Improve a reference genome with sequencing data 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.

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?
One approach to this is to use whatever data you have to iteratively update the reference genome. You can keep chain files along the way so you can convert coordinates (e.g. in gff files) from the original reference to your new pseudoreference.

A simple approach might be:


Align new data to existing reference
Call variants (e.g. samtools mpileup, GATK, or whatever is best for you)
Create new reference incorporating variants from 2
Rinse and repeat (i.e. go to 1)


You can track some simple stats as you do this - e.g. the number of new variants should decrease, the number of reads mapped should increase, and the mismatch rate should decrease, with every iteration of the above loop. Once the pseudoreference stabilises, you know you can't do much more.