Hubble.2D6 is a tool to predict CYP2D6 haplotype function. Hubble.2D6 predicts function on an oridal scale: Normal Function, Decreased Function, and No Function, as used in CPIC guidelines*. You can use Hubble.2D6 to predict the function of CYP2D6 star alleles that comprise any arbitrary set of variants within the CYP2D6 locus.
Read more about Hubble.2D6 in the manuscript.
*Increased function is not predicted because currently the only known mechanism the leads to an increased function allele is through increased copy number, which is not evaluated by Hubble.2D6.
If you only need to analyze haplotypes containing SNVs and common INDELs (INDELs found in existing star alleles), you can use the quick installation. We have precomputed embeddings for all possible SNVs and common INDELs so the lengthy annotation step is not needed. If you require analysis of INDELs not in existing star alleles, proceed to "Full Installation".
We recommend using a conda environment for the python libraries. Python version 3.6 required.
pip install -r requirements.txt
Multiallelic sites need to be split and INDELs realigned. This ensures INDELs can be mapped consistently to annotation embeddings. These functions can be performed using BCFtools.
Split multialleleic sites:
bcftools norm -m-any -o OUTPUT.vcf INPUT.vcf
Normalize INDELs:
bcftools norm -f $REF_GENOME -c s -o OUTPUT.vcf INPUT.vcf
Finally, predict haplotype function using hubble.py. The output will be written to hubble_output.txt or a file specified with --output.
The input to Hubble.2D6 is a VCF and can have as many samples (haploytypes) as needed. All sites that differ from the reference (*1) should be listed in the VCF and the appropriate allele indicated as homozygous (e.g. 1/1).
Example:
python bin/hubble.py -s data/sample.vcf
Run python bin/hubble.py --help for full list of commands.
Hubble.2D6 uses annotation embeddings for input variants to predict star allele function. If you reqruire analysis of INDELs not found in existing star alleles (currently up to CYP2D6*139) you need to generated annotation embeddings to perform the analysis. A number of steps are required to generate the annotation embeddings which requires installation of several tools. Reminder: If you only need to evaluate star alleles with SNVs, you can skip this step. Annotation embeddings for SNVs have been precomputed.
Fair warning, installation of VEP can be frustrating and the Annovar databases are very large.
If you struggle with installation, open an issue and we may be able to generate annotation embeddings for you.
We recommend using a conda environment for the python libraries. Python version 3.6 required.
pip install -r requirements.txt
- Install Annovar
- Download required annovar databases. Some of these are large databases that will take a while to download. (ex.
annotate_variation.pl -downdb -buildver hg19 -webfrom annovar DATABASE_NAME humandb/)- wgEncodeAwgDnaseMasterSites
- wgEncodeRegTfbsClusteredV3
- tfbsConsSites
- wgEncodeHaibMethyl450Gm12878SitesRep1
- wgEncodeHaibMethylRrbsGm12878HaibSitesRep1
- dann
- cadd
- dbnsfp33a
- fathmm
- gnomad_genome
- refGene
- Copy custom database files to annovar directory (ex.
cp data/DATABASE_NAME $ANNOVAR/humandb/)- CYP2D6_eQTLs
- cyp2d6_active_site
- Install BCFtools
wget http://hgdownload.cse.ucsc.edu/goldenPath/hg19/bigZips/hg19.fa.gz
gunzip hg19.fa.gz
Set global variables. These do not need to be set for the quick installation.
HUBBLE_PATH=path_to_install_dir
ANNOVAR_PATH=path_to_annovar_dir
VEP_PATH=path_to_vep_dir
BCFTOOLS_PATH=path_to_bcftools
REF_GENOME=path_to_ref_genomie
Input a VCF into the data processing pipeline which will annotation the variants and create embeddings that can be used for prediction.
sh bin/vcf2seq.sh INPUT_VCF PREFIX
Example:
sh bin/vcf2seq.sh data/test.vcf test
This will generate a file with a .seq extension that can be used to predict function
After preparing the seq file as described above use this function to predict the haplotype function.
Example:
python bin/hubble.py -s data/test.seq