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Mathematical Modelling of Tetranucleotide Usage Evolution in Bacterial Genomes for Improved Phylogenetic Inferences

This study aims to reconcile the inconsistencies in phylogenetic inferences by developing a new method based on mathematical modelling of the evolution of tetranucleotide usage patterns in bacterial genomes. The research also aims to determine the biological model for alignment and annotation methods and develop an online web-based tool for researchers. Case studies will include various bacterial groups, and the driving force of the observed evolutionary patterns will be investigated. The study is conducted at the Centre of Bioinformatics and Computational Biology, University of Pretoria.

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Mathematical Modelling of Tetranucleotide Usage Evolution in Bacterial Genomes for Improved Phylogenetic Inferences

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  1. Mathematical modelling of evolution of tetranucleotide usage patterns of whole bacterial genomes to improve phylogenetic inferences Centre of Bioinformatics and Computational Biology University of Pretoria Xiaoyu Yu

  2. Background Problem • Phylogenomics: Reconstruction of evolutionary relationships by comparing sequences of whole genomes or portions of genomes. • Prediction of gene function • Establishment and clarification of evolutionary relationships • Gene family evolution • Prediction and retracing lateral gene transfer

  3. Background Problem • Multiple algorithms available but falls short individually. • Inconsistency of results between algorithms. No consensus between results

  4. Pairwise distance plot

  5. Aim and Objectives • Reconcile MSA based and OUP based distances to solve inconsistency

  6. Aim and Objectives • Reconcile MSA based and OUP based distances to solve inconsistency • Creating new phylogenomic inferencing method to cover the shortfalls of individual algorithms

  7. Aim and Objectives • Reconcile MSA based and OUP based distances to solve inconsistency • Creating new phylogenomic inferencing method to cover the shortfalls of individual algorithms • Determine the biological model for the alignment and annotation method (OUP)

  8. Aim and Objectives • Reconcile MSA based and OUP based distances to solve inconsistency • Creating new phylogenomic inferencing method to cover the shortfalls of individual algorithms • Determine the biological model for the alignment and annotation method (OUP) • Develop online web based tool

  9. http://swphylo.bi.up.ac.za/

  10. Clustering

  11. Cladogram

  12. Cladogram

  13. Inferencing

  14. Inferencing

  15. Case Studies • Bacillus • Corynebacteria • Enterobacteria • Lactobacilli • Mycobacteria • Pseudomonas • Prochlorococcus • Thermatoga

  16. Current Limitations • Not all groups of organisms cluster well with current model

  17. Current Limitations • Not all groups of organisms cluster well with current model • Parameter still being tested to reconcile the best tree

  18. Current Limitations • Not all groups of organisms cluster well with current model • Parameter still being tested to reconcile the best tree • Website under construction

  19. What is the driving force of OUP evolution?

  20. What is the driving force of OUP evolution? • OUP evolution is driven by adaptation to codon usage • OUP pattern is adjusted to the optimal codon usage with a permanent rate. • Concentrations of tRNAs fluctuate in closely related organisms

  21. Conclusion • Driving force of OUP evolution

  22. Conclusion • Driving force of OUP evolution • Reconciliation of WGS and OUP based distances lead to new phylogenomic inference method

  23. Conclusion • Driving force of OUP evolution • Reconciliation of WGS and OUP based distances lead to new phylogenomic inference method • Aweb-based tool is being created for researchers to do phylogenomic studies

  24. Acknowledgement • University of Pretoria • Centre of Bioinformatics and Computational Biology Staff Members • National Research Foundation grant # 93664

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