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Locityper benchmarking scripts

Let us assume that base repository is installed at ~/Code/locityper and this repository is cloned to ~/Code/locityper-bench.

Accuracy evaluation

First, predictions need to be converted to CSV format:

~/Code/locityper/extra/into_csv.py -i analysis/./* -o summary.csv.gz

Haplotypes alignments are constructed using locityper align

locityper align -i loci/$locus/haplotypes.fa.gz -o loci/$locus/haplotypes.paf.gz -A -D 1

Next, eval_accuracy script is called:

~/Code/locityper/extra/eval_accuracy.py \
    -i summary.csv.gz -o eval.csv.gz \
    -a db/loci/{}/haplotypes.paf.gz -d dbs/327 [--loo]

Trio concordance calculation

To calculate trio concordance, we use trio_conc.py and 1KGP pedigree file.

~/Code/locityper/extra/trio_conc.py -i summary.csv.gz -p 1KGP.ped \
    -a db/loci/{}/haplotypes.paf.gz -o trio_conc.csv.gz

Comparing VCF files

In order to convert Locityper predictions into VCF, we use the following command:

~/Code/locityper/extra/into_vcf.py -i summary.csv.gz -d db \
    -v hprc-v1.1-grch38.vcf.gz -g GRCh38 -o locityper-vcfs

To extract 1KGP call set haplotypes, we used the following commands:

bcftools view -s $sample -ac1 -R locus.bed -e 'ALT~"<.*>" && ALT != "<DEL>"' 1KGP.vcf.gz

and reconstructed haplotypes using bcftools consensus. Next, accuracy evaluation, similar to Locityper, can be performed for the reconstructed haplotypes.

In order to compare VCF files, we first decomposed HPRC, Pangenie and Locityper VCF files:

for i in 0 1; do
    # Start with `vt decompose_blocksub -a`, then try without `-a`.
    if [[ i -eq 0 ]]; then
        args=(-a)
    else
        args=()
    fi
    if (bcftools norm -m-any -f "$genome/genome.fa" "$invcf" | \
        vt decompose_blocksub ${args[@]+"${args[@]}"} - | \
        vt normalize -r "$genome/genome.fa" - | \
        bcftools sort - | \
        vt uniq -o "$outvcf" -)
    then
        tabix -p vcf "$outvcf"
        return 0
    fi
done

1KGP call sets were not decomposed, as non-decomposed variants showed higher accuracy. Then, RTG-tools were used to compare two call sets:

rtg vcfeval -b base.vcf.gz -c calls.vcf.gz -e locus.bed -t genome.fa.sdf -o out

Finally, we combined vcfeval results using:

~/Code/locityper-bench/combine_vcfevals.py \
    -i loci/{locus}/evals/{tool}.{sample} -o combined_evals.csv.gz \
    -b loci/{locus}/decompose/baseline.{sample}.vcf.gz -c loci/{locus}/decompose/{tool}.{sample}.vcf.gz \
    -t 0,1,10

Evaluating MHC/KIR-calling

We used existing Immuannot annotations for the HPRC samples, combined them with

~/Code/locityper-bench/hla/convert_annot.py -a Immuannot/{H,N,CHM13,GRCh38}*.gtf.gz -o Immuannot/annot.csv

Then, we converted Locityper and T1K predictions to the same format:

~/Code/locityper-bench/hla/convert_locityper.py -i summary.csv.gz -l loci.txt -a Immuannot/annot.csv \
    -o locityper.csv
~/Code/locityper-bench/hla/convert_locityper.py -i summary_loo.csv.gz -l loci.txt -a Immuannot/annot.csv \
    -o locityper_loo.csv
~/Code/locityper-bench/hla/convert_t1k.py -t t1k/analysis/*/./* -o t1k.csv

where loci.txt represented two-column file with HLA/KIR genes and the corresponding Locityper loci.

Finally, we evaluated accuracy with

~/Code/locityper-bench/hla/accuracy.py -b Immuannot/annot.csv \
    -p t1k.csv -o eval/t1k.csv
~/Code/locityper-bench/hla/accuracy.py -b Immuannot/annot.csv \
    -p locityper.csv -o eval/locityper.csv -a Immuannot/annot.csv
~/Code/locityper-bench/hla/accuracy.py -b Immuannot/annot.csv \
    -p locityper_loo.csv -o eval/locityper_loo.csv -a Immuannot/annot.csv --loo