ikmb/deepvariant Docker 镜像 - 轩辕镜像 | Docker 镜像高效稳定拉取服务

IKMB DeepVariant Pipeline

This pipeline performs an end-to-end variant calling, starting from raw fastQ files to a multi-sample VCF. It has been pre-configured for the CCGA MedCluster.

Running the pipeline

This pipeline requires nextflow and singularity, so make sure these modules are loaded.

To run the pipeline, a typical command will looks as follows:

nextflow run ikmb/deepvariant --samples Samples.csv --genome GRCh38

These options are further explained in the following:

Options

--samples

This option expects a sample sheet in CSV format that specifies information on the subject, sample, details of the library and the location of the paired-end files.

A script is included that takes a folder full of fastQ files and creates a compliant sample sheet. This script requires ruby and can be downloaded here

ruby samplesheet_from_folder.rb --folder /path/to/reads > Samples.csv

Please note that the script makes some assumptions about the naming structure of the FastQ files (typically Illumina-generated data should work - but please check manually to see if the information makes sense!).

--joint_calling

Perform joint calling of all samples in this pipeline run (otherwise, only per-sampe VCFs are produced without ***ing variant sites in other samples.

--genome

The name of the genome assembly version to use. Allowed options are:

• GRCh37 (1000Genomes reference with decoys)
• GRCh38 (Current human genome WITHOUT alt contigs, as recommended by Heng Li)
• hg38 (Current human genome with all alt contigs, as distributed by the BROAD)

Please note that all assemblies come with pre-defined calling regions, as provided by the BROAD institute. These were manually curated to exclude regions that cannot be reliably called with short read data

Accuracy

Deepvariant has been shown to be highly accurate to the Genome-in-a-Bottle benchmark sets. Using in-house produced WGS data from GIAB sample NA***, the following concordance scores were achieved within the GIAB high confidence intervals:

type TP FP FN RECALL PRECISION

SNP 3040675 1048 2825 0.999 1.0

INDEL 479765 *** 20038 0.96 0.961