Hello,
We’re benchmarking against HG002 within Tier 1 regions using the HiFi sequel data from HPRC. We’re getting good recall but low precision. Do you have any parameter suggestions on how to improve precision?
Here is our result:
| TP |
FN |
FP |
Precision |
Recall |
F1 |
| 9387 |
259 |
2890 |
0.76460047 |
0.97314949 |
0.8563609 |
We are using the latest conda version and running it with default parameters as follows.
SVDSS smooth --reference human_g1k_v37.fasta --bam HG002_hg19.bam --threads 32 > smoothed.bam
SVDSS search --index human_g1k_v37.fmd --bam smoothed.bam --threads 32 > specifics.txt
SVDSS call --reference human_g1k_v37.fasta --bam smoothed.bam --sfs specifics.txt --threads 32 > svdss.vcf
We are using truvari(v4.1.0) for the benchmarking:
truvari bench -c svdss.vcf.gz -b HG002_SVs_Tier1_v0.6.vcf.gz --typeignore --dup-to-ins -p 0 -s 50 -S 0 --sizemax 100000000 -o truvari/svdss --passonly --includebed HG002_SVs_Tier1_v0.6.bed
Best
Ayse
Hello,
We’re benchmarking against HG002 within Tier 1 regions using the HiFi sequel data from HPRC. We’re getting good recall but low precision. Do you have any parameter suggestions on how to improve precision?
Here is our result:
We are using the latest conda version and running it with default parameters as follows.
We are using truvari(v4.1.0) for the benchmarking:
truvari bench -c svdss.vcf.gz -b HG002_SVs_Tier1_v0.6.vcf.gz --typeignore --dup-to-ins -p 0 -s 50 -S 0 --sizemax 100000000 -o truvari/svdss --passonly --includebed HG002_SVs_Tier1_v0.6.bedBest
Ayse