Interpreting Deep Learning Models Of Functional Genomics Data To Decode Regulatory Sequence...

on YouTube ↗38m

" summary

Dilated convolutional neural networks map DNA sequence to genome-wide molecular profiles at single-base resolution across hundreds to thousands of cell contexts (data from ENCODE and Roadmap Epigenomics Project). Attribution methods like DeepLIFT and FastISM extract basewise contribution scores; distilled motifs reveal combinatorial rules. Synthetic in silico perturbations expose a soft spacing syntax with 10.5 bp helical periodicity, and key predictions receive CRISPR validation in cell assays.

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" chapters (17)

1. 0:00Intro
2. 1:50Genetic variants associated with traits and dise
3. 3:10Population sequencing to identify disease-associated genet
4. 6:01Functional components of the human genom
5. 8:3695% of disease variants are not disrupting protein coding se
6. 12:54Molecular mapping of functional components of the
7. 15:17Deep learning framework for decoding regulator
8. 16:49BPNet maps DNA sequence to base-resolution molecular profil unprecedented accuracy on par with concordance between replicate experiment
9. 22:20Syntax discovery using in-silico perturba
10. 26:53Designing CRISPR experiments to validate mot
11. 29:49In-silico mutagenesis: Predict effect of genetic variant on molecule
12. 30:47Interpret disrupted predictive sequence syntax
13. 31:20Genetic loci associated with Alzheimer's disease
14. 32:03Molecular profiling of cell types in the brain
15. 32:37Predicting and interpreting causal AD variants
16. 33:40Predicting de-novo mutations in Autism
17. 34:19Predicting de novo mutations in congenital heart dise

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