~/talk

Introduction to Regulatory Genomics and Epigenomics III: Hidden Markov Models in Gene Regulation

name:Uwe Ohler
links:none on file
slides:none on file

Covers using Hidden Markov Models (HMMs) to model gene regulation and epigenomic states, describing how HMMs are applied to genomic and epigenomic data (e.g., chromatin state annotation) and the algorithms for training and inference. Focuses on practical use of HMMs for regulatory genomics tasks such as segmenting genomes into functional states and interpreting regulatory patterns from sequencing-based assays.

AI-written summary · about

  1. 0:00Intro
  2. 0:35Hidden Markov Models
  3. 4:03Outline
  4. 5:33Markov Chains To compute the probability of a sequences of (arbitrary) length L, we can decompose it via the product rule
  5. 7:48Training a Markov Chain A popular objective function to set parameters of Markov models is to maximize the likelihood of the data, defined as
  6. 11:23Weight Matrices
  7. 14:27Markov Chains as Stochastic Automata
  8. 16:19Markov Chain: Formal Definition
  9. 18:14Probability of a Sequence PYRYRYM
  10. 18:58Another Example
  11. 20:24Parsing: Finding the Most Probable Path
  12. 21:43Parsing/Decoding with an HMM
  13. 22:47Dynamic Programming: Best Partial Parse
  14. 23:57The Viterbi Algorithm
  15. 25:24Viterbi: Traceback
  16. 26:20The Forward Algorithm : Probability of a Sequence
  17. 30:32Example: Eukaryotic Gene Structure complete mRNA coding segment
  18. 31:16Using an HMM for Gene Prediction
  19. 32:26Higher Order Markovian Eukaryotic Recognizer (HOMER)
  20. 36:41Unsupervised (ML) Training
  21. 37:29Viterbi Training training features
  22. 38:48Recall: The Forward Algorithm
  23. 39:36The Backward Algorithm
  24. 40:17Baum-Welch: Summing over All Paths
  25. 42:49Combining Forward & Backward
  26. 43:57Monotonic Convergence Behavior
  27. 45:33Variants of Inference
  28. 47:42Example: The Ahab model
  29. 55:22HMMs & Geometric Feature Lengths
  30. 57:42Recall: Decoding with an HMM
  31. 58:16Recall: Viterbi Decoding for HMMs
  32. 58:28Naive GHMM Decoding
  33. 59:32Typical GHMM Topology for Gene Finding Fixed-length states are called signal
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