I have time series data from NanoPore sequencing (Attached is a illustrative figure and short explanation) which I'd ultimately like to use in order to find various methylation patterns in the input DNA sequences.
For those of you for whom the above paragraph doesn't mean much: This is a hidden Markov model sort of situation, where the data is some noisy function of the true input sequence, and I would like to infer certain properties of said input sequence.
I have a few approaches in mind. But first, what would be some some good data exploration methods? Especially ways to visualize the raw data that would help in understanding the type of structures which are present. For example, I thought of dividing the data into overlapping windows and plotting a matrix of [Pearson?] correlation between the different windows.
Any suggestions and/or references for further exploration are greatly appreciated.
NanoPore sequencing: This is a DNA sequencing method in which the DNA strand is forced through a "hole" in a membrane while at the same time there is a flux of Ions from one side of the membrane to the other. Different nucleotide patterns block the hole in different ways, creating a measurable difference in the flux, which is being measured constantly (vicariously, via the current-density) on the membrane.