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