Maintainer Email: [email protected]
Hare is a de novo variant caller leveraging the power of Parabricks GPU accelerated variant calling. This is an updated version of the workflow described in Ng et al. 2022. The original code of the workflow can be found here. This version has been tested with the output from Parabricks v.3.0.0, as well as the free version of Parabricks v4.0.0.0-1. You can find dependencies and instructions on how to run Parabricks here.
Three main inputs:
- .bam or .cram files for the trio(s)
- A comma-delimited text file, with one trio per line, with sample IDs formatted in the following way: Father,Mother,Child
- The reference genome .fasta used when running GATK and DeepVariant
You'll first need to run your crams through Parabricks GATK Haplotypecaller and DeepVariant, the instructions of which can be found above. When doing so, please ensure you are using the --gvcf flag. You'll use that output for the Snakemake file found in this repo.
NOTE This pipeline has been specifically tested using output from Parabricks GATK Haplotypecaller and DeepVariant. Other g.vcf output may not work properly. Please only use WGS and WES data with this pipeline. If you have PacBio long-read data, please use Tortoise.
This workflow also makes use of specific RepeatMasker files, links to which can be found below.
wget -q https://de.cyverse.org/dl/d/B42A0F3D-C402-4D5F-BBD5-F0E61BE2F4AC/hg38_centromeres_09252018.bed.gz
wget -q https://de.cyverse.org/dl/d/37B13DB5-0478-4C4B-B18D-33AFB742E782/hg38_centromeres_09252018.bed.gz.tbi
wget -q https://de.cyverse.org/dl/d/870755FF-CD04-4010-A1EC-658D7E1151EF/LCR-hs38-5bp-buffer.bed.gz
wget -q https://de.cyverse.org/dl/d/01D038EA-51CC-4750-9814-0BB3784E808E/LCR-hs38-5bp-buffer.bed.gz.tbi
wget -q https://de.cyverse.org/dl/d/185DA9BC-E13D-429B-94EA-632BDAB4F8ED/recent_repeat_b38-5bp-buffer.bed.gz
wget -q https://de.cyverse.org/dl/d/4A6AF6EF-D3F0-4339-9B8E-3E9E83638F00/recent_repeat_b38-5bp-buffer.bed.gz.tbi
wget -q https://de.cyverse.org/dl/d/786D1640-3A26-4A1C-B96F-425065FBC6B7/CpG_sites_sorted_b38.bed.gz
wget -q https://de.cyverse.org/dl/d/713F020E-246B-4C47-BBC3-D4BB86BFB6E9/CpG_sites_sorted_b38.bed.gz.tbi
While the use of Docker is highly recommended, the workflow is able to run outside of a Docker environment, please see the software dependencies below. If you would like to run Parabricks 4.0.0, having a system able to run Docker is a requirement.
The workflow Docker image can be pulled from here:
tnturnerlab/hare:v1.2
Before running, please make any necessary changes to the options below in the config.json.
* regions: "/region" *If you don't have the RepeatMasker files, please make this entry blank*
* gq_value: 20 *Default gq value filter*
* depth_value: 10 *Default depth value filter*
* suffix_dv: *Suffix of the DeepVariant data files. Assumes input files are \<sample\_name\>\<suffix\>*
* suffix_hc: *Suffix of the GATK Haplotypecaller data files. Assumes input files are \<sample\_name\>\<suffix\>*
* family_file: "/dnv_wf_cpu/<your_family_file>"
* glnexus_dv_model: Please change this to DeepVariantWES if you are running WES, otherwise leave it blank.
* chrom_length: *Optional chromosome length file, use if you are not using human reference GRCh38. You can leave it blank if using GRCh38. Please make this a two-column, tab-delimited file, with the first chromosome and the second column the length of the chromosome*
Below is an example Docker run command:
docker run -v "/path/to/hare/code:/dnv_wf_cpu" -v "/path/to/reference:/reference" -v "/path/to/deepvariant/output:/dv" -v "/path/to/gatk/output:/gatk" -v "/path/to/RepeatMasker/region/files:/region" tnturnerlab/hare:v1.2 /opt/conda/envs/snake/bin/snakemake -s /dnv_wf_cpu/hare_1.2.smk -j 6 --cores -k --rerun-incomplete -w 120
We also provide this workflow in a .wdl format. Unlike the Snakemake, you will be able to run Parabricks directly from this workflow, instead of separately. You can also run this workflow in the cloud. To run this, you'll need to download the Cromwell .jar found here. This wdl was specifically tested on cromwell-83.
The basic config file looks like this:
{
"jumping_hare.num_ram_hc": "Int (optional, default = 120)",
"jumping_hare.extra_mem_hc": "Int (optional, default = 65)",
"jumping_hare.maxPreemptAttempts": "Int (optional, default = 3)",
"jumping_hare.cpu_hc": "Int (optional, default = 24)",
"jumping_hare.glnexus_deep_model": "String (optional, default = \"DeepVariant\")", #Please change this to DeepVariantWES for WES data
"jumping_hare.test_intersect": "File", #pathway to the test_intersect.py file
"jumping_hare.deep_model": "String (optional, default = \"shortread\")",
"jumping_hare.gpuDriverVersion_DV": "String (optional, default = \"460.73.01\")",
"jumping_hare.sample_suffix": "String", #suffix of the input cram file. If your sample was NA12878.final.cram, you would put ".final.cram" here
"jumping_hare.typeOfGPU_HC": "String (optional, default = \"nvidia-tesla-t4\")",
"jumping_hare.gq": "Int (optional, default = 20)",
"jumping_hare.glnexus_ram_dv": "Int (optional, default = 100)",
"jumping_hare.filter_glnexuscombined_updated": "File", #pathway to filter_glnexuscombined_updated.py
"jumping_hare.num_gpu_HC": "Int (optional, default = 2)",
"jumping_hare.num_ram_dv": "Int (optional, default = 120)",
"jumping_hare.naive_inheritance_trio_py2": "File", #pathway to naive_inheritance_trio_py2.py
"jumping_hare.num_gpu_dv": "Int (optional, default = 4)",
"jumping_hare.glnexus_DV.extramem_GLDV": "Int? (optional)",
"jumping_hare.extra_mem_dv": "Int (optional, default = 65)",
"jumping_hare.typeOfGPU_DV": "String (optional, default = \"nvidia-tesla-t4\")",
"jumping_hare.combinedAndFilter.extramem_GLDV": "Int? (optional)",
"jumping_hare.glnexus_ram_hc": "Int (optional, default = 100)",
"jumping_hare.deep_docker": "String (optional, default = \"nvcr.io/nvidia/clara/clara-parabricks:4.0.0-1\")",
"jumping_hare.pathToReference": "File", #pathway to tarball of reference information
"jumping_hare.wes": "Boolean (optional, default = false)", #Please set this to true if you are analyzing WES data
"jumping_hare.glnexus_cpu": "Int (optional, default = 32)",
"jumping_hare.gpuDriverVersion_HC": "String (optional, default = \"460.73.01\")",
"jumping_hare.cram_files": "Array[Array[WomCompositeType {\n cram -> File\ncrai -> File \n}]]", #cram/bam file input, please see the example for formatting
"jumping_hare.cpu_dv": "Int (optional, default = 24)",
"jumping_hare.glnexus_HC.extramem_GLDV": "Int? (optional)",
"jumping_hare.interval_file": "String (optional, default = \"None\")", #This is the name of your exome capture region file
"jumping_hare.depth": "Int (optional, default = 10)",
"jumping_hare.reference": "String", #name of reference fasta
"jumping_hare.regions": "File? (optional)", #This is the tarball of your RepeatMaster files
"jumping_hare.hare_docker": "String (optional, default = \"tnturnerlab/hare:v1.1\")",
"jumping_hare.trios": "Array[WomCompositeType {\n father -> String\nmother -> String\nchild -> String \n}]", #trios, MUST be in same order as trios in cram_file
"jumping_hare.chrom_length": "File? (optional)" #Optional chromosome length file if you are not using Human build GRCh38
}
Required arguments are highlighted in the comments above. We have provided an example config to help with formatting. Please modify the computational requirements to fit your HPC. If you are running it on Google Cloud Platform, you may keep the computation settings. Requirements are based on NVIDIA's own workflows found here. If you are going to use this wdl, please tarball your reference files. If you are running WES data, please include your capture region in this tarball. Please fill in the name of your exome region
tar -jcf reference.tar.bz2 reference.fa reference.fa.fai reference.dict
You will also need to put the RepeatMaster files into a separate tarball.
Please find the Cromwell documentation for a submission command that fits your specific HPC, but generally it would be run like this:
java -jar cromwell-83.jar run tortoise_1.2.wdl --inputs test_wdl_config.json
We also provide the Dockerfile if you would like to make modifications.
Below is a brief description of the main output folders from Hare:
- dv_bcf: Output folder for GLnexus .bcf files for DeepVariant output.
- hc_bcf: Output folder for GLnexus .bcf files for GATK HaplotypeCaller output.
- dv_vcf: Output folder for converted GLnexus .bcf files to .vcf.gz files for DeepVariant output. Also includes tabix index file.
- hc_vcf: Output folder for converted GLnexus .bcf files to .vcf.gz files for GATK HaplotypeCaller output. Also includes tabix index file.
- out_hare: Output folder where the de novo variant files can be found. If you are running multiple trios, each trio will have an individual folder, identified by the child ID.
The main output files are:
-
out_hare/<child_name>/<child_name>.glnexus.family.combined_intersection_filtered_gq_<gq_value>depth<depth_value>_position.vcf
- This file holds the de novo variants
-
out_hare/<child_name>/<child_name>.glnexus.family.combined_intersection_filtered_gq_<gq_value>depth<depth_value>_position_all.vcf
- This file holds the de novo variants specifically within CpG regions.
If you want to use the WES filter, please modify the config.json file in the wes_filtering folder. You can run the Snakemake with a docker command like this:
docker run -v "/path/to/script:/wes_filter" -v "/path/to/data:/data" tnturnerlab/tortoise:v1.2 /opt/conda/envs/snake/bin/snakemake -s wes_filter.smk --cores -s /wes_filter/wes_filter.smk --cores
This script will separate your WES DNVs into high and low confidence files. One as a bed file and another as a tab-delimited .txt file.
This current version has able to find almost all of the same de novo variants found from the original pipeline. The NA12878 trio from the 1000 Genomes Project 30x WGS data is used as an example:
- bcftools v1.11
- python v3.9.7
- tabix v1.11
- vcflib v1.0.0-rc0
- bedtools v2.29.2
- samtools v1.11
- snakemake v7.15.2-0
- python v2.7
- GLnexus v1.4.1
- pytabix v0.1
- pybedtools v0.9.0
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