1. Chromosome Architecture
January 2015

2. Motivation Regulation of gene expression Gene complexes
DNA replication and repair
Recombination
Epigenetic symptoms

3. Chromosome conformation structure
Restriction enzyme is selected by the size of the loci examined

4. Methods
3C – ligation  reverse cross link  quantization using PCR with primers against ligation sites
4C – another round of restriction digest  intermolecular ligation  inverse PCR with primers against the site near the restriction site
5C – multiplexed ligation-mediated amplification (LMA) by using a more general primer
All require choosing the target loci in advance

5. Hi-C Same 3 first steps as previous methods
Using biotin in order to recognize ligated sites
Massively parallel DNA sequencing
Problems
Centromeres
Heterochromatin
Vs. euchromatin

6. Statistical biases Neighboring DNA due to incomplete digestion
Same fragment re-ligation
Size of fragment
Nucleotide composition
Chromatin mobility
Other epigenetic features (replication time, trans-factors, etc.)
Actually reflects a competitive process of ligation

7. Results

8. Domains
Not necessarily compacted

9. Classifications
Active Domains
A1-A4
Null Domains
B1-B2

10. Boundaries
Some DNA elements are found above random at domain boundaries (insulators)
Some DNA elements are found above random inside domains (mainly histone modifications)

11. HK
hetero
CTCF

12. pseudogenes
MAFK
genes
ZNF143

13. isValley test There are 161 transcription factors
Hundreds of other DNA elements
We need a test to distinguish which element is more likely to be found in domain boundaries and which one inside the domain (Valley shaped diagram)
We will take the average of the ends and compare it to the average of the middle and see if the difference is significant (as a function of the total count)

14. isValley test
Randomizing counts and running isValley on it, returned interesting results
Ends
middle
Part of count
P-value simulated
Trxn factors returned true (out of 161)
0-10,90-100
40-60
1000 82
2000
117
3000
0.01
127

15. P-value of isValley test
Under the assumption of uniform distribution we can calculate it rigorously
For a count which derived from a uniform distribution, we will get that the chance of returning true is:

16. P-value of isValley test
Now, the average of n cells of uniform distribution is a random variable

17. P-value of isValley test
Now, the average of n cells of uniform distribution is a random variable
The difference between avg(ends), avg(middle) is

18. Higher resulotion Kilobase resolution instead of megabase
6 subcompartments with distinct patterns of histone modifications – relations between domains in the same compartment
Diploid Hi-C maps Reveals homolog specific features (X chromosome especially)

19. loops
Searching for pairs of loci with significantly higher value then their neighbors
Comparing results with 3D-fish to a control point
Consistent with earlier lists of loops

20. Loops ~10,000 found by “peak loci test”
Frequently linking promoters and enhancers
CTCF are found in convergent orientation
Conserved over evolution

21. Future steps Testing DNA elements by compartments
Testing DNA elements by loop domains and non loop domains