Human variation workflow

SNV, SV and CNV calling, modified base calling, and STR genotyping of human samples.

Introduction

This repository contains a nextflow workflow for analysing variation in human genomic data. Specifically this workflow can perform the following:

diploid variant calling
structural variant calling
analysis of modified base calls
copy number variant calling
short tandem repeat (STR) expansion genotyping

The wf-human-variation workflow consolidates the small variant calling from the previous wf-human-snp, structural variant calling from wf-human-sv, CNV calling from wf-cnv (all of which are now deprecated), as well as performing STR expansion genotyping. This pipeline performs the steps of the four pipelines simultaneously and the results are generated and output in the same way as they would have been had the pipelines been run separately.

Please note, the tools embedded in individual sub-workflows within wf-human-variation are intended for use with 20x whole-genome Oxford Nanopore Technologies sequencing data (with the exception of QDNAseq, please see this section for more information). Usage outside of this (e.g. with adaptive sampling data, or using lower coverage inputs) may cause the workflow to terminate with an error, or produce unexpected results.

Compute requirements

Recommended requirements:

CPUs = 32
Memory = 128GB

Minimum requirements:

CPUs = 16
Memory = 32GB

Approximate run time: Variable depending on whether it is targeted sequencing or whole genome sequencing, as well as coverage and the individual analyses requested. For instance, a 90X human sample run (options: --snp --sv --mod --str --cnv --phased --sex male) takes less than 8h with recommended resources.

ARM processor support: False

Install and run

These are instructions to install and run the workflow on command line. You can also access the workflow via the EPI2ME Desktop application.

The workflow uses Nextflow to manage compute and software resources, therefore Nextflow will need to be installed before attempting to run the workflow.

The workflow can currently be run using either Docker or Singularity to provide isolation of the required software. Both methods are automated out-of-the-box provided either Docker or Singularity is installed. This is controlled by the -profile parameter as exemplified below.

It is not required to clone or download the git repository in order to run the workflow. More information on running EPI2ME workflows can be found on our website.

The following command can be used to obtain the workflow. This will pull the repository in to the assets folder of Nextflow and provide a list of all parameters available for the workflow as well as an example command:

nextflow run epi2me-labs/wf-human-variation --help

To update a workflow to the latest version on the command line use the following command:

nextflow pull epi2me-labs/wf-human-variation

A demo dataset is provided for testing of the workflow. It can be downloaded and unpacked using the following commands:

wget https://ont-exd-int-s3-euwst1-epi2me-labs.s3.amazonaws.com/wf-human-variation/hg002%2bmods.v1/wf-human-variation-demo.tar.gz
tar -xzvf wf-human-variation-demo.tar.gz

The workflow can then be run with the downloaded demo data using:

nextflow run epi2me-labs/wf-human-variation \
	--bam 'wf-human-variation-demo/demo.bam' \
	--ref 'wf-human-variation-demo/demo.fasta' \
	--bed 'wf-human-variation-demo/demo.bed' \
	--sample_name 'DEMO' \
	--snp \
	--sv \
	--mod \
	--phased \
	-profile standard

For further information about running a workflow on the command line see https://labs.epi2me.io/wfquickstart/

Related protocols

This workflow is designed to take input sequences that have been produced from Oxford Nanopore Technologies devices.

Find related protocols in the Nanopore community.

Input example

The --bam input parameter for this workflow accepts a path to a single BAM file, or a folder containing multiple BAM files for the sample. A sample name can be supplied with --sample.

(i)                     (ii)    
input_reads.bam     ─── input_directory
                        ├── reads0.bam
                        └── reads1.bam

Input parameters

Workflow Options

Nextflow parameter name	Type	Description	Help	Default
sv	boolean	Call for structural variants.	If this option is selected, structural variant calling will be carried out using Sniffles2.	False
snp	boolean	Call for small variants	If this option is selected, small variant calling will be carried out using Clair3.	False
cnv	boolean	Call for copy number variants.	If this option is selected, copy number variant calling will be carried out with either Spectre (default) or QDNAseq. To use QDNAseq instead of Spectre, use the option `--use_qdnaseq`. Spectre is only compatible with genome build hg38, and if QDNAseq is used, it is only compatible with genome builds hg37 and hg38.	False
str	boolean	Enable Straglr to genotype STR expansions.	If this option is selected, genotyping of STR expansions will be carried out using Straglr. This sub-workflow is only compatible with genome build hg38.	False
mod	boolean	Enable output of modified calls to a bedMethyl file [requires input BAM with Ml and Mm tags]	This option is automatically selected and aggregation of modified calls with be carried out using modkit if Ml and Mm tags are found. Disable this option to prevent output of a bedMethyl file.	False

Main options

Nextflow parameter name	Type	Description	Help	Default
sample_name	string	Sample name to be displayed in workflow outputs.		SAMPLE
bam	string	BAM or unaligned BAM (uBAM) files for the sample to use in the analysis.	This accepts one of two cases: (i) the path to a single BAM file; (ii) the path to a top-level directory containing BAM files. A sample name can be supplied with `--sample`.
ref	string	Path to a reference FASTA file.	Reference against which to compare reads for variant calling.
bam_min_coverage	number	Minimum read coverage required to run analysis.		20
bed	string	An optional BED file enumerating regions to process for variant calling.
annotation	boolean	SnpEff annotation.	If this option is unselected, VCFs will not be annotated with SnpEff.	True
phased	boolean	Perform phasing.	This option enables phasing of SV, SNP and modifications, depending on which sub-workflow has been chosen; see README for more details.	False
include_all_ctgs	boolean	Call for variants on all sequences in the reference, otherwise small and structural variants will only be called on chr{1..22,X,Y,MT}.	Enabling this option will call for variants on all contigs of the input reference sequence. Typically this option is not required as standard human reference sequences contain decoy and unplaced contigs that are usually omitted for the purpose of variant calling. This option might be useful for non-standard reference sequence databases.	False
output_gene_summary	boolean	If set to true, the workflow will generate gene-level coverage summaries.	If set to true, a 4-column BED file must be supplied, where column 4 is the gene label. The workflow will generate a list of all genes in the BED and their percentage coverage at a range of thresholds (1x, 10x, 15x, 20x, and 30x), as well as the average coverage of each gene.	False
igv	boolean	Visualize outputs in the EPI2ME IGV visualizer.	Enabling this option will visualize the alignments and VCF files in the EPI2ME desktop app IGV visualizer.	False
out_dir	string	Directory for output of all workflow results.		output

Copy number variant calling options

Nextflow parameter name	Type	Description	Help	Default
use_qdnaseq	boolean	Use QDNAseq for CNV calling.	Set this to true to use QDNASeq for CNV calling instead of Spectre. QDNAseq is better suited to shorter reads such as those generated from adaptive sampling experiments.	False
qdnaseq_bin_size	integer	Bin size for QDNAseq in kbp.	Pre-computed bin annotations are available for a range of bin sizes. Larger sizes reduce noise, however this may result in reduced sensitivity.	500

Modified base calling options

Nextflow parameter name	Type	Description	Help	Default
force_strand	boolean	Require modkit to call strand-aware modifications.	By default strand calls are collapsed (strand reported as '.'). Enabling this will force stranding to be considered when calling modifications, creating one output per modification per strand and the report will be tabulated by both modification and strand.	False

Short tandem repeat expansion genotyping options

Nextflow parameter name	Type	Description	Help	Default
sex	string	Sex (XX or XY) to be passed to Straglr-genotype.	The sex determines how many calls will be obtained for all repeats on chrX. If not specified, the workflow will naively attempt to infer whether the sample carries XX or XY based on relative coverage of the allosomes.

Advanced Options

Nextflow parameter name	Type	Description	Help	Default
depth_intervals	boolean	Output a bedGraph file with entries for each genomic interval featuring homogeneous depth.	The output bedGraph file will have an entry for each genomic interval in which all positions have the same alignment depth. By default this workflow outputs summary depth information from your aligned reads. Per-base depth outputs are slower to generate but may be required for some downstream applications.	False
GVCF	boolean	Enable to output a gVCF file in addition to the VCF outputs (experimental).	By default the the workflow outputs a VCF file containing only records where a variant has been detected. Enabling this option will output additionally a gVCF with records spanning all reference positions regardless of whether a variant was detected in the sample.	False
downsample_coverage	boolean	Downsample the coverage to along the genome.	This options will trigger a downsampling of the read alignments to the target coverage specified by --downsample_coverage_target. Downsampling will make the workflow run faster but could lead to non-deterministic variant calls.	False
downsample_coverage_target	number	Average coverage or reads to use for the analyses.	This options will set the target coverage for the downsampling stage, if downsampling has been enabled.	60
output_xam_fmt	string	Desired file format of alignment files created by alignment and phasing.	This setting controls the file format of (1) alignment files created by aligning or re-aligning an input BAM and (2) alignment files with haplotag information created during phasing of an input BAM. If using QDNASeq for CNV calling, the setting will be ignored for alignment or realignment as QDNASeq requires BAM input.	cram
modkit_args	string	The additional options for modkit.	This is an advanced option to allow running modkit with custom settings. The arguments specified in this option will fully override all options set by the workflow. To provide custom arguments to modkit pileup from command line proceed as follows: `--modkit_args="--preset traditional"`
sniffles_args	string	Additional command line arguments to pass to the Sniffles2 process	The additional command line arguments will be passed directly to Sniffles2; ensure to use the right commands for the version and from command line provide them as follows: `--sniffles_args="--non-germline"`.
override_basecaller_cfg	string	Name of the model to use for selecting a small variant calling model.	The workflow will attempt to find the basecaller model from the headers of your input data, providing a value for this option will override the model found in the data. If the model cannot be found in the header, it must be provided with this option as the basecaller model is required for small variant calling. The basecaller model is used to automatically select the appropriate small variant calling model. The model list shows all models that are compatible for small variant calling with this workflow. You should select 'custom' to override the basecaller_cfg with clair3_model_path.
tr_bed	string	Input BED file containing tandem repeat annotations for the reference genome.	Providing a tandem repeat BED can improve calling in repetitive regions. The workflow will attempt to select an appropriate hg19 or hg38 TR BED depending on the genome detected, but this can be overridden with a custom TR BED with this parameter.

Multiprocessing Options

Nextflow parameter name	Type	Description	Default
threads	integer	Set max number of threads to use for more intense processes (limited by config executor cpus)	4
ubam_map_threads	integer	Set max number of threads to use for aligning reads from uBAM (limited by config executor cpus)	8
ubam_sort_threads	integer	Set max number of threads to use for sorting and indexing aligned reads from uBAM (limited by config executor cpus)	3
ubam_bam2fq_threads	integer	Set max number of threads to use for uncompressing uBAM and generating FASTQ for alignment (limited by config executor cpus)	1
modkit_threads	integer	Total number of threads to use in modkit modified base calling (limited by config executor cpus)	4

Outputs

Output files may be aggregated including information for all samples or provided per sample. Per-sample files will be prefixed with respective aliases and represented below as {{ alias }}.

Title	File path	Description	Per sample or aggregated
Report of the alignment statistics	{{ alias }}.wf-human-alignment-report.html	Report summarising the results of the alignment statistics for the sample.	per-sample
JSON file of some base statistics	{{ alias }}.stats.json	This JSON file contains base statistics on the reads, mappings, SNPs and SVs for the sample.	per-sample
Report of the SNP workflow	{{ alias }}.wf-human-snp-report.html	Report summarising the results of the SNP subworkflow for the sample.	per-sample
Report of the SV workflow	{{ alias }}.wf-human-sv-report.html	Report summarising the results of the SV subworkflow for the sample.	per-sample
Report of the CNV workflow	{{ alias }}.wf-human-cnv-report.html	Report summarising the results of the CNV subworkflow for the sample.	per-sample
Report of the STR workflow	{{ alias }}.wf-human-str-report.html	Report summarising the results of the short tandem repeat subworkflow for the sample.	per-sample
Short variant VCF	{{ alias }}.wf_snp.vcf.gz	VCF file with the SNPs for the sample.	per-sample
Structural variant VCF	{{ alias }}.wf_sv.vcf.gz	VCF file with the SVs for the sample.	per-sample
Structural variant SNF	{{ alias }}.wf_sv.snf	SNF file with the SVs for the sample, for onward multi-sample SV calling.	per-sample
Copy number variants VCF	{{ alias }}.wf_cnv.vcf.gz	VCF file with the CNV for the sample.	per-sample
Modified bases BEDMethyl	{{ alias }}.wf_mods.bedmethyl.gz	BED file with the aggregated modification counts for the sample.	per-sample
Modified bases BEDMethyl (haplotype 1)	{{ alias }}.wf_mods.1.bedmethyl.gz	BED file with the aggregated modification counts for haplotype 1 of the sample.	per-sample
Modified bases BEDMethyl (haplotype 2)	{{ alias }}.wf_mods.2.bedmethyl.gz	BED file with the aggregated modification counts for haplotype 2 of the sample.	per-sample
Modified bases BEDMethyl (ungrouped)	{{ alias }}.wf_mods.ungrouped.bedmethyl.gz	BED file with the aggregated modification counts of non-haplotagged reads for the sample.	per-sample
Short tandem repeat VCF	{{ alias }}.wf_str.vcf.gz	VCF file with the STR sites for the sample.	per-sample
Alignment file	{{ alias }}.cram	CRAM or BAM file with the aligned reads for the sample, generated when the input file is unaligned.	per-sample
Alignment file index	{{ alias }}.cram.crai	The index of the resulting CRAM or BAM file with the reads for the sample, generated when the input file is unaligned.	per-sample
Haplotagged alignment file	{{ alias }}.haplotagged.cram	CRAM or BAM file with the haplotagged reads for the sample.	per-sample
Haplotagged alignment file index	{{ alias }}.haplotagged.cram.crai	The index of the resulting CRAM or BAM file with the haplotagged reads for the sample.	per-sample
Mean coverage for each region	{{ alias }}.regions.bed.gz	The mean coverage in the individual regions of the genome in BED format.	per-sample
Coverage per region above the given thresholds	{{ alias }}.thresholds.bed.gz	The BED reporting the number of bases in each region that are covered at or above each threshold values (1x, 10x, 20x and 30x).	per-sample
Distribution of the proportion of total bases covered by a given coverage value	{{ alias }}.mosdepth.global.dist.txt	The cumulative distribution indicating the proportion of total bases covered by a given coverage value, both genome-wide and by sequence.	per-sample
Mean coverage per sequence and target region	{{ alias }}.mosdepth.summary.txt	The summary of mean depths per chromosome and within specified regions per chromosome.	per-sample
BEDgraph of the single-base coverage	{{ alias }}.per-base.bedgraph.gz	The single-base coverage of the genome in BED graph format.	per-sample
Gene level coverage summary	SAMPLE.gene_summary.tsv	A table where each gene of the input BED file has columns describing the percentage of positions along the gene region that are covered to a given threshold, and a column with the average coverage.	per-sample
Haplocheck contamination summary	{{ alias }}.haplocheck.tsv	A table generated by haplocheck, with estimate of contamination from the MT genome.	per-sample
FAI index of the reference FASTA file	{{ ref }}.fai	FAI Index of the reference FASTA file.	aggregated
GZI index of the reference FASTA file	{{ ref }}.gzi	GZI Index of the reference FASTA file.	aggregated

Pipeline overview

The workflow is composed of 6 distinct subworkflows, each enabled by a command line option:

SNP calling: --snp
SV calling: --sv
Analysis of modified bases: --mod
CNV calling (Spectre): --cnv
CNV calling (QDNAseq): --cnv --use_qdnaseq
STR genotyping: --str

Subworkflows where the relevant option is omitted will not be run.

1. Input and data preparation

The workflow relies on two primary input files:

A reference genome in FASTA format
Sequencing data for the sample in the form of a single BAM file or a folder of BAM files, either aligned or unaligned.

When analysing human data, we recommend using human_g1k_v37.fasta.gz or GCA_000001405.15_GRCh38_no_alt_analysis_set.fna.gz. For more information see this blog post which outlines potential pitfalls with the various flavours of human references.

The input BAM file can be generated using the wf-basecalling workflow, which is up to date with the current dorado releases and models.

2. Data QC and pre-processing

The workflow starts by performing multiple checks of the input BAM file, as well as computing:

depth of sequencing with mosdepth;
read alignment statistics with fastcat.

After computing the coverage, the workflow will check that the input BAM file has a depth greater than --bam_min_coverage. In case the user specify --bam_min_coverage 0, the check will be skipped and the workflow will proceed directly to the downstream analyses. Some components work better withing certain ranges of coverage, and the user might achieve better results by providing a target coverage to downsample to. The user can set --downsample_coverage true to enable the downsampling of the reads, and --downsample_coverage_target {{ X }} to specify the target coverage (default: 60x).

An optional gene summary may also be generated by specifying --output_gene_summary. A 4-column BED file is required for this, where the fourth column contains the gene label.

3. Small variant calling with Clair3

The workflow implements a deconstructed version of Clair3 (v1.0.4) to call germline variants. The workflow will select an appropriate Clair3 model by detecting the basecall model from the input data. If the input data does not have the required information to determine the basecall model, the workflow will require the basecall model to be provided explicitly with the --override_basecall_cfg option. This workflow takes advantage of the parallel nature of Nextflow, providing optimal efficiency in high-performance, distributed systems. The workflow will automatically call small variants (SNPs and indels), collect statistics, annotate them with SnpEff (and additionally for SNPs, ClinVar details), and create a report summarising the findings.

If desired, the workflow can perform phasing of structural variants by using the --phased option. This will lead the workflow to use whatshap to perform phasing of the variants, with the option to use longphase instead by setting --use_longphase true. The phasing will also generate a GFF file with the annotation of the phase blocks, enabling the visualisation of these blocks in genome browsers.

4. Structural variant (SV) calling with Sniffles2

The workflow allows for calling of SVs using long-read sequencing data with Sniffles2. The workflow will perform SV calling, filtering and generation of a report. The SV workflow can accept a tandem repeat annotations BED file to improve calling in repetitive regions --- see the sniffles documentation for more information. The workflow will attempt to select an appropriate hg19 or hg38 TR BED but you can override the BED file used with the --tr_bed parameter. SVs can be phased using --phased. However, this will cause the workflow to run SNP analysis, as SV phasing relies on the haplotagged reads generated in this stage.

5. Modified base calling with modkit

Modified base calling can be performed by specifying --mod. The workflow will call modified bases using modkit. The workflow will automatically check whether the files contain the appropriate MM/ML tags, required for running modkit pileup. If the tags are not found, the workflow will not run the individual analysis, but will still run the other subworkflows requested by the user. The default behaviour of the workflow is to run modkit with the --cpg --combine-strands options set. It is possible to report strand-aware modifications by providing --force_strand, which will trigger modkit to run in default mode. The resulting bedMethyl will include modifications for each site on each strand separately. The modkit run can be fully customized by providing --modkit_args. This will override any preset, and allow full control over the run of modkit. Haplotype-resolved aggregated counts of modified bases can be obtained with the --phased option. This will generate three distinct BEDMethyl files with the naming pattern {{ alias }}.wf_mods.{{ haplotype }}.bedmethyl.gz, where haplotype can be 1, 2 or ungrouped.

6a. Copy number variants (CNV) calling with Spectre

CNV calling is performed using a fork of Spectre, using the --cnv flag. Spectre is the default CNV caller in the workflow, and is compatible with hg38/GRCh38. The output of this workflow is a VCF of CNV calls, annotated with SnpEff.

6b. Copy number variants (CNV) calling with QDNASeq

CNV calling may alternatively be performed using QDNAseq, using --cnv --use_qdnaseq. This workflow is compatible with genome builds hg19/GRCh37 or hg38/GRCh38, and is recommended for shallow WGS or adaptive sampling data. In addition to the VCF of CNV calls, the workflow emits QDNAseq-generated plots and BED files of both raw read counts per bin and corrected, normalised, and smoothed read counts per bin. Please note that QDNAseq was the default CNV caller until version 1.11.0 of the workflow, and the additional --use_qdnaseq flag is now required to use it.

7. Short tandem repeat (STR) genotyping with Straglr

STR genotyping is performed using a fork of straglr. This workflow is compatible with genome build hg38/GRCh38. The number of calls for repeats on chrX is dependent on the sample's genetic sex which should be provided if known with --sex XX or --sex XY. If --sex is not specified, the workflow will attempt to infer the genetic sex from coverage of the allosomes, falling back to XX if a determination is unclear. Please be aware that incorrect sex assignment will result in the wrong number of calls for all repeats on chrX. In addition to a gzipped VCF file containing STRs found in the dataset, the workflow emits a TSV straglr output containing reads spanning STRs, and a haplotagged BAM.

8. Phasing variants

Variant phasing is switched on simply using the --phased option. By default, the workflow uses whatshap to perform phasing of the variants, with the option to use longphase instead by setting --use_longphase true. The workflow will automatically turn on the necessary phasing processes based on the selected subworkflows. The behaviour of the phasing is summarised in the below table:

				Phased SNP VCF	Phased SV VCF	Phased bedMethyl
`--snp`	`--sv`	`--mod`	`--phased`	✓	✓	✓
`--snp`	`--sv`		`--phased`	✓	✓
`--snp`			`--phased`	✓
	`--sv`		`--phased`		✓
		`--mod`	`--phased`			✓

Using --GVCF together with --phased will generate a phased GVCF, created by reflecting the phased genotype and the phase set annotation in the VCF file. This operation is performed using bcftools annotate, targeting the GT and PS fields.

Running the phasing is a compute intensive process. Running the workflow in phasing mode doubles the runtime, and significantly increases the storage requirements to the order of terabytes.

9. Variant annotation

Annotation will be performed automatically by the SNP and SV subworkflows, and can be disabled by the user with --annotation false. The workflow will annotate the variants using SnpEff, and currently only support the human hg19 and hg38 genomes. Additionally, the workflow will add the ClinVar annotations for the SNP variants.

Running the workflow on non-human samples will require this option to be disabled. For more detail, see Section 10 below.

10. Genome compatibility and running the workflow on non-human genomes

Some of the sub-workflows in wf-human-variation are restricted to certain genome builds, which means they will not be executable on non-human genomes or human genome builds outside hg19/GRCh37 and hg38/GRCh38. The following table summarises which subworkflows and options are available (or required) for a desired input genome:

Genome	`--snp`	`--sv`	`--mod`	`--cnv`	`--cnv --use_qdnaseq`	`--str`	`--annotation false`	`--include_all_ctgs`
hg19/GRCh37	✓	✓	✓		✓		*
hg38/GRCh38	✓	✓	✓	✓	✓	✓	*
Other human	✓	✓	✓				✓
Non human	✓	✓	✓				✓	✓

* As noted above, annotation is performed by default but can be switched off for hg19/GRCh37 and hg38/GRCh38.

Troubleshooting

Annotations for --snp and --sv are generated using SnpEff. For --snp, additional ClinVar annotations are displayed in the report where available (please note, the report will not display any variants classified as 'Benign' or 'Likely benign', however these variants will be present in the output VCF).
Aggregation of modified calls with --mod requires data to be basecalled with a model that includes base modifications, providing the MM and ML BAM tags
CRAM files generated within the workflow cannot be read without the corresponding reference
The STR workflow performs genotyping of specific repeats, which can be found here.
By default, SNPs and SVs will be called on chromosomes 1-22, X, Y and MT; to call sites on other sequences enable the --include_all_ctgs option.
While designed to work on human genomes, the workflow can be run on non-human species by setting --cnv false --str false --annotation false --include_all_ctgs true.
Ensure that the provided reference and BED files use the same chromosome coding (for example, that they both have the chr prefix, or they both to not have it).
If unaligned reads were provided, the workflow will output a CRAM file (or BAM if the user runs the --cnv option) containing the alignments used to make the downstream variant calls.
Renaming, moving or deleting the input BAM, reference genome or the output directory from the location provided at runtime will cause IGV not to load.
The workflow expects either an uncompressed or bgzip-compressed reference. If the user provides a reference compressed not with bgzip, the workflow will run to completion, but won't be able to generate the necessary indexes to visualize the outputs in IGV.

FAQ's

If your question is not answered here, please report any issues or suggestions on the github issues page or start a discussion on the community.

The number of SNVs and indels in the report do not sum up to the number of record, is that normal? - Yes; this can be due to some multiallelic sites carrying a mixture of SNV and indel alleles.

Name		Name	Last commit message	Last commit date
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.github/ISSUE_TEMPLATE		.github/ISSUE_TEMPLATE
bin		bin
data		data
docs		docs
lib		lib
modules/local		modules/local
util		util
workflows		workflows
.dockerignore		.dockerignore
.gitignore		.gitignore
.gitlab-ci.yml		.gitlab-ci.yml
.pre-commit-config.yaml		.pre-commit-config.yaml
CHANGELOG.md		CHANGELOG.md
LICENSE		LICENSE
README.md		README.md
base.config		base.config
main.nf		main.nf
nextflow.config		nextflow.config
nextflow_schema.json		nextflow_schema.json
output_definition.json		output_definition.json
tower.yml		tower.yml

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Repository files navigation

Human variation workflow

Introduction

Compute requirements

Install and run

Related protocols

Input example

Input parameters

Workflow Options

Main options

Copy number variant calling options

Modified base calling options

Short tandem repeat expansion genotyping options

Advanced Options

Multiprocessing Options

Outputs

Pipeline overview

1. Input and data preparation

2. Data QC and pre-processing

3. Small variant calling with Clair3

4. Structural variant (SV) calling with Sniffles2

5. Modified base calling with modkit

6a. Copy number variants (CNV) calling with Spectre

6b. Copy number variants (CNV) calling with QDNASeq

7. Short tandem repeat (STR) genotyping with Straglr

8. Phasing variants

9. Variant annotation

10. Genome compatibility and running the workflow on non-human genomes

Troubleshooting

FAQ's

Related blog posts

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epi2me-labs/wf-human-variation

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Human variation workflow

Introduction

Compute requirements

Install and run

Related protocols

Input example

Input parameters

Workflow Options

Main options

Copy number variant calling options

Modified base calling options

Short tandem repeat expansion genotyping options

Advanced Options

Multiprocessing Options

Outputs

Pipeline overview

1. Input and data preparation

2. Data QC and pre-processing

3. Small variant calling with Clair3

4. Structural variant (SV) calling with Sniffles2

5. Modified base calling with modkit

6a. Copy number variants (CNV) calling with Spectre

6b. Copy number variants (CNV) calling with QDNASeq

7. Short tandem repeat (STR) genotyping with Straglr

8. Phasing variants

9. Variant annotation

10. Genome compatibility and running the workflow on non-human genomes

Troubleshooting

FAQ's

Related blog posts

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