Introduction to Cancer Bioinformatics I: Inferring Genomic Variation from Tumor Sequencing Data

on YouTube ↗01h 31m

" summary

Course argues cancer sequencing needs beta‑binomial overdispersion models to avoid thousands of false SNV calls, shows ultra‑deep control detecting variants down to detection limit ~10^-4 at 10^5× coverage, describes copy‑number inference from read‑depth + B‑allele frequency but notes C×μ identifiability, presents network propagation/random‑walk prioritization and a mutual exclusivity signal to find pathways, and highlights metastasis as the fatal problem (≈90% deaths) with circulating clusters (~2%) far more likely to seed new tumors.

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" chapters (38)

1. 0:00Start
2. 3:16Somatic Mutations in Cancer
3. 5:38Drug resistance
4. 6:07Cancer: Mutation and Selection
5. 8:04The Cancer Genome Atlas (TCGA)
6. 9:42Future? Personalized Medicine
7. 13:05Outline
8. 13:49Sequencing of cancer genomes Measure all somatic mutations
9. 14:44Algorithms for identifying Somatic Mutations
10. 16:12Common aberrations in cancer genomes
11. 18:21Next-generation ultra-deep sequencing
12. 19:19Calling single-nucleotide variants (SNV)
13. 21:00Challenges in NGS-based diversity estimation
14. 22:03Beta-binomial model of allele counts
15. 23:54Overdispersion
16. 25:23Comparative ultra-deep sequencing of a tumor
17. 25:56Likelihood ratio test
18. 26:10Strand specificity
19. 26:57Test data: mix of 5 clones, coverage 10
20. 27:50Performance comparison
21. 28:57Application: Renal cell carcinoma
22. 36:11Intra-tumor diversity matters
23. 37:39Copy number aberrations in cancer genomes
24. 38:56Copy number aberration analysis
25. 40:23B-allele Frequencies (BAFs)
26. 42:59Sequencing Tumor Sample
27. 43:36Mixture Deconvolution
28. 45:16Copy Number Aberrations in Tumors
29. 45:51Probabilistic Model
30. 47:19Maximum Likelihood Mixtures
31. 48:27Clonal Structure from Copy Number Aberrations
32. 50:31Split Reads
33. 51:24Complex Genome Rearrangements
34. 53:19Finding cancer genes
35. 54:15Coin flips and cancer genes
36. 54:58Estimating background mutation rate
37. 56:14Key Issue: Tumor Heterogeneity
38. 59:02Can we discover the pathways?

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