Skip to content
/ vg_wdl Public

Workflow Description Language (WDL) scripts for common vg workflows

License

MIT license
16 stars 9 forks Branches Tags Activity
Star
Notifications You must be signed in to change notification settings

vgteam/vg_wdl

BranchesTags

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

508 Commits
params
params
 
 
scripts
scripts
 
 
tasks
tasks
 
 
tests
tests
 
 
workflows
workflows
 
 
.dockstore.yml
.dockstore.yml
 
 
.gitignore
.gitignore
 
 
.travis.yml
.travis.yml
 
 
LICENSE
LICENSE
 
 
README.md
README.md
 
 

Repository files navigation

vg_wdl

Eric T Dawson, Mike Lin and Charles Markello, Jean Monlong, Adam Novak MIT License, 2023

Workflow Description Language (WDL) scripts for vg workflows.

Workflows

See also the Going further section for more details on some aspects and HOW-TOs:

Giraffe-DeepVariant workflow

The full workflow to go from sequencing reads (FASTQs, CRAM) to small variant calls (VCF).

Parameters (semi-auto-generated from the parameter_meta section):

  • INPUT_READ_FILE_1: Input sample 1st read pair fastq.gz
  • INPUT_READ_FILE_2: Input sample 2nd read pair fastq.gz
  • INPUT_CRAM_FILE: Input CRAM file
  • CRAM_REF: Genome fasta file associated with the CRAM file
  • CRAM_REF_INDEX: Index of the fasta file associated with the CRAM file
  • GBZ_FILE: Path to .gbz index file
  • DIST_FILE: Path to .dist index file
  • MIN_FILE: Path to .min index file
  • SAMPLE_NAME: The sample name
  • OUTPUT_GAF: Should a GAF file with the aligned reads be saved? Default is 'true'.
  • OUTPUT_SINGLE_BAM: Should a single merged BAM file be saved? If yes, unmapped reads will be inluded and 'calling bams' (one per contig) won't be outputed. Default is 'true'.
  • PAIRED_READS: Are the reads paired? Default is 'true'.
  • READS_PER_CHUNK: Number of reads contained in each mapping chunk. Default 20 000 000.
  • PATH_LIST_FILE: (OPTIONAL) Text file where each line is a path name in the GBZ index, to use instead of CONTIGS. If neither is given, paths are extracted from the GBZ and subset to chromosome-looking paths.
  • CONTIGS: (OPTIONAL) Desired reference genome contigs, which are all paths in the GBZ index.
  • REFERENCE_PREFIX: Remove this off the beginning of path names in surjected BAM (set to match prefix in PATH_LIST_FILE)
  • REFERENCE_FILE: (OPTIONAL) If specified, use this FASTA reference instead of extracting it from the graph. Required if the graph does not contain all bases of the reference.
  • REFERENCE_INDEX_FILE: (OPTIONAL) If specified, use this .fai index instead of indexing the reference file.
  • REFERENCE_DICT_FILE: (OPTIONAL) If specified, use this pre-computed .dict file of sequence lengths. Required if REFERENCE_INDEX_FILE is set
  • LEFTALIGN_BAM: Whether or not to left-align reads in the BAM. Default is 'true'.
  • REALIGN_INDELS: Whether or not to realign reads near indels. Default is 'true'.
  • REALIGNMENT_EXPANSION_BASES: Number of bases to expand indel realignment targets by on either side, to free up read tails in slippery regions. Default is 160.
  • MIN_MAPQ: Minimum MAPQ of reads to use for calling. 4 is the lowest at which a mapping is more likely to be right than wrong. Default is 1
  • MAX_FRAGMENT_LENGTH: Maximum distance at which to mark paired reads properly paired. Default is 3000.
  • GIRAFFE_OPTIONS: (OPTIONAL) extra command line options for Giraffe mapper
  • TRUTH_VCF: (OPTIONAL) Path to .vcf.gz to compare against
  • TRUTH_VCF_INDEX: (OPTIONAL) Path to Tabix index for TRUTH_VCF
  • EVALUATION_REGIONS_BED: (OPTIONAL) BED to restrict comparison against TRUTH_VCF to
  • DV_MODEL_META: (OPTIONAL) .meta file for a custom DeepVariant calling model
  • DV_MODEL_INDEX: (OPTIONAL) .index file for a custom DeepVariant calling model
  • DV_MODEL_DATA: (OPTIONAL) .data-00000-of-00001 file for a custom DeepVariant calling model
  • DV_KEEP_LEGACY_AC: Should DV use the legacy allele counter behavior? Default is 'true'.
  • DV_NORM_READS: Should DV normalize reads itself? Default is 'false'.
  • OTHER_MAKEEXAMPLES_ARG: Additional arguments for the make_examples step of DeepVariant
  • SPLIT_READ_CORES: Number of cores to use when splitting the reads into chunks. Default is 8.
  • MAP_CORES: Number of cores to use when mapping the reads. Default is 16.
  • MAP_MEM: Memory, in GB, to use when mapping the reads. Default is 120.
  • CALL_CORES: Number of cores to use when calling variants. Default is 8.
  • CALL_MEM: Memory, in GB, to use when calling variants. Default is 50.

Related topics: read realignment, reference prefix removal, CRAM input, reads chunking, path list, single-end reads, unmapped reads, HPRC pangenomes.

Test locally with:

miniwdl run --as-me workflows/giraffe_and_deepvariant.wdl -i params/giraffe_and_deepvariant.json
miniwdl run --as-me workflows/giraffe_and_deepvariant.wdl -i params/giraffe_and_deepvariant_single_end.json
miniwdl run --as-me workflows/giraffe_and_deepvariant.wdl -i params/giraffe_and_deepvariant_cram.json

Giraffe workflow

Core VG Giraffe mapping, usable for DeepVariant. Reads are mapped to a pangenome with vg giraffe and pre-processed (e.g. indel realignment).

Parameters (semi-auto-generated from the parameter_meta section):

  • INPUT_READ_FILE_1: Input sample 1st read pair fastq.gz
  • INPUT_READ_FILE_2: Input sample 2nd read pair fastq.gz
  • INPUT_CRAM_FILE: Input CRAM file
  • CRAM_REF: Genome fasta file associated with the CRAM file
  • CRAM_REF_INDEX: Index of the fasta file associated with the CRAM file
  • GBZ_FILE: Path to .gbz index file
  • DIST_FILE: Path to .dist index file
  • MIN_FILE: Path to .min index file
  • SAMPLE_NAME: The sample name
  • OUTPUT_SINGLE_BAM: Should a single merged BAM file be saved? If yes, unmapped reads will be inluded and 'calling bams' (one per contig) won't be outputed. Default is 'true'.
  • OUTPUT_CALLING_BAMS: Should individual contig BAMs be saved? Default is 'false'.
  • OUTPUT_GAF: Should a GAF file with the aligned reads be saved? Default is 'false'.
  • PAIRED_READS: Are the reads paired? Default is 'true'.
  • READS_PER_CHUNK: Number of reads contained in each mapping chunk. Default 20 000 000.
  • PATH_LIST_FILE: (OPTIONAL) Text file where each line is a path name in the GBZ index, to use instead of CONTIGS. If neither is given, paths are extracted from the GBZ and subset to chromosome-looking paths.
  • CONTIGS: (OPTIONAL) Desired reference genome contigs, which are all paths in the GBZ index.
  • REFERENCE_PREFIX: Remove this off the beginning of path names in surjected BAM (set to match prefix in PATH_LIST_FILE)
  • REFERENCE_FILE: (OPTIONAL) If specified, use this FASTA reference instead of extracting it from the graph. Required if the graph does not contain all bases of the reference.
  • REFERENCE_INDEX_FILE: (OPTIONAL) If specified, use this .fai index instead of indexing the reference file.
  • REFERENCE_DICT_FILE: (OPTIONAL) If specified, use this pre-computed .dict file of sequence lengths. Required if REFERENCE_INDEX_FILE i
  • LEFTALIGN_BAM: Whether or not to left-align reads in the BAM. Default is 'true'.
  • REALIGN_INDELS: Whether or not to realign reads near indels. Default is 'true'.
  • REALIGNMENT_EXPANSION_BASES: Number of bases to expand indel realignment targets by on either side, to free up read tails in slippery regions. Default is 160.
  • MAX_FRAGMENT_LENGTH: Maximum distance at which to mark paired reads properly paired. Default is 3000.
  • GIRAFFE_OPTIONS: (OPTIONAL) extra command line options for Giraffe mapper
  • SPLIT_READ_CORES: Number of cores to use when splitting the reads into chunks. Default is 8.
  • MAP_CORES: Number of cores to use when mapping the reads. Default is 16.
  • MAP_MEM: Memory, in GB, to use when mapping the reads. Default is 120.
  • HAPLOTYPE_SAMPLING: Whether or not to use haplotype sampling before running giraffe. Default is 'true'
  • IN_DIPLOID:Whether or not to use diploid sampling while doing haplotype sampling. Has to use with Haplotype_sampling=true. Default is 'true'
  • HAPL_FILE: (OPTIONAL) Path to .hapl file used in haplotype sampling
  • R_INDEX_FILE: (OPTIONAL) Path to .ri file used in haplotype sampling
  • IN_KFF_FILE: (OPTIONAL) Path to .kff file used in haplotype sampling
  • IN_HAPLOTYPE_NUMBER: Number of generated synthetic haplotypes used in haplotype sampling. (Default: 4)

Related topics: read realignment, reference prefix removal, CRAM input, reads chunking, path list, single-end reads, unmapped reads, HPRC pangenomes, Haplotype Sampling.

Test locally with:

miniwdl run --as-me workflows/giraffe.wdl -i params/giraffe.json
miniwdl run --as-me workflows/giraffe.wdl -i params/giraffe.singleended.json
miniwdl run --as-me workflows/giraffe.wdl -i params/giraffe.singleended.cram.json
miniwdl run --as-me workflows/giraffe.wdl -i params/haplotype_sampling_and_giraffe.json

Giraffe-DeepVariant from GAF workflow

Surject a GAF and prepare the BAMs (e.g. fix names, indel realign), and call small variants with DeepVariant.

Parameters (semi-auto-generated from the parameter_meta section):

  • INPUT_GAF: Input gzipped GAF file
  • GBZ_FILE: Path to .gbz index file
  • SAMPLE_NAME: The sample name
  • OUTPUT_SINGLE_BAM: Should a single merged BAM file be saved? If yes, unmapped reads will be inluded and 'calling bams' (one per contig) won't be outputed. Default is 'true'.
  • PAIRED_READS: Are the reads paired? Default is 'true'.
  • PATH_LIST_FILE: (OPTIONAL) Text file where each line is a path name in the GBZ index, to use instead of CONTIGS. If neither is given, paths are extracted from the GBZ and subset to chromosome-looking paths.
  • CONTIGS: (OPTIONAL) Desired reference genome contigs, which are all paths in the GBZ index.
  • REFERENCE_PREFIX: Remove this off the beginning of path names in surjected BAM (set to match prefix in PATH_LIST_FILE)
  • REFERENCE_FILE: (OPTIONAL) If specified, use this FASTA reference instead of extracting it from the graph. Required if the graph does not contain all bases of the reference.
  • REFERENCE_INDEX_FILE: (OPTIONAL) If specified, use this .fai index instead of indexing the reference file.
  • REFERENCE_DICT_FILE: (OPTIONAL) If specified, use this pre-computed .dict file of sequence lengths. Required if REFERENCE_INDEX_FILE is set
  • LEFTALIGN_BAM: Whether or not to left-align reads in the BAM. Default is 'true'.
  • REALIGN_INDELS: Whether or not to realign reads near indels. Default is 'true'.
  • REALIGNMENT_EXPANSION_BASES: Number of bases to expand indel realignment targets by on either side, to free up read tails in slippery regions. Default is 160.
  • MIN_MAPQ: Minimum MAPQ of reads to use for calling. 4 is the lowest at which a mapping is more likely to be right than wrong. Default is 1
  • MAX_FRAGMENT_LENGTH: Maximum distance at which to mark paired reads properly paired. Default is 3000.
  • DV_MODEL_META: (OPTIONAL) .meta file for a custom DeepVariant calling model
  • DV_MODEL_INDEX: (OPTIONAL) .index file for a custom DeepVariant calling model
  • DV_MODEL_DATA: (OPTIONAL) .data-00000-of-00001 file for a custom DeepVariant calling model
  • DV_KEEP_LEGACY_AC: Should DV use the legacy allele counter behavior? Default is 'true'.
  • DV_NORM_READS: Should DV normalize reads itself? Default is 'fasle'.
  • OTHER_MAKEEXAMPLES_ARG: Additional arguments for the make_examples step of DeepVariant
  • VG_CORES: Number of cores to use when projecting the reads. Default is 16.
  • VG_MEM: Memory, in GB, to use when projecting the reads. Default is 120.
  • CALL_CORES: Number of cores to use when calling variants. Default is 8.
  • CALL_MEM: Memory, in GB, to use when calling variants. Default is 50.

Related topics: read realignment, reference prefix removal, path list, single-end reads, unmapped reads, HPRC pangenomes.

Test locally with:

miniwdl run --as-me workflows/giraffe_and_deepvariant_fromGAF.wdl -i params/giraffe_and_deepvariant_gaf.json
miniwdl run --as-me workflows/giraffe_and_deepvariant_fromGAF.wdl -i params/giraffe_and_deepvariant_gaf_single_end.json

Happy workflow

Evaluation of the small variant calls using hap.py.

Parameters (semi-auto-generated from the parameter_meta section):

  • VCF: bgzipped VCF with variant calls
  • VCF_INDEX: (Optional) If specified, use this tabix index for the VCF instead of indexing it
  • TRUTH_VCF: bgzipped VCF with truthset
  • TRUTH_VCF_INDEX: (Optional) If specified, use this index for the truth VCF instead of indexing it
  • REFERENCE_FILE: Use this FASTA reference.
  • REFERENCE_INDEX_FILE: (Optional) If specified, use this .fai index instead of indexing the reference file.
  • EVALUATION_REGIONS_BED: (Optional) BED to restrict comparison against TRUTH_VCF to
  • REFERENCE_PREFIX: (Optional) Remove this off the beginning of sequence names in the VCF

Test locally with:

miniwdl run --as-me workflows/happy_evaluation.wdl -i params/happy_evaluation.json

GAF to sorted GAM workflow

Currently, only GAM file can be sorted and indexed, for example to extract and subgraph and visualize, or use with the sequenceTubeMap. This workflow converts reads aligned to a pangenome in a GAF file to a sorted and indexed GAM file.

Parameters (semi-auto-generated from the parameter_meta section):

  • GAF_FILE: GAF file to convert and sort.
  • GBZ_FILE: the GBZ index of the graph
  • SAMPLE_NAME: (Optional) a sample name

Related topics: HPRC pangenomes.

Test locally with:

miniwdl run --as-me workflows/sort_graph_aligned_reads.wdl -i params/sort_graph_aligned_reads.gaf.json

Giraffe SV workflow

Workflow for mapping short reads and genotyping the structural variants in a pangenome.

Haplotype Sampling workflow

Workflow for creating a personalized pangenome with haplotype sampling.

Parameters (semi-auto-generated from the parameter_meta section):

  • IN_GBZ_FILE: Path to .gbz index file
  • INPUT_READ_FILE_FIRST: Input sample 1st read pair fastq.gz
  • INPUT_READ_FILE_SECOND: Input sample 2st read pair fastq.gz
  • HAPL_FILE: Path to .hapl file
  • IN_DIST_FILE: Path to .dist file
  • R_INDEX_FILE: Path to .ri file
  • KFF_FILE: Path to .kff file
  • IN_OUTPUT_NAME_PREFIX: Name of the output file (Default: haplotype_sampled_graph)
  • IN_KMER_LENGTH: Size of kmer using for sampling (Up to 31) (Default: 29)
  • CORES: Number of cores to use with commands. (Default: 16)
  • WINDOW_LENGTH: Window length used for building the minimizer index. (Default: 11)
  • SUBCHAIN_LENGTH: Target length (in bp) for subchains. (Default: 10000)
  • HAPLOTYPE_NUMBER: Number of generated synthetic haplotypes. (Default: 4)
  • PRESENT_DISCOUNT: Multiplicative factor for discounting scores for present kmers. (Default: 0.9)
  • HET_ADJUST: Additive term for adjusting scores for heterozygous kmers. (Default: 0.05)
  • ABSENT_SCORE: Score for absent kmers. (Default: 0.8)
  • INCLUDE_REFERENCE: Include reference paths and generic paths from the full graph in the sampled graph. (Default: true)
  • DIPLOID: Activate diploid sampling. (Default: true)

Test locally with:

miniwdl run --as-me workflows/haplotype_sampling.wdl -i params/haplotype_sampling.json

Map-call workflow

Map-call Pedigree workflow

Going further

See below more information about: read realignment, reference prefix removal, CRAM input, reads chunking, path list, single-end reads, unmapped reads, HPRC pangenomes.

Read realignment

Once the reads are projected to a linear reference, we've noticed that realigning the reads can improve the variant calling with DeepVariant. This helps mostly for the small insertions-deletions (indels).

The full realignment process involves:

  1. Leftaligning the reads with freebayes' bamleftalign.
    • Can be enabled/disabled with the LEFTALIGN_BAM parameter
  2. Identifying regions to realign further with GATK RealignerTargetCreator.
  3. Expand those regions with bedtools.
    • Number of bases to expand controlled by the REALIGNMENT_EXPANSION_BASES parameter.
  4. Realigning the reads in those regions with ABRA2.

The last 3 steps can be enabled/disabled with the REALIGN_INDELS parameter.

Although it produces the best variant calls, these extra steps increase the computational resources (and cost) of the workflow. For a lighter run, switch off those two realignment steps and use DeepVariant's integrated realigner instead with:

  • LEFTALIGN_BAM=false
  • REALIGN_INDELS=false
  • DV_NORM_READS=true

Reference prefix removal

The names of contigs/paths/haplotypes in pangenomes sometimes contains a prefix that we'd want to remove. In the HPRC pangenomes, for example, the chromosomal contigs from GRCh38 are named GRCh38.chr1, etc. In practice, we want to remove this prefix from the variant calls (VCFs), or reads aligned to that reference (BAMs).

This is controlled by the REFERENCE_PREFIX parameters in the workflows. Setting REFERENCE_PREFIX="GRCh38." for example will ensure the VCFs/BAMs have chr1, etc. for contig names.

Because the pangenome uses them, the prefix must still be present when specifying the paths to project the reads too though. Hence, the CONTIGS and PATH_LIST_FILE must use the prefix.

However, provided reference FASTAs or dictionary must not have the prefix. These could be FASTA or .dict files from the "official" reference genome or pre-computed for them, hence no prefix. So, no prefix in REFERENCE_FILE, REFERENCE_INDEX_FILE, REFERENCE_DICT_FILE.

CRAM input

When the input is a CRAM file (INPUT_CRAM_FILE) instead of a pair of FASTQ files (INPUT_READ_FILE_1/INPUT_READ_FILE_2), the user must also provide the appropriate reference FASTA to work with that CRAM file with CRAM_REF, and its index with CRAM_REF_INDEX.

The CRAM file will be converted back to a pair of FASTQs, so it costs a little bit more to analyze CRAMs than FASTQs currently.

Reads chunking

Sequencing reads are chunked to parallelize read mapping. The amount of chunking is controlled by the READS_PER_CHUNK parameter which specify how many reads each chunk should have. For a WGS experiment, we use chunks of about 20M reads.

Path list

We might not always want to project the reads alignments to all the paths in the pangenome. For example, we might only care about alignment to chromosomes and not alternate contigs. Or there might be multiple sets of paths like in the CHM13-based HPRC pangenome which contains both reference paths for CHM13 and GRCh38. In that case, we can specify a list of paths to project the reads to using one of the following.

PATH_LIST_FILE is a file which lists the paths names, one per line. For the HPRC pangenomes it looks like:

GRCh38.chr1
GRCh38.chr2
GRCh38.chr3
...etc

Otherwise, paths can be listed in the CONTIGS parameter as a list (WDL array).

Single-end reads

Workflows expect paired-end reads, but some workflows can also analyze single-end reads.

To use single-end reads:

  • If providing FASTQs, only provide INPUT_READ_FILE_1 (no INPUT_READ_FILE_2).
  • Use PAIRED_READS=false

Unmapped reads

If including unmapped reads in the BAMs is important, make sure to switch on OUTPUT_SINGLE_BAM=true in the Giraffe-DeepVariant workflow and Giraffe workflow.

HPRC pangenomes

We recommend using the filtered CHM13-based pangenome (freeze 1). It contains both the CHM13 and GRCh38 reference paths.

Use the following indexes for the pangenome:

To project reads and call variants relative to the GRCh38 reference:

  • REFERENCE_PREFIX="GRCh38."
  • PATH_LIST_FILE containing GRCh38.chr1, GRCh38.chr2, etc. File available at GRCh38.path_list.txt
  • REFERENCE_FILE: hg38.fa
  • REFERENCE_INDEX_FILE: hg38.fa.fai. Optional, the workflow will create it if necessary (for a small extra cost/time).
  • REFERENCE_DICT_FILE: hg38.dict. Optional, the workflow will create it if necessary (for a small extra cost/time).

To project reads and call variants relative to the CHM13 reference:

For earlier versions of DeepVariant (<1.5), models were retrained using reads aligned to the HPRC pangenomes. The corresponding model files were deposited at: https://s3-us-west-2.amazonaws.com/human-pangenomics/index.html?prefix=publications/PANGENOME_2022/DeepVariant/models/DEEPVARIANT_MC_Y1/. They can be passed to the workflows using the DV_MODEL_META, DV_MODEL_INDEX, and DV_MODEL_DATA. Note that it is not necessary to use custom models in the latest version of the workflows as DeepVariant v1.5 includes default models suited for analyzing reads mapped to pangenomes (and projected back to a linear reference).

Usage

Dockstore

The workflows that were deposited on Dockstore can be launched using its command line or on platform like Terra.

Using miniwdl

Install miniwdl, for example, with pip:

pip3 install miniwdl

Clone this repo somewhere with git clone https://github.com/vgteam/vg_wdl.git

Run a workflow using:

miniwdl run /path/to/vg_wdl/workflows/WORKFLOW.wdl -i your-inputs.json

To modify the input parameters, edit the input .json with the necessary changes.

Using Cromwell

Cromwell can be run WDL workflows with:

java -jar $CROMWELL_JAR run workflow.wdl -i inputs.json

Where CROMWELL_JAR points at the Cromwell jar downloaded their release page, for example set with CROMWELL_JAR=/path/to/cromwell-<whatever>.jar in your shell.

To run one of the workflows in this repo, clone the repo somewhere with git clone https://github.com/vgteam/vg_wdl.git and run the desired workflow .wdl file:

java -jar $CROMWELL_JAR run /path/to/vg_wdl/workflows/WORKFLOW.wdl -i inputs.json

Docker Containers

WDL needs the runtime Docker image to be present online (e.g. Dockerhub). Cromwell/miniwdl will pull those images automatically. VG images are available at quay.io and can be pulled with:

docker pull quay.io/vgteam/vg:v1.44.0

Specific versions can be specified like above for version v1.44.0.

Testing locally

To test the workflow locally, e.g. on the small simulated dataset, you can run it with Cromwell or miniwdl (see Usage). So, from the root of this repo, run something like:

java -jar $CROMWELL_JAR run workflows/WORKFLOW.wdl -i params/INPUTS.json
## or
miniwdl run --as-me workflows/WORKFLOW.wdl -i params/INPUTS.json

Miniwdl might be slightly more useful when developing/testing a WDL because is catches errors in WDL syntax faster, and is a bit more explicit about them.

Citation

Cite HPRC

If you use the Giraffe-DeepVariant workflows, please cite the HPRC preprint:

Liao, Asri, Ebler, et al. A Draft Human Pangenome Reference. preprint, bioRxiv 2022; doi: https://doi.org/10.1101/2022.07.09.499321

Cite Giraffe-SV

If you use the SV genotyping workflow with vg giraffe, please cite this article:

Sirén, Monlong, Chang, Novak, Eizenga, et al. Pangenomics Enables Genotyping of Known Structural Variants in 5202 Diverse Genomes. Science, vol. 374, no. 6574, Dec. 2021; doi: https://doi.org/10.1126/science.abg8871.

Cite Pedigree-VG

If you use the pedigree-based workflow for rare variant discovery, please cite this article:

Markello et al. A Complete Pedigree-Based Graph Workflow for Rare Candidate Variant Analysis. Genome Research, Apr. 2022; doi: https://doi.org/10.1101/gr.276387.121.

Contributing, Help, Bugs and Requests

Please open an Issue on GitHub for help, bug reports, or feature requests. When doing so, please remember that vg_wdl is open-source software made by a community of developers. Please be considerate and support a positive environment.

About

Workflow Description Language (WDL) scripts for common vg workflows

Resources

Readme

License

MIT license
Activity
Custom properties

Stars

16 stars

Watchers

8 watching

Forks

9 forks
Report repository

Releases 1

SV genotyping with giraffe Latest
Oct 31, 2020

Packages

No packages published

Contributors 6

Languages