Genomics Statistics Problem I have used a python script to identify target sequences in a DNA sequence file. There are two classes of sequence: coding and non-coding. 

I have identified 728 sequences of interest. 597 of these fall into the coding regions and 131 of these fall into the non-coding regions. This is the equivalent of 18% non-coding. The total non-coding region in the sequence file is 13%. 

Is there a statistical tool to demonstrate the python script identified target sequences in a non-random fashion?
If the script identified sequences that were randomly distributed then 13% of them would have been found in the non-coding region, from a total of 728 this seems like it should be reliable.
A Poisson or binomial would suffice. 

I'm not at all sure the difference will significantly deviate from randomness. 

Perl: 95% confidence interval of a non-parametric bootstrap for e.g. 1000 replicates is an alternative approach. I'd be more comfortable coding that solution in Perl than I would in Python, so is unlikely to be of much use here. Anyway, I would use a 2-dimensional array for your total data-set, the second dimension being coding/non-coding, I would then use the inbuilt random-number generator and multiply by the size of the array to randomly pluck out each element. 95% CI of the mean works by ranking the data and removing the top and bottom 2.5% and taking the interval for a single replicate (which is repeated 999 times more).

There will be a deep learning approach to this data, which would be easier to implement in Python, but I'd need to think about this because it is not simple classification.

Again my warning is that given the sample size, the result at best might be borderline. There are likely to be lots of interesting in your data, but coding/non-coding may not be one of them.