Hi,
I am using golden gate assemblies to create modular plasmid libraries. I currently am looking at potentially a 80 combinations of plasmids that are quite similar. (4 Promoters x 8 Gene A x 8 Gene B )
backbone---PromoterRBS1---GeneA1---GeneB1---Barcode
backbone---PromoterRBS2---GeneA1---GeneB1---Barcode
...
backbone---PromoterRBS4---GeneA4---GeneB4---Barcode
I sequence the multiplexed plasmids using and demultiplex using the barcode. I want to identify the corresponding plasmids using the demultiplexed reads. Currently I am using:
minimap2 -x map-ont --secondary=no
I then take the mapping with the highest score for each read and assign the plasmid by the highest abundance of a mapping. This works somewhat ok with a control experiment but I want to work on my minimap settings and I am wondering what the right minimap settings are for my situation.
Thanks for the help!
You may consider to add -f 100 in case minimap2's automatic heuristic chooses an inappropriate k-mer threshold. As to other options, you have to try by yourself as I haven't worked with such data.
Hi, I am using golden gate assemblies to create modular plasmid libraries. I currently am looking at potentially a 80 combinations of plasmids that are quite similar. (4 Promoters x 8 Gene A x 8 Gene B ) backbone---PromoterRBS1---GeneA1---GeneB1---Barcode backbone---PromoterRBS2---GeneA1---GeneB1---Barcode ... backbone---PromoterRBS4---GeneA4---GeneB4---Barcode
I sequence the multiplexed plasmids using and demultiplex using the barcode. I want to identify the corresponding plasmids using the demultiplexed reads. Currently I am using:
minimap2 -x map-ont --secondary=no
I then take the mapping with the highest score for each read and assign the plasmid by the highest abundance of a mapping. This works somewhat ok with a control experiment but I want to work on my minimap settings and I am wondering what the right minimap settings are for my situation. Thanks for the help!