bio-phasing-imputation-reference-panels — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited bio-phasing-imputation-reference-panels (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Reference examples tested with: bcftools 1.19+, picard 3.1+
Before using code patterns, verify installed versions match. If versions differ:
<tool> --version then <tool> --help to confirm flagsIf code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
"Set up reference panels for imputation" → Download, prepare, and manage reference panels (1000 Genomes, HRC, TOPMed) for genotype phasing and imputation, including population subsetting and format conversion.
bcftools view -S panel_samples.txt for subsetting, bcftools norm for normalization# Download from IGSR
BASE_URL="http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/1000G_2504_high_coverage/working/20201028_3202_phased"
for chr in {1..22}; do
wget ${BASE_URL}/CCDG_14151_B01_GRM_WGS_2020-08-05_chr${chr}.filtered.shapeit2-duohmm-phased.vcf.gz
wget ${BASE_URL}/CCDG_14151_B01_GRM_WGS_2020-08-05_chr${chr}.filtered.shapeit2-duohmm-phased.vcf.gz.tbi
done# Download sample info
wget http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/1000G_2504_high_coverage/1000G_2504_high_coverage.sequence.index
# Create population sample lists
grep "EUR" samples.txt | cut -f1 > european_samples.txt
grep "AFR" samples.txt | cut -f1 > african_samples.txt
grep "EAS" samples.txt | cut -f1 > east_asian_samples.txt
# Subset reference to specific population
bcftools view -S european_samples.txt \
1000GP.chr22.vcf.gz \
-Oz -o 1000GP_EUR.chr22.vcf.gz# Beagle uses VCF directly, but ensure proper format
bcftools view -m2 -M2 -v snps reference.vcf.gz | \
bcftools annotate --set-id '%CHROM:%POS:%REF:%ALT' | \
bgzip > reference_beagle.vcf.gz
bcftools index reference_beagle.vcf.gz# IMPUTE5 uses its own format
imp5Converter \
--h reference.vcf.gz \
--r chr22 \
--o reference.chr22.imp5# HRC requires registration at EGA
# After access granted:
# Download from EGA using pyega3
pip install pyega3
pyega3 -cf credentials.json fetch EGAD00001002729
# HRC contains 32,470 samples (mostly European)# TOPMed available through imputation servers
# Or download from dbGaP with appropriate access
# Use via Michigan Imputation Server:
# 1. Upload study VCF
# 2. Select "TOPMed r2" as reference
# 3. Download imputed results# Beagle format (GRCh38) - from Browning lab
wget https://faculty.washington.edu/browning/beagle/genetic_maps/plink.GRCh38.map.zip
unzip plink.GRCh38.map.zip -d genetic_maps/
# SHAPEIT5 format (recommended for SHAPEIT5)
wget https://github.com/odelaneau/shapeit5/raw/main/maps/genetic_maps.b38.tar.gz
tar xzf genetic_maps.b38.tar.gz# Basic stats
bcftools stats reference.vcf.gz | head -50
# Sample count
bcftools query -l reference.vcf.gz | wc -l
# Variant count
bcftools view -H reference.vcf.gz | wc -l
# Check chromosomes
bcftools index -s reference.vcf.gz# GRCh37 to GRCh38
# Using Picard
java -jar picard.jar LiftoverVcf \
I=reference_hg19.vcf.gz \
O=reference_hg38.vcf.gz \
CHAIN=hg19ToHg38.over.chain.gz \
REJECT=rejected.vcf \
R=hg38.fa
# Or using CrossMap
CrossMap.py vcf hg19ToHg38.chain reference_hg19.vcf hg38.fa reference_hg38.vcf# Check strand concordance
bcftools +fixref study.vcf.gz -Oz -o study_fixed.vcf.gz -- \
-f reference.fa \
-i reference_panel.vcf.gz \
-m flip
# Statistics on fixes
bcftools +fixref study.vcf.gz -- -f reference.fa -m stats# Remove singletons (appear in only 1 sample)
bcftools view -c 2 reference.vcf.gz -Oz -o reference_no_singletons.vcf.gz
# Filter by MAF
bcftools view -q 0.001:minor reference.vcf.gz -Oz -o reference_maf001.vcf.gz
# Remove indels (SNPs only)
bcftools view -v snps reference.vcf.gz -Oz -o reference_snps.vcf.gz# If you have additional reference samples
bcftools merge \
1000GP.chr22.vcf.gz \
custom_reference.chr22.vcf.gz \
-Oz -o combined_reference.chr22.vcf.gz
# Ensure matching variants first
bcftools isec -n=2 \
1000GP.chr22.vcf.gz \
custom_reference.chr22.vcf.gz \
-p isec_output| Panel | Samples | Variants | Populations |
|---|---|---|---|
| 1000G Phase 3 | 2,504 | 88M | 26 global |
| HRC r1.1 | 32,470 | 40M | European-heavy |
| TOPMed r2 | 97,256 | 308M | 60% European, diverse |
| UK10K | 3,781 | 42M | British |
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.