1. CSCI2950-C Genomes, Networks, and Cancer

2. Course Organization Seminar style Grading: CS requirements:
10% participation
30% paper reviews (~10)
20% presentations (2-3)
40% project. Midterm proposal and final presentations
Extra credit: 5% for report on comp. bio. seminars (pre-arranged): Maximum 2
CS requirements:
PhD: Area B (Algorithms)
ScM: Theory or Practice* *depending on project
Significant programming*

3. Biology 101

4. Biology 101
Central Dogma

5. What can we measure? Sequencing (expensive) Hybridization (noisy)
Mass spectrometry (noisy)
Hybridization (very noisy!)

6. Human Genome Sequenced 2000-2003, ???
Comparative Genomics
Genomes of other mammals
2) Disease genomics
HapMap
1000 Genomes
3) Cancer genomics
The Cancer Genome Atlas
Tumor Sequencing Project
Sept. 3
“In the Genome Race, the Sequel Is Personal”
Now what?

8. Course Topics Genomes Algorithms Visualization Machine Learning
Systems
Data Mining
Networks
Cancer

9. Course Topics
Genomes
Networks
Cancer

10. DNA sequencing How we obtain the sequence of nucleotides of a species
…ACGTGACTGAGGACCGTG
CGACTGAGACTGACTGGGT
CTAGCTAGACTACGTTTTA
TATATATATACGTCGTCGT
ACTGATGACTAGATTACAG
ACTGATTTAGATACCTGAC
TGATTTTAAAAAAATATT…

11. DNA Sequencing Goal: Find the complete sequence of A, C, G, T’s in DNA
Challenge:
There is no machine that takes long DNA as an input, and gives the complete sequence as output
Can only sequence ~500 letters at a time

12. Shotgun Sequencing Get one or two reads from each segment
genomic segment
cut many times at random (shotgun)
Get one or two reads from each segment
No technology yet for reading a long strand of DNA in it’s entirety. Instead fragment. New tech. prodcuing shorter reads.
~500 bp
~500 bp

13. Fragment Assembly Cover region with ~7-fold redundancy
reads
Put all pieces back together. Great problem for CS. One formulation as SCS.
Cover region with ~7-fold redundancy
Overlap reads and extend to reconstruct the original genomic region

14. Comparative Genomics Compare genome sequences of related species.
Sequence Alignment
Phylogeny: defining ancestral relationships between species, genomes, genes.
Sequences
Evolve via substitutions
Conservation suggests function
mouse
human
EFTPPVQAAYQKVVAG
DFNPNVQAAFQKVVAG

15. History of Chromosome X
Rat Consortium, Nature, 2004

16. Comparative Genomic Architectures: Mouse vs Human Genome
Humans and mice have similar genomes, but their genes are ordered differently
~245 rearrangements
Reversals
Fusions
Fissions
Translocation

17. Genome rearrangements
Mouse (X chrom.)
Unknown ancestor ~ 75 million years ago
Human (X chrom.)
What are the similarity blocks and how to find them?
What is the architecture of the ancestral genome?
What is the evolutionary scenario for transforming one genome into the other?

18. Reversals 1 3 2 4 10 5 6 8 9 7
1, 2, 3, 4, 5, 6, 7, 8, 9, 10
Blocks represent conserved genes.

19. Reversals 1 2 3 9 10 8 4 7 5 6
1, 2, 3, -8, -7, -6, -5, -4, 9, 10
Blocks represent conserved genes.
In the course of evolution or in a clinical context, blocks 1,…,10 could be misread as 1, 2, 3, -8, -7, -6, -5, -4, 9, 10.

20. Reversals and Breakpoints1 2 3 9 10 8 4 7 5 6
1, 2, 3, -8, -7, -6, -5, -4, 9, 10
The reversion introduced two breakpoints (disruptions in order).

21. Sorting Permutations by Reversals
(Sankoff et al.1990)
 = 12…n signed permutation
Reversal (i,j) [inversion]
1…i-1 -j ... -i j+1…n
Problem: Given , find a sequence of reversals 1, …, t with such that:
 . 1 . 2 … t = (1, 2, …, n) and t is minimal.
Solution: Analysis of breakpoint graph
Polynomial time algorithms
O(n4) : Hannenhalli and Pevzner, O(n2) : Kaplan, Shamir, Tarjan, 1997.
O(n) [distance t] : Bader, Moret, and Yan, O(n3) : Bergeron, 2001.

22. Course Topics Genomes Networks Cancer Network Alignment
Network Integration

23. Biological Interaction Networks
Many types:
Protein-DNA (regulatory)
Protein-metabolite (metabolic)
Protein-protein (signaling)
RNA-RNA (regulatory)
Genetic interactions (gene knockouts)

24. Regulatory Networks

25. Cis-regulatory Network

26. Metabolic Networks Nodes = reactants Edges = reactions
labeled by enzyme (protein) that catalyzes reaction

27. Protein-Protein Interaction (PPI) Network

28. Protein-Protein Interaction Network?
Proteins are nodes
Interactions are edges
Edges may have weights
Yeast PPI network
H. Jeong et al. Lethality and centrality in protein networks. Nature 411, 41 (2001)

29. Network Alignment
Sharan and Ideker. Modeling cellular machinery through biological network comparison. Nature Biotechnology 24, pp , 2006

30. The Network Alignment Problem
Given: k different interaction networks belonging to different species,
Find: Conserved sub-networks within these networks
Conserved defined by protein sequence similarity (node similarity) and interaction similarity (network topology similarity)

31. Protein Signaling Networks
Art Salomon
Biology Department
Use machine learning methods (Bayesian networks, etc. to derive network structure.

32. Computational Problems
Classifying Network Topology
Finding paths, cliques, dense subnetworks, etc.
Comparing Networks Across Species
Using networks to explain data
Dependencies revealed by network topology
Modeling dynamics of networks

33. Course Topics
Genomes
Networks
Cancer
Cancer Genomes
Cancer & Networks

34. Cell Division and Mutation
Single nucleotide
change
A major contributor to the development of cancer are somatic mutations that occur during cell division
Will focus on structural and later copy number, which is not to say that single are not as important. What is the effect of structural changes
Copy number
Structural

35. Cancer Genomes
Leukemia
Breast

36. Rearrangements in Tumors
Change gene structure, create novel fusion genes
Same translocation as previous slide. 30 years from rearrangement to Gleevec
Gleevec (Novartis 2001) targets ABL-BCR fusion

37. Challenges What is detailed organization of cancer genomes?
What genes affected?
What sequence of rearrangements produced this genome?
Can we create custom treatments for tumors based on mutational spectrum? (e.g. Gleevec)
Maybe time for a “Cancer Genome Project”??

38. Tumor Genome → Phenotype Gene Networks and Pathways
Integration of Multiple Data Sources
ESP and copy number
Mutation
Expression
(mRNA and miRNA)
Binding (ChIP-chip)
Pathways
Epigenetics
activation
repression
Duplicated genes
Deleted genes

39. Tumor Genome → Phenotype Gene Networks and Pathways
Integration of Multiple Data Sources
ESP and copy number
Mutation
Expression
(mRNA and miRNA)
Binding (ChIP-chip)
Pathways
Epigenetics
activation
repression
Duplicated genes
Deleted genes

40. Course Topics Genomes Algorithms Visualization Machine Learning
Systems
Data Mining
Networks
Cancer