A dependent-rates model and an MCMC-based methodology for the maximum-likelihood analysis of sequences with...

Area of Science:

• Computational Biology
• Molecular Evolution
• Bioinformatics

Background:

• Analyzing DNA sequences with overlapping genes presents challenges due to complex evolutionary constraints.
• Existing models often fail to fully capture the intricate relationships imposed by the genetic code in dual reading frames.

Purpose of the Study:

• To develop a robust maximum-likelihood model for pairwise DNA sequence alignment with overlapping reading frames.
• To implement a Markov chain Monte Carlo (MCMC) procedure for parameter estimation and hypothesis testing in such models.

Main Methods:

• A novel substitution model incorporating neighborhood-dependent rates, considering both reading frames.
• Development of an MCMC procedure to estimate transition probabilities and perform likelihood ratio tests.
• Application to a pairwise alignment of hepatitis B sequences with overlapping genes.

Main Results:

• The proposed model successfully analyzes pairwise DNA alignments with overlapping genes.
• The MCMC procedure enables accurate parameter estimation and likelihood ratio tests.
• Analysis of hepatitis B sequences demonstrates the model's practical utility.

Conclusions:

• The developed model and methodology provide a powerful tool for studying molecular evolution in regions with overlapping genes.
• The approach effectively addresses the complexities introduced by dual reading frames and genetic code constraints.
• Further validation using an extended model confirms the adequacy of the primary model.