Open jdidion opened 1 month ago
Hi @jdidion ,
It's been a while since I've looked at the performance. Mind sharing the summary recall/precisions stats with me? The tool tends to be overly sensitive, so it's possible precision is the problem. I doubt there's a bias in the benchmarking tool, but I can suggest some simple filters if the precision is driving the terrible F1.
Attached, thanks! Recall looks to be the major issue. I'm going to try again with wham
instead of whamg
.
https://www.nature.com/articles/s41439-024-00276-x/figures/4
I found this somewhat recent benchmarking paper:
It looks like the recall is around 70% for deletions in non-repeat regions.
When I run wham
I get:
When maskLen < 15, the function ssw_align doesn't return 2nd best alignment information.
N
Is this expected?
I re-aligned the HG002 30x PCR-free WGS BAM from Baid et al against hs37d5 using DRAGEN.
I then used the BAM with WHAM to call SVs:
I then benchmarked against the GIAB v0.6 high-confidence callset using Witty.er:
The F1 score at the event level is 0.01 and at the base level is 0.14. I suspect I'm doing something wrong, but I can't figure out what. I've used the same process for benchmarking other SV callers and it works fine. Given the author of Wittyer, is it somehow biased against WHAM :)? Is there a different comparison tool and/or callset I should be using for evaluation.