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An EM Algorithm for Inferring the Evolution of Eukaryotic Gene Structure

Outline. Background and Related WorkData ComponentsThe ModelThe AlgorithmResults

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An EM Algorithm for Inferring the Evolution of Eukaryotic Gene Structure

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    1. An EM Algorithm for Inferring the Evolution of Eukaryotic Gene Structure Liran Carmel, Igor B. Rogozin, Yuri I. Wolf and Eugene V. Koonin NCBI, NLM, National Institutes of Health

    2. Outline Background and Related Work Data Components The Model The Algorithm Results – Homogeneous Evolution Results – Heterogeneous Evolution Summary

    3. What are Exons and Introns

    4. Related work

    5. Outline Background and Related Work Data Components The Model The Algorithm Results – Homogeneous Evolution Results – Heterogeneous Evolution Summary

    6. Phylogenetic tree

    7. Multiple alignment

    8. Presence/absence maps (proteasome component C3)

    9. Missing data

    10. Missing data (proteasome component C3)

    11. Bayesian Network

    12. Outline Background and Related Work Data Components The Model The Algorithm Results – Homogeneous Evolution Results – Heterogeneous Evolution Summary

    13. Probability structure

    14. Rate variation across sites gain variation loss variation

    15. Parameter Summary Global parameters – probability for intron absence in the root – fraction of invariant sites – shape parameters of the gamma distribution Gene-specific parameters – gain rate – loss rate Branch-specific parameters – gain coefficient – loss coefficient

    16. Homogeneous vs. Heterogeneous Evolution The number of parameters in the model

    17. Outline Background and Related Work Data Components The Model The Algorithm Results – Homogeneous Evolution Results – Heterogeneous Evolution Summary

    18. Likelihood maximization via Expectation Maximization E-Step inward-outward recursions on the tree member in the junction-tree algorithms family missing data are naturally embedded

    19. Inward (gamma) recursion

    20. Inward (gamma) recursion - Initialization

    21. Inward (gamma) recursion - Recursion

    22. Outward (alpha) recursion

    23. Likelihood maximization via EM E-Step inward-outward recursions on the tree member in the junction-tree algorithms family missing data are naturally embedded M-Step low-tolerance variable-by-variable maximization Newton-Raphson

    24. Outline Background and Related Work Data Components The Model The Algorithm Results – Homogeneous Evolution Results – Heterogeneous Evolution Summary

    25. Intron density in ancient eukaryotes

    26. Evolutionary Landscape

    27. Modes of Evolution

    28. Modes of Evolution

    29. Outline

    30. Gene Characteristics

    31. Combined Features

    32. Combined Features

    33. Important genes gain introns

    34. Outline

    35. Conclusions Disparate landscape – both gain and loss play role in intron evolution The common ancestor of the crown group had an intron content comparable to fungi, apicomlexans and dipterans Three modes of evolution – more than one mechanism? Important genes tend to gain introns

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