I have a DNA sample which I know doesn't quite match my reference genome - my culture comes from a subpopulation which has undergone significant mutation since the reference was created.
The example I have in mind is E.coli. We've tried assembly using a couple of different tools and the de-novo assembly isn't as high quality as we would like, despite having tonnes of data. Approaching this from a Bayesian point of view, the reference genome provides a very good prior if we could use it wisely
From visual inspection with IGV, a significant number of both SNPs and SVs appear to be present, but an assembly built entirely from my own sequencing data is not high enough quality for my purposes.
How can I modify this reference genome to match my sample with new sequencing data (preferably with Oxford Nanopore Technologies long reads, but I can also use these to scaffold short reads if necessary), taking advantage of my knowledge that the existing reference is mostly very good, without having to access the reads which were originally used to construct the reference genome?
The goal of the project isn't to determine where the SVs are, I just need a reference that accurately represents my sample in order to use the data for downstream analysis (as the training set for machine learning.) So by a high quality reference, I mean one which represents as well as possible the sample that was sequenced. To make matters worse, this may not be the one which has the highest alignment identity if there are systematic sequencing errors, as in nanopore sequencing!