An implementation of "Replicon" (Gindin et. al. 2014) as a Rust programming and bioinformatics exercise, based on the original works. Follows the same strategy: predict replication timing (RT) for a full genome using only a probability of initiation at each position in the genome and the number of replication machineries as an input.
Each cell simulation uses a very space-efficient representation of the replication-state, which is independant of the genome length. The representation stores the replicated state as a series of alternating replicated (R) and unreplicated (U) runs, much like a run-length encoding (RLE). This allows for a fixed size represntation driven by the number of replication machineries (M) of size = (M * 2) + 3.
The alternating states in the representation allow the bi-directional progress of the fork to be done by unreplicated regions "giving" their bases to replicating regions on either side. Merging replicating regions with a shared adjacent empty unreplicated region conserves the number of replication forks implicitly, and requires only one edge-condition for 5' chromosome end. I have some ideas for other versions of this encoding that take up more space but may allow for fewer values being moved around such as:
- extending the array to remove the 5' edge case,
- doubling the length of the array and starting in the middle, with heuristics to choose which way to shift values to minimise work,
but these are not high-priority optimisations.
So far, the basic simulation is completed and I'm generating an initiation probability landscape (IPLS) for the MCF-7 data from the Hansen paper.
- Basic replication tracking on a fixed size genome, with uniform IPLS
- Process ENCODE data:
- Replication Timing for MCF-7
- DNAse-seq for MCF-7
- Read DNAse IPLS into Rust
- Multi-cell asynchronous run
- Flow-gate sorting simulation
- Comparison to reference RT data