GenePy v1.3 is the latest version of GenePy which implements the following improvement from v1.2:
- GenePy now implements CADD v1.5 for the following reasons: scores both SNPs and INDELS, scores both coding and non-coding, works with both hg19 and hg38
- population frequencies are now obtained from gnomAD_exome but can be modified to use gnomAD_genome.
- novel variant frequency is set to 3.98e-6 (1 allele out 125,748 indiv in gnomADexome (251496 alleles))
- A (multi)sample VCF file (can accept compressed vcf.gz)
- List of genes for which generate GenePy scores. (gene.list)
- CADD v1.5 installed
- Vcftools
- Annovar (RefGene and gnomAD v.2.11 annotations)
- Python 2.7.x
Before running GenePy, we need to annotate SNVs and generate a GenePy-ready file (ALL_genepy.meta)
The first input required to GenePy is a multi-sample VCF (GENOTYPED_ALL.vcf.gz in this example) containing only BI-ALLELIC variants.
vcftools --gzvcf GENOTYPED_ALL.vcf.gz --min-alleles 2 --max-alleles 2 --recode --out FINAL # Keep only Biallelic SNVs
./annovar/convert2annovar.pl \
-format vcf4 FINAL.recode.vcf.gz \
-outfile ALL_genepy.input \
-allsample \
-withfreq \
-include 2>annovar.log
./annovar/table_annovar.pl \
ALL_genepy.input \
./annovar/humandb/ \
-buildver hg38 \
-out ALL_genepy \
-remove \
-protocol refGene,gnomad211_exome \
-operation g,f \
--thread 40 \
-nastring . >>annovar.log
For additional notes on how to install CADD please visit https://github.com/kircherlab/CADD-scripts
# first remove the header from the vcf and strip off the leading "chr" as required by CADD
zgrep -v "^#" FINAL.recode.vcf.gz >caddin.vcf
sed -i 's|^chr||g' caddin.vcf
# activate CADD environment
module load conda/py2-latest
source activate cadd-env-v1.5
# run CADD
./CADD-scripts/CADD.sh -g GRCh38 -v v1.5 -o caddout.tsv.gz caddin.vcf
# extract the genotypes..
cut -f 18- ALL_genepy.input > a1
# and the sample headers
zgrep '^#CHR' FINAL.recode.vcf.gz | cut -f 10- > b1
cat b1 a1 > geneanno
# extract allele frequencies
cut -f 1,2,4,5,6,7,11 ALL_genepy.hg38_multianno.txt >freqanno
# transform/prepare output, this script automatically fixes missmatch between line in the vcf and annotation
# if any fix fail, it will be prompted and replaced with "NAN". If NAN are not removed, GenePy will fail. The cross-annotate-cadd.py script will always fail at positions chr6:75085419 and chr17:6787257. The scores for these can be filled in manually by cross-checking with caddout.tsv
gunzip caddout.tsv.gz
python cross-annotate-cadd.py
# collate everything together
paste freqanno caddanno geneanno > ALL_genepy.meta
rm a1 b1 caddanno freqanno geneanno
make new folders in your current directory to store raw GenePy score files
mkdir CADD15_RAW
Take the header from the ALL_genepy.meta file and stores it in a newly created header file
grep "^Chr" ALL_genepy.meta> header
If willing to use only exonic variants run the following
grep -E "exonic|splicing" ALL_genepy.meta > temp
cat header temp > ALL_genepy_exonic.meta
Once the ALL_genepy.meta file is created, GenePy_1.3.sh can be run by simply iterating through the list of deisred genes. Be aware, the make_scores_mat_6.py file must be in the same directory of GenePy_1.3.sh. WARNING If using the ALL_genepy_exonic.meta, replace the correct filename in the GenePy_1.3.sh file
#Create gene list
cut -f 7 ALL_genepy.hg38_multianno.txt | grep -v ";" | grep -v "Gene.refGene" | sort | uniq >gene.list
#Run GenePy
while read gene:
do
sh GenePy_1.3.sh $gene ;
done< gene.list
Here a smart way of parallelise the generation of GenePy scores using the subber script:
# split the gene list in batches of 400 genes
split -d -l 400 gene.list batch_
#put all the batch names in a file
ls batch_* >parts
#check how many batches we have (52 in my case)
wc -l parts
#submit the job array
sbatch --array=1-52 subber.sh
You can also run some of the script of GenePy as a package.
GenePy can be installed on python3+ from the latest code on GitHub with:
$ pip install git+https://github.com/AldisiRana/GenePy-1.3.git
This function calculates the genepy scores of a gene list for a group of samples.
$ genepy get-genepy -genepy-meta meta/file/path --output-dir /directory/for/score/output --gene-list /path/to/gene/list --score-col score_column_in_meta_file