Open MatthewRalston opened 7 months ago
Have been reviewing k-mer literature because of the search algorithm. I think I can write my own smith-waterman alignment variant on search alignment and seeding/match optimizer and heuristic which is based on k-mer indices.
The obvious option is on-disk (index+cache based) matching for k-mers but that seems over engineered, so maybe a much simpler k-mer seed region matching strategy is needed for the aligner.
Might suggest the use of conda package
[vsearch](https://github.com/torognes/vsearch/)
to merge reads into 'contigs' i.e. inserts.