hello everyone,
I face an unusual issue with merging Illumina Paired-end reads and controling for the merging using individual base quality:
using your preferred merge (let us say Pear, FLash...) you can of course control for the effect of quality score difference between R1 and R2 for a given position and decide wether the difference is large enough to make the base with highest value the one you keep. Ok, works fine in most cases. Here I have a different situation where I would like to do that + if one of the two scores for a given position is < threshold (let us say 20 for example) then the other strand is kept, whatever the difference in score AS LONG as its own score > 20. And that I could not find it from any PE reads merger yet !
Any (verified) idea anyone?
just to avoid out of topic comments, &- yes, I alreadyy though of softmasking low quality bases before merging but I could not find any merger that uses this information also. 2- No, I cannot just remove the reads with low quality bases before merging as I cannot use a strategy based on the % of low quality reads or sliding windows as I really want to use the individual position quality profile for rare variants calling. 3- yes, using illumina correction algorithms like dada2 is an option I will also explore but I would prefer exploring the solution I detail during merging first.
Thanks all !
I face an unusual issue with merging Illumina Paired-end reads and controling for the merging using individual base quality:
using your preferred merge (let us say Pear, FLash...) you can of course control for the effect of quality score difference between R1 and R2 for a given position and decide wether the difference is large enough to make the base with highest value the one you keep. Ok, works fine in most cases. Here I have a different situation where I would like to do that + if one of the two scores for a given position is < threshold (let us say 20 for example) then the other strand is kept, whatever the difference in score AS LONG as its own score > 20. And that I could not find it from any PE reads merger yet !
Any (verified) idea anyone?
just to avoid out of topic comments, &- yes, I alreadyy though of softmasking low quality bases before merging but I could not find any merger that uses this information also. 2- No, I cannot just remove the reads with low quality bases before merging as I cannot use a strategy based on the % of low quality reads or sliding windows as I really want to use the individual position quality profile for rare variants calling. 3- yes, using illumina correction algorithms like dada2 is an option I will also explore but I would prefer exploring the solution I detail during merging first.
Thanks all !
Comment