Introduction
Chromatin architecture is of high interest in molecular biology, and increasingly so in molecular evolution. A high-throughput method for mapping the 3D organization of the genome was proposed by Lieberman-Aiden et al. (2009). Here, chromatin interactions from Hi-C sequencing is used to generate an interaction matrix of valid Hi-C contacts, and PCA is performed on the covariance matrix of the observed and expected matrix. The following rationale is proposed and experimentally supported; if clustering into 2 types (A/B compartments), genomic positions that interact will have a positive value in the covariance matrix, and vice versa for positions that do not interact. Then, when PCA is performed, the first principal component captures the variance that arise from being in different compart by its sign. The direction of the eigenvector (the sign) is then phased with a biologically relevant measure (such as GC content) to establish that the A compartment is largely the active chromatin and B is largely inactive.
Methods
Here are methods
Method 1
The first method with a code block
# Python code
= mc**2
e
print(e)
Results
Results are written here
Discussion
The discussion
Conclusion
I am concluding this analysis
Bon mot
Nothing in Biology Makes Sense except in the Light of Evolution
- Theodosius Dobzhansky