29. An Improved Nonparametric Approach for Detecting Differentially Expressed Genes with Replicated Microarray Data
  30. Letter to the Editor
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https://doi.org/10.2202/1544-6115.1217
Keywords for this article
EM-algorithm; Markov processes; maximum likelihood; pseudo-likelihood

Articles in the same Issue

  1. Article
  2. Low-Order Conditional Independence Graphs for Inferring Genetic Networks
  3. A Generalized Clustering Problem, with Application to DNA Microarrays
  4. A Bayes Regression Approach to Array-CGH Data
  5. Statistical Selection of Maintenance Genes for Normalization of Gene Expressions
  6. Predicting the Strongest Domain-Domain Contact in Interacting Protein Pairs
  7. Dimension Reduction for Classification with Gene Expression Microarray Data
  8. A New Type of Stochastic Dependence Revealed in Gene Expression Data
  9. A New Order Estimator for Fixed and Variable Length Markov Models with Applications to DNA Sequence Similarity
  10. Quality Optimised Analysis of General Paired Microarray Experiments
  11. Issues of Processing and Multiple Testing of SELDI-TOF MS Proteomic Data
  12. Cross-Validated Bagged Prediction of Survival
  13. Treatment of Uninformative Families in Mean Allele Sharing Tests for Linkage
  14. Quantile-Function Based Null Distribution in Resampling Based Multiple Testing
  15. Combining Results of Microarray Experiments: A Rank Aggregation Approach
  16. Model Selection for Mixtures of Mutagenetic Trees
  17. Pseudo-likelihood for Non-reversible Nucleotide Substitution Models with Neighbour Dependent Rates
  18. A Method to Increase the Power of Multiple Testing Procedures Through Sample Splitting
  19. Bayesian Hierarchical Model for Correcting Signal Saturation in Microarrays Using Pixel Intensities
  20. Using Complexity for the Estimation of Bayesian Networks
  21. Detecting Local High-Scoring Segments: a First-Stage Approach for Genome-Wide Association Studies
  22. Examining Protein Structure and Similarities by Spectral Analysis Technique
  23. Parameter Estimation for the Exponential-Normal Convolution Model for Background Correction of Affymetrix GeneChip Data
  24. Approximate Sample Size Calculations with Microarray Data: An Illustration
  25. Numerical Solutions for Patterns Statistics on Markov Chains
  26. A Heuristic Bayesian Method for Segmenting DNA Sequence Alignments and Detecting Evidence for Recombination and Gene Conversion
  27. A Two-Step Multiple Comparison Procedure for a Large Number of Tests and Multiple Treatments
  28. Validation in Genomics: CpG Island Methylation Revisited
  29. An Improved Nonparametric Approach for Detecting Differentially Expressed Genes with Replicated Microarray Data
  30. Letter to the Editor
  31. Treating Expression Levels of Different Genes as a Sample in Microarray Data Analysis: Is it Worth a Risk?
  32. Reader's Reaction
  33. Reader's Reaction to "Dimension Reduction for Classification with Gene Expression Microarray Data" by Dai et al (2006)
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