MIT Computational Biology: Genomes, Networks, Evolution, Health
[ Ссылка ]
Prof. Manolis Kellis

Full playlist with all videos in order is here: [ Ссылка ]

All slides from Fall 2019 are here: [ Ссылка ]

Outline for this lecture:
1. Introduction to regulatory motifs / gene regulation
- Expts vs. comp. Co-regulated genes (EM, Gibbs). Conserv.
2. Expectation maximization: Motif matrix-to-positions
- E step: Estimate motif positions Zij from motif matrix
- M step: Find max-likelihood motif from all positions Zij
3. Gibbs Sampling: Sample from joint (M,Zij) distribution
- Sampling motif positions based on the Z vector
- More likely to find global maximum, easy to implement
4. Evolutionary signatures for de novo motif discovery
- Genome-wide conservation scores, motif extension
- Validation of discovered motifs: functional datasets
5. Evolutionary signatures for instance identification
- Phylogenies, Branch length score -to- Confidence score

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