The following section describes the input files required by DRAGEN Array.

IDAT Files

For each sample a pair of raw intensity files (.idat) are generated from the iScan System or NextSeq550 (for non-methylation arrays). They provide intensities in the red and green channels for each probe on the Infinium array.

An IDAT file is identified by the BeadChip Barcode (12-digit unique Sentrix ID, i.e. 123456789101), BeadChip Position (row and column of the sample, i.e. R01C01), and Grn (Green) or Red for the specific channel.

Manifest Files

The CSV manifest file (.csv) provides complementary data to the BPM manifest file in a human readable format. It is a required input to the genotype gtc-to-vcf command to enable VCF generation for insertion/deletion variants.

Cluster File

CN Model File

PGx Database File

The PGx database file (.zip) contains the variant mapping information from Infinium PGx arrays to PGx variants. For each gene and each variant used in the star allele definitions of the gene, there is a mapping to the ID field in the SNV VCF file. Each line in the gene mapping file represents a single variant and contains the SNV VCF ID for that variant followed by the HGVS (Human Genome Variation Society) tag for the variant. The PGx database file is array specific and is one of the product files provided by Illumina for each PGx array product.

Genome FASTA Files

The genome FASTA file (.fa) is a text file with the reference genome sequences.The FASTA index file (.fai) contains meta-data about chromosomal orchestration within the FASTA file for a particular species. DRAGEN Array PGx calling supports human genome build 37 and 38. The genome FASTA file and FASTA index file are both provided by Illumina for human species and should be stored together in the same input folder.

IDAT Sample Sheet

For local analysis, the IDAT sample sheet can be a CSV or JSON formatted file with direct paths to sample IDAT files. It enables easy analysis of samples from different directories.

Example CSV format:

Green IDAT Path,Red IDAT Path

/path/to/sample1_Grn.idat,/path/to/sample1_Red.idat

/path/to/sample2_Grn.idat,/path/to/sample2_Red.idat

/path/to/sample3_Grn.idat,/path/to/sample3_Red.idat

Example JSON format:

[

{

"Green IDAT Path": "/path/to/sample1_Grn.idat",

"Red IDAT Path": "/path/to/sample1_Red.idat"

},

{

"Green IDAT Path: "/path/to/sample2_Grn.idat",

"Red IDAT Path": "/path/to/sample2_Red.idat"

},

{

"Green IDAT Path": "/path/to/sample3_Grn.idat",

"Red IDAT Path": "/path/to/sample3_Red.idat"

},

]

For cloud analysis, the IDAT sample sheet can be a CSV formatted file.

beadChipName,sampleSectionName

Beadchip 1 barcode (204753010023), sample section (R01C01)

Beadchip 1 barcode (204753010023), sample section (R02C01)

Beadchip 2 barcode (204753010024), sample section (R01C01)

Beadchip 2 barcode (204753010024), sample section (R02C01)

For DRAGEN Array Methylation QC on cloud, additional optional sample sheet fields are available.

Following Sample_Group, any number of additional columns can be added to include meta data fields such as sex, sample type, plate and well information, etc. Additional columns added after the Sample_Group column may have user-defined column header values. The Sample_ID field and any additional meta data added will be replicated in the Sample QC Summary output files.

The Sample_Group field will be used to populate the PCA Control Plot within the Sample QC Summary Plots file and the Principal Component Summary file. For the PCA Control Plot, each sample group will be assigned a unique color. Samples assigned to the same Sample_Group value will be the same color in the PCA Control Plot.

beadChipName,sampleSectionName,Sample_ID,Sample_Group,MetaData1

Beadchip 1 barcode (204753010023), sample section (R01C01),NA1231,Group1,F

Beadchip 1 barcode (204753010023), sample section (R02C01),NA1232,Group2,F

Beadchip 2 barcode (204753010024), sample section (R01C01),NA1233,Group2,M

Beadchip 2 barcode (204753010024), sample section (R02C01),NA1234,Group1,M

GTC Sample Sheet

The GTC sample sheet is a CSV or JSON formatted file with direct paths to sample GTC files. It enables easy analysis of samples from different directories.

Example CSV format:

GTC Path

/path/to/sample1.gtc

/path/to/sample2.gtc

/path/to/sample3.gtc

Example JSON format:

[

{

"GTC Path": "/path/to/sample1.gtc"

},

{

"GTC Path": "/path/to/sample2.gtc"

},

{

"GTC Path": "/path/to/sample3.gtc"

}

]

Input File Summary Table

In addition to the input files, there are set of intermediate files, including GTC, SNV VCF, CNV VCF and PGx CSV, which are outputs of some DRAGEN Array Local commands and inputs to other commands.

The table below summarizes the input files or intermediate file, their sources, and the associated DRAGEN Array Local commands and options.

Technical Support

Additional Resources

1. Is DRAGEN Array analysis a local (on-premises) or cloud solution? DRAGEN Array analysis is available locally (on-premises) and cloud.\

   DRAGEN Array Local Analysis utilizes a command-line interface for power users to have granular control and flexibility to support large scale microarray genomic studies. Deployed on Windows or Linux operating systems, the local package is CPU-based and does not require a specialized server or hardware.\

   DRAGEN Array Cloud Analysis utilizes the user-friendly, graphical interface of BaseSpace Sequence Hub to simplify analysis setup and kickoff.\

2. Which Infinium arrays is DRAGEN Array compatible with? Genotyping: DRAGEN Array can produce GTC files for any Infinium genotyping array. For customers interested in obtaining a genotyping VCF file, any human genotyping array is supported. Conversion to VCF requires a FASTA file input. Illumina provides FASTA files for human genome build 37 and 38 on the support site.\

   Pharmacogenomics: Global Diversity Array with Enhanced PGx is supported for PGx CNV calling and star allele calling. Manifest version E and later should be used.\

3. How many samples are needed per analysis? Genotyping: As few as one sample can be used for genotyping. Multiple analysis batches can be kicked off and run in parallel.\

   Pharmacogenomics: A minimum of 24 samples is required for PGx CNV calling with 22 passing QC. Passing QC is defined as Log R Dev < 0.2. 96 samples are recommended for the most accurate CNV results. Multiple analysis batches can be kicked off and run in parallel.\

RELEASE DATE

December 2023

RELEASE HIGHLIGHTS

• Improved star allele calling accuracy for Global Diversity Array with enhanced PGx (GDA-ePGx) BeadChips.

• Reports star allele calls with quality scores for greater transparency and confidence.

• Provides missing variant reporting to improve data quality.

NEW FEATURES IN DETAIL

• Star Allele Calling

  • • For in-silico datasets, call rate ≥99%, diplotyping accuracy ≥ 90%

    • Includes reporting of the hybrid star alleles and allelic specific copy number

  • Provides quality score that estimates confidence in the star allele call as an additional quality metric

  • Star allele call rate increased through more robust error tolerance and missing data tolerance

    • Supporting variants and missing variants are listed and can be further reviewed

    • Quality score indicates confidence in result considering the missing data

  • Reports alternative ranked PGx star allele solutions

    • Allows an alternative to be investigated which may be desirable for samples with low confidence calls

    • Provides quality score (negative log likelihood) for alternative solutions

• • Metabolizer and function annotations are supported for two sets of guidelines from CPIC and DPWG respectively

  • Activity scores are provided for CYP2C9, CYP2D6, and DPYD

• CNV VCF

  • CNV coverage for genes listed in PGx CNVs Coverage

  • Compressed and indexed files for size reduction and faster reading

  • Updated VCF header description to indicate copy number of 5 may be reported by the software

  • Revised filter field delimiter to comply with VCF 4.3 specification which allows VCF parsing software to parse the file successfully

• Genotyping VCF

  • Compressed and indexed files for size reduction and faster reading

KNOWN ISSUES

• Corrupt or invalid GTC files will abort with an error instead of skipping. The corrupt or invalid GTC files will need to be removed before proceeding.

• In the gtc-to-vcf subcommand a mismatch between BPM and CSV manifests will not cause the command to abort with an error. The mismatch will need to be addressed before proceeding.

• For gtc-to-vcf, multi-allelic variants designed with multiple assays might not always collapse into one variant correctly and be reported as two separate variants instead. Some indel variants are missing from SNV VCF due to mapping issue between the designed indels and the reference genome.

• Manifest names greater than 80 characters will cause failure when converting IDATs to GTCs.

• Symbolic links for VCFs are not supported as the inputs to the “star-allele call” subcommand.

• The local Linux CLI and Cloud offering do not sort the star_alleles.csv and various fields in the metabolizer_status.json. The local Windows CLI does.

KNOWN LIMITATIONS

• PGx CNV calling and star allele calling and annotation were only validated and intended to be used with GDA_PGx_E2 product files.

• Using subcommands “unsquash-duplicates” and “filter loci” during gtc-to-vcf conversion should not be used when star allele calling is desired.

• Only CPIC guidelines are available for star allele annotation (metabolizer status calling) for the cloud offering. For local, CPIC and DPWG are available.

DRAGEN Array Local Overview

DRAGEN Array provides accurate, comprehensive, and efficient analysis of Infinium microarray data. The local command-line interface makes it easy for power users to have granular control and flexibility to support large scale microarray genomic studies.

Getting Started

Computing Requirements

Before downloading and installing the software, ensure the following specifications are met for best performance:

Quota Specifications

Internet is required to do a software license check and ensure paid quota is available for all samples in the analysis batch. For the software license check, the following endpoint is used: license.edicogenome.com.

Installation

Please follow the steps below to install the software on your compute infrastructure:

2. Unzip and extract the package. The executable can be found in the dragena subfolder of the software download after extraction.

3. To check that the DRAGEN Array installation was successful, follow these steps:

   • Open a command prompt (Windows) or terminal (Linux).

   • [Optional] Add /path/to/dragena/, e.g. /usr/local/bin/dragena-linux-x64-DAv1.0.0/dragena/, to your PATH – to access the executable anywhere in the folder structure

   • Execute the following command: /path/to/dragena/dragena version, or if the environmental variable PATH is set: dragena version

The version of the software will be displayed in the terminal window when the installation was successful.

Run DRAGEN Array Local

For genotyping analysis, there is no sample minimum required to run analysis.

To optimize performance of the targeted PGx CNV caller and minimize batch effect, it is recommended to:

• Analyze samples that were processed together in one batch

• Avoid combining sample batches processed on different reagent lots.

• Analyze batches of 96 samples or more

• Use the CN Model and PGx Database File provided as part of the standard product files

Quick Start

Command examples show analysis for a Linux system using folders instead of sample sheets. For Windows users, make sure to substitute the file paths in the commands following windows conventions, e.g., using backslash (\) instead of forward-slash (/). A sample sheet can be used to select specific samples out of a folder.

1. Open a command prompt (Windows) or terminal window (Linux) and navigate to the directory where the software was installed. Or a different, desired directory if the executable was added to the PATH environmental variable.

2. Use the genotype call command to call genotypes and generate GTC files using IDAT files as input. dragena genotype call --bpm-manifest /user/productfiles/manifest.bpm --cluster-file /user/productfiles/clusterfile.egt –-idat-folder /user/IDATs –-output-folder /user/gtc

3. Use the genotype gtc-to-vcf command to create SNV VCF files from the GTC files generated by the genotype call command. dragena genotype gtc-to-vcf --bpm-manifest /user/productfiles/manifest.bpm --csv-manifest /user/productfiles/manifest.csv --genome-fasta-file /user/productfiles/genome.fa --gtc-folder /user/gtc --output-folder /user/vcf

4. Use the copy-number call command to call PGx CNVs from the GTC files and produce CNV VCF files. It is recommended to use the same output folder used for SNV VCF since the star-allele call command accepts one VCF folder with SNV and CNV VCFs. dragena copy-number call --cn-model /user/productfiles/cnv_model.dat --gtc-folder /user/gtc --output-folder /user/vcf

5. Use the star-allele call command to generate star allele calls using the CNV and SNV VCF files generated by the gtc-to-vcf and copy-number call commands. dragena star-allele call --vcf-folder /user/vcf --database /user/productfiles/GDA_ePGx_E2_DAv1.0.0.zip --output-folder /user/star-alleles --license-server-url https://username:password@license.edicogenome.com

6. Use the star-allele annotate command to summarize the star alleles and add metabolizer statuses to the star alleles generated by the star-allele call command. Guidelines (CPIC or DPWG) can be specified. dragena star-allele annotate –-star-alleles star_alleles.csv --guidelines CPIC --output-folder /user/metabolizer-statuses

7. [Optional] Use the copy-number train command to retrain the copy number model. dragena copy-number train --bpm-manifest /user/productfiles/manifest.bpm -–genome-fasta-file /user/productfiles/genome.fa –-gtc-folder /user/gtc --platform LCG –-output-folder /user/productfiles/cnmodelnew

Command Index

Use the following syntax when using the command-line interface:

dragena [command] [required parameters] [optional parameters]

copy-number

The root command for actions that act on copy number variants.

copy-number call

The command used to call copy number variants. A batch of 24 samples or more are required for analysis. For a successful analysis, 22 samples must pass QC defined as having log R dev < 0.2.

copy-number help

Displays help information for a copy-number command.

copy-number train

Trains copy number (CN) model for a set of samples. Generate a new CN model if using a customized cluster file (.egt) optimized for the specific data set.

• Execute the train command using the data sets that were used to optimize the cluster file.

• To use a CN model generated by the train command, the mask file for the manifest must be saved in the same directory as the manifest.

• A minimum of 96 samples is required to use the copy-number train command. For optimal performance, at least 150 is recommended.

• For best performance, validate the CN model using truth data before using in CN calling.

copy-number version

Displays version information for copy-number command.

genotype

The root command for genotype calling.

genotype call

Determines genotype calls (GTC) from IDAT files.

genotype gtc-to-bedgraph

Converts GTC to BedGraph files, producing BedGraph formatted visualization files from the log R ratio data contained in the GTC intermediate files.

genotype gtc-to-vcf

genotype help

Displays the help information for a genotype command.

genotype version

Displays current DRAGEN Array Local version.

help

Displays the help information.

version

Displays current DRAGEN Array Local version.

star-allele

The root command PGx star allele calling.

star-allele help

Displays help information for a star-allele command.

star-allele version

Displays version information for star-allele.

star-allele call

Calls PGx star allele diplotypes. The SNV VCF files should be generated using the DRAGEN Array gtc-to-vcf command with unsquash-duplicates off (default) and without filter loci.

star-allele annotate

Annotates and summarizes the star-alleles, specifically for metabolizer statuses and outputs in a consolidated JSON report. Metatolizer status is determined through direct lookup into public PGx guidelines CPIC or DPWG as specified by the user.

Troubleshooting and Additional Support

Tips for using the Command-line interface

DRAGEN Array Local utilizes a command-line interface which allows full user control of software functionality and easy automation of tasks. The software is designed to be used by power users and bioinformaticians.

When using command-line consider the following tips:

• Spaces cannot be part of a file name in a command. If the file name has spaces, use quotes around the file name

• To correct a typing error in a previously entered command, use the up arrow to repeat the previous command, then correct the error before re-entering it.

• Double check the command. Misspelling, extra, or missing dashes, etc. will cause the command to be unrecognizable by the software.

  • When entering paths or long names, copy and paste the values to help avoid errors.

  • If using Windows, use a File Explorer window to navigate to the product file or folder that is needed by the DRAGEN Array Local command. While holding down the shift button on the keyboard, right click the file and select the 'Copy as Path' option. Then paste the copied path into the command prompt to use the file or folder.

• To cancel a command while it is running, press Control + C on the keyboard.

Optimizing cluster files and copy number models

When updating the cluster file for pharmacogenomic applications, understand the specifications for the copy number model file before beginning.

To retrain the CN model file, 96 samples must be used at minimum with 90 of those samples passing QC defined as Log R Dev less than or equal to 0.2. It is recommended to train with at least 150 samples. A greater number of samples can be advantageous, but diminishing returns and longer computation times are seen after 3,000 samples.

It is recommended to manually QC the training samples and remove samples that have Log R Dev > 0.2, call rate < 0.99, or TGA Control probe < 1.0 so only the highest quality sample are used in the training. The same samples used to create the new cluster file should be used to retrain the CN Model. To minimize batch effect in the training sample set, the samples should be analyzed in as few batches as possible and come from the same reagent lots.

The copy-number train algorithm is designed with the assumption that the copy number distribution resembles the standard population distributions. This ensures the updated CN model file is representative of the normal populations in which it will be used to calculate copy number for key pharmacogenomic targets.

Pharmacogenomic analysis for semi-custom arrays

When designing a semi-custom array using a commercial Infinium PGx array backbone, such as the Global Diversity Array with enhanced PGx, it is important to retain all backbone content in the design as removing content could decrease the quality of result.

RELEASE DATE

May 2024

RELEASE HIGHLIGHTS

• Adjustable thresholds to determine pass/fail status

• Data summary plots for a quick visual check of each analysis batch

• Determining detection p-value, beta-values, and m-values from each methylation sample

• Deployment on BaseSpace™ Sequence Hub user interface for easy analysis kickoff

NEW FEATURES IN DETAIL

• Adjustable thresholds for 21 built in controls, p-value detection, proportion probes passing, and offset correction within BaseSpace Sequence Hub to customize for user’s study needs

  • Thresholds are used to assign pass (1) or fail (0) status to each sample

    • Failed metrics can be highlighted for easy viewing

  • Pinpoint areas of failure including bisulfite conversion, staining, hybridization, etc. to identify assay steps in need of troubleshooting

  • Quantitative values for each control removing ambiguity with manual interpretation

• Data summary plots with information on passing p-value detection and principal component analysis of beta values

• Provides detection p-value, beta-values and m-values for each CG site per sample to use in downstream analysis

KNOWN ISSUES

KNOWN LIMITATIONS

• Standard thresholds may not be applicable for all discontinued, semi-custom or custom BeadChips and IDATs originating from NextSeq550

• Built-in controls may not be available on all discontinued, semi-custom or custom BeadChips

The following Types of Analysis are currently supported by DRAGEN Array:

• DRAGEN Array – Genotyping

• DRAGEN Array – PGx – CNV calling

• DRAGEN Array – PGx – Star allele annotation

• DRAGEN Array - Methylation QC

DRAGEN Array – Genotyping

DRAGEN Array - PGx – CNV calling

DRAGEN Array – PGx – Star Allele Annotation

DRAGEN Array – Methylation QC

DRAGEN Array Cloud Analysis Overview

DRAGEN Array Cloud utilizes the user-friendly graphical interface of BaseSpace Sequence Hub to simplify DRAGEN Array analysis setup and kickoff. Optional integration with the iScan System allows data to be streamed directly from the instrument to the cloud platform. Analysis data is stored on the Illumina Connected Platform providing secure storage for both microarray and sequencing data.

Getting Started

The following prerequisites are needed to get started with DRAGEN Array Cloud:

• • Designating a workgroup as ‘Collaborative’ allows projects to be shared with collaborators or Illumina Tech Support to assist with troubleshooting. To create a collaborative workgroup, select the Enable collaborators outside of this domain checkbox during workgroup creation.

• [Optional] iScan integration: The iScan System is integrated with Illumina Connected Platform and can send IDATs for further analysis. The iScan System must be running iScan Control Software version 4.2.1 or later.
• EULA acceptance: Accept all necessary End User License Agreements in BaseSpace Sequence Hub before scanning begins.

• Internet connection: For uploading product files or IDATs, a network connection 1 GbE or faster is recommended.

Note: Accessioning BeadChips before scanning and starting analysis is no longer a required step and has been automated within the system.

Running Analysis

Before beginning analysis, ensure workgroup context is being used so analysis can be viewed by all members of your workgroup. The name of your workgroup should appear in the top right corner.

Use the following steps to run the Microarray Analysis Setup on BaseSpace Sequence Hub:

1. Select the Runs tab

2. Select New Run

3. Select Microarray Analysis Setup

4. Enter the Analysis Name (Figure 1)
5. Use the Select Project link to choose the project for your output files To select an existing project, click the radio button next to the desired project name. You can also create a project by clicking the New button in the project selection window.

7. (Optional) Create a custom configuration via the "Add Custom Configuration" option in Configuration Settings. Custom configurations must be assigned a name and product files can be uploaded or selected (Figure 2). Custom configuration options vary by Type of Analysis including:

• DRAGEN Array - Genotyping provides flexibility for turning off/on specific output files and adjusting GenCall score cutoff. Its recommended to turn off VCF output for non-human species and Final Report output for large sample numbers.

2. Select Next

3. Select either Import Sample Sheet, Select BeadChips, or Import IDAT Files (Figure 3)

• Import Sample Sheet presents a link to upload sample sheet. Users may download a template sample sheet by selecting the Download Template link.

• Select BeadChips allows users to select BeadChips from the displayed list of available BeadChips. If selecting specific samples within the BeadChip is desired the Import Sample Sheet option should be used.

• Import IDAT Files allows users to upload the IDAT files from a local folder to the cloud platform for use with the current and future analyses by users within the same workgroup.

1. Select Launch Analysis

View Outputs

1. On the Analyses tab, view the analysis status, e.g., initializing or complete.

2. After the analysis is complete, select the analysis and select the Files tab.

3. From the Files tab, select the Output folder.

DRAGEN Array Methylation QC

Threshold Adjustment

When using DRAGEN Array – Methylation – QC cloud analysis type, additional customization options will appear after product files are selected within Configuration Settings. Adjustments to these thresholds will be saved as part of the Configuration Setting. Thresholds can be adjusted based on study objectives. Adjusting thresholds will impact the pass or fail status of samples in the output files.

Illumina recommends thresholds for MethylationEPIC v1 & v2 and Methylation Screening Array (MSA). Users may use these thresholds as a starting point when defining thresholds for their custom or semi-custom BeadChip or other Infinium Methylation arrays. Further tuning may be required based on BeadChip used, laboratory conditions, iScan settings, bisulfite conversion methods, FPPE sample type, etc. A dataset deemed acceptable to the user based on proportion probes passing can be used for these additional threshold adjustments.

The recommended thresholds are pre-set within the software for MethylationEPIC and Methylation Screening Array with the following values:

DRAGEN Array Methylation QC and GenomeStudio Methylation Module Differences

DRAGEN Array Methylation QC software provides automated methylation sample QC using assay control probes on the Infinium Methylation Arrays. Unlike the manual visual QC in GenomeStudio, DRAGEN Array ultilizes 21 numerical metrics defined based on the control probes and uses standard thresholds to determine pass/fail status of a sample. Unlike GenomeStuio, probe detection rate (proportion of probes passing at a given p-value threshold) is not utilized to determine sample pass/fail status in DRAGEN Array.

DRAGEN Array Methylation QC performs background normalization, dye bias correction, and detection p-value calculation differently in comparison to the GenomeStudio Methylation module, leading to differences in probe detection p-values and detection rates. For the GenomeStudio Methylation Module, non-cancer samples at standard DNA input typically have detection rate > 96%. The detection rates from DRAGEN Array Methylation QC are typically lower compared to GenomeStudio, because the detection p-value from DRAGEN Array is more stringent than that from the GenomeStudio Methylation Module. The table below shows example detection rates from the DRAGEN Array Methylation QC software from MSA (Methylation Screening Array) datasets.

Note that only samples passing QC are included and all samples are at or above 50ng DNA input. Detection p-value threshold 0.05.

Known Issues

DRAGEN Array Methylation QC cloud v1.0.0 is released on BSSH version 7.21. There are two known issues with the BSSH UI that impact Methylation QC analyses.

• Sample sheets with greater than 144 samples lead to undefined failures. The issue impact methylation QC as well as other analysis types. To process larger sample numbers (up to 1152 for DRAGEN Array methylation QC), samples can be separated into batches of 144 when using sample sheet, or by selecting BeadChips directly from the BeadChip table.

Troubleshooting and Additional Support

Troubleshooting iScan integration

The firewall protects the iScan control computer by filtering incoming traffic to remove potential threats. The firewall is enabled by default to block all inbound connections. Keep the firewall enabled and allow outbound connections.

The following table shows the applicable endpoints for the iScan.

Sharing a project

1. Navigate to the Projects tab

2. Click the button next to the desired project

3. Select the Share button above to list (Figure 3)

4. Select the Get Link Option to Activate a link for the project

5. Copy the link and send it to the desired recipient(s)

Note: The project owner maintains ownership and write access. If project owner deletes the data, the collaborators lose access to it.

DRAGEN Array star allele calling leverages the star allele definitions provided by PharmVar and PharmGKB. DRAGEN Array star allele phenotype annotation, using the “star-alle annotate” command, is achieved through direct lookup into public PGx guidelines CPIC or DPWG, which is selected by the user when running DRAGEN Array.

See table below for details of the data sources.

DRAGEN Array “star-alle annotate” command provides both metabolizer status and activity score annotations for genes covered by the CPIC and DPWG guidelines.

Specifically, CPIC metabolizer/phenotype annotations are supported for CACNA1S, CYP2B6, CYP2C19, CYP2C9, CYP2D6, CYP3A5, DPYD, G6PD, MT-RNR1, NUDT15, RYR1, SLCO1B1, TPMT, UGT1A1, CFTR, IFNL3/IFNL4 and VKORC1, among them activity scores are supported for CYP2C9, CYP2D6, and DPYD. DPWG metabolizer/phenotype annotations are supported for CYP1A2, CYP2B6, CYP2C19, CYP2C9, CYP2D6, CYP3A4, CYP3A5, DPYD, NUDT15, SLCO1B1, TPMT, UGT1A1, VKORC1 and F5, among them activity scores are supported for CYP2D6 and DPYD.

The genes and star alleles listed below can be detected by DRAGEN Array v1.0 if available on the microarray. Known and novel star alleles not in the below list will not be reported. Star allele definitions are sourced from PharmVar and PharmGKB.

DRAGEN (Dynamic Read Analysis for GENomics) Array secondary analysis is a powerful bioinformatics software for Illumina Infinium array-based assays. DRAGEN Array uses cutting-edge data analysis tools to provide accurate, comprehensive, and highly efficient secondary analysis to maximize genomic insights and meet your research needs across multiple applications.

DRAGEN Array is offered as a local package with command-line interface (no specialized server or hardware required) and as a cloud-based package with an intuitive graphical user interface, as summerized in the table below.

The version history for DRAGEN Array product documentation:

Copy number variation can be detected for genes and regions listed below. The chromosome locations are GRCh38 based.

The following section describes the outputs produced by DRAGEN Array.

CNV VCF File

DRAGEN Array produces one CNV variant call file (VCF) (*.cnv.vcf) per sample to report the CN status on the gene and sub gene level, along with the CN events for PGx targets.

The CNV VCF output file follows the standard VCF format. The QUAL field in the VCF file measures the CNV call quality. The CNV call quality is a Phred-scaled score capped at 60 and the minimal value is 0. Low quality calls (QUAL<7) are flagged by the Q7 filter. Low quality samples with LogRDev greater than a threshold 0.2 are flagged with the SampleQuality flag.

The CNV VCF output file includes the following content.

##fileformat=VCFv4.1

##source=dragena 1.0.0

##genomeBuild=38

##reference=file:///hg38_with_alt/hg38_nochr_MT.fa

##FORMAT=<ID=CN,Number=1,Type=Integer,Description="Copy number genotype for imprecise events. CN=5 indicates 5 or 5+">

##FORMAT=<ID=NR,Number=1,Type=Float,Description="Aggregated normalized intensity">

##ALT=<ID=CNV,Description="Copy number variant region">

##FILTER=<ID=Q7,Description="Quality below 7">

##FILTER=<ID=SampleQuality,Description="Sample was flagged as potentially low-quality due to high noise levels.">

##INFO=<ID=CNVLEN,Number=1,Type=Integer,Description="Number of bases in CNV hotspot">

##INFO=<ID=PROBE,Number=1,Type=Integer,Description="Number of probes assayed for CNV hotspot">

##INFO=<ID=END,Number=1,Type=Integer,Description="End position of CNV hotspot">

##INFO=<ID=SVTYPE,Number=1,Type=String,Description="Structural Variant Type">

##CNVOverallPloidy=1.8

##CNVGCCorrect=True

##contig=<ID=1,length=248956422>

##contig=<ID=4,length=190214555>

##contig=<ID=10,length=133797422>

##contig=<ID=16,length=90338345>

##contig=<ID=19,length=58617616>

##contig=<ID=22,length=50818468>

##contig=<ID=22_KI270879v1_alt,length=304135>

#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT 204619760001_R01C01

1 109687842 CNV:GSTM1:chr1:109687842:109693526 N <CNV> 60 PASS CNVLEN=5685;PROBE=124;END=109693526;SVTYPE=CNV CN:NR 2:0.966631132771593

4 68537222 CNV:UGT2B17:chr4:68537222:68568499 N <CNV> 60 PASS CNVLEN=31278;PROBE=383;END=68568499;SVTYPE=CNV CN:NR 0:0.376696837881692

10 133527374 CNV:CYP2E1:chr10:133527374:133539096 N <CNV> 60 PASS CNVLEN=11723;PROBE=194;END=133539096;SVTYPE=CNV CN:NR 2:0.980059731860893

16 28615068 CNV:SULT1A1:chr16:28615068:28623382 N <CNV> 57 PASS CNVLEN=8315;PROBE=164;END=28623382;SVTYPE=CNV CN:NR 2:0.980552325552963

19 40844791 CNV:CYP2A6.intron.7:chr19:40844791:40845293 N <CNV> 60 PASS CNVLEN=503;PROBE=38;END=40845293;SVTYPE=CNV CN:NR 2:0.9663775484762

19 40850267 CNV:CYP2A6.exon.1:chr19:40850267:40850414 N <CNV> 60 PASS CNVLEN=148;PROBE=21;END=40850414;SVTYPE=CNV CN:NR 2:0.9663775484762

22 42126498 CNV:CYP2D6.exon.9:chr22:42126498:42126752 N <CNV> 48 PASS CNVLEN=255;PROBE=370;END=42126752;SVTYPE=CNV CN:NR 2:0.981703411438716

22 42129188 CNV:CYP2D6.intron.2:chr22:42129188:42129734 N <CNV> 10 PASS CNVLEN=547;PROBE=333;END=42129734;SVTYPE=CNV CN:NR 2:0.965498002434641

22 42130886 CNV:CYP2D6.p5:chr22:42130886:42131379 N <CNV> 60 PASS CNVLEN=494;PROBE=172;END=42131379;SVTYPE=CNV CN:NR 2:0.970341562236357

22_KI270879v1_alt 270316 CNV:GSTT1:chr22_KI270879v1_alt:270316:278477 N <CNV> 60 PASS CNVLEN=8162;PROBE=91;END=278477;SVTYPE=CNV CN:NR 2:1.01191145130511

SNV VCF File

The software produces one genotyping variant call file (*.snv.vcf) file per sample, covering single nucleotide variants (SNV) and indels for the sample. It reports GenCell score (GS), B Allele Frequency (BAF), and Log R Ratio (LRR) per variant.

The BAF and LRR are oriented with Ref as A and Alt as B relative to the reference genome, while GS is agnostic to the reference genome. Users familiar with GenomeStudio may observe BAF and LRR reported in the VCF as 1 minus the value reported in GenomeStudio depending on the Ref Alt allele orientation with the reference genome. GenomeStudio reports these values based on the information in the manifest without knowledge of the reference genome.

The SNV VCF output file includes the following content. The last row shows an example of variant call.

##fileformat=VCFv4.1

##source=dragena 1.0.0

##genomeBuild=38

##reference=file:///genomes/38/genome.fa

##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype">

##FORMAT=<ID=GS,Number=1,Type=Float,Description="GenCall score. For merged multi-assay or multi-allelic records, min GenCall score is reported.">

##FORMAT=<ID=BAF,Number=1,Type=Float,Description="B Allele Frequency">

##FORMAT=<ID=LRR,Number=1,Type=Float,Description="LogR ratio">

##contig=<ID=1,length=248956422>

##contig=<ID=2,length=242193529>

##contig=<ID=3,length=198295559>

##contig=<ID=4,length=190214555>

##contig=<ID=5,length=181538259>

##contig=<ID=6,length=170805979>

##contig=<ID=7,length=159345973>

##contig=<ID=8,length=145138636>

##contig=<ID=9,length=138394717>

##contig=<ID=10,length=133797422>

##contig=<ID=11,length=135086622>

##contig=<ID=12,length=133275309>

##contig=<ID=13,length=114364328>

##contig=<ID=14,length=107043718>

##contig=<ID=15,length=101991189>

##contig=<ID=16,length=90338345>

##contig=<ID=17,length=83257441>

##contig=<ID=18,length=80373285>

##contig=<ID=19,length=58617616>

##contig=<ID=20,length=64444167>

##contig=<ID=21,length=46709983>

##contig=<ID=22,length=50818468>

##contig=<ID=MT,length=16569>

##contig=<ID=X,length=156040895>

##contig=<ID=Y,length=57227415>

#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT 202937470021_R06C01

1 2290399 rs878093 G A . PASS . GT:GS:BAF:LRR 0/1:0.7923:0.50724137:0.14730307

Genotype Call (GTC) File

BedGraph File

The BedGraph file contains the log R ratios from the genotyping algorithm for use in visual tools.

Star Allele CSV File

The Star Allele CSV file is an intermediate file generated by the star-allele call command and serves as the input to the star-allele annotate command. It contains all the star allele calls for all samples in a run. Each row in the file provides either a star allele diplotype or simple variant call for a PGx-related gene. Star allele diplotype calls for a sample and a gene may span multiple lines where alternative solutions can be listed.

The Star Allele CSV file also contains meta information marked by # at the top of the file for the genome build and PGx database used for the star allele calling.

The star_allele.csv file contains the following details per sample:

Below is an example of the first 4 columns from a star allele CSV file:

Sample,Rank,Gene or Variant,Type,Solution

204650490282_R02C01,1,CYP2C9,Haplotype,*9/*11

204650490282_R02C01,1,CYP2C19,Haplotype,*2/*10

Genotype Summary Files

The software produces genotype summary files (gt_sample_summary.csv and gt_sample_summary.json) that contains the following details per sample:

• Sample ID

• Sample Name

• Sample Folder

• Autosomal Call Rate

• Call Rate

• Log R Ratio Std Dev

• Sex Estimate

• TGA_Ctrl_5716 Norm R

The TGA_Ctrl_5716 Norm R field is specific to PGx products (e.g., Global Diversity Array with enhanced PGx). The field value is the Normalized R value of one probe and is meant as an assay control where < 1 indicates the sample failed in the TGA (Targeted Gene Amplification) process. If the product does not have this probe, it is not included in the gt_sample_summary.

Final Report

DRAGEN Array Cloud produces a Final Report (gtc_final_report.csv) per analysis batch similar to the one available in GenomeStudio. It contains the following details per locus per sample:

Note: Analyses on products with large numbers of loci (>1 Million) and large numbers of samples (>100) yield a large (50+ Gigabyte) Final Report that are difficult to download and review. It’s recommended to create analysis configurations that do not produce this report if large batches are desired.

Locus Summary

DRAGEN Array Cloud produces a Locus Summary (locus_summary.csv) per analysis batch similar to the one available in GenomeStudio. It contains the following details per locus:

CN Summary File

The sample summary contains per sample key stats for each sample in a batch that contains the following details per sample:

• Sample ID

• Sample Name

• Sample Folder

Copy Number Batch File

The copy number batch summary file (cn_batch_summary.csv) shows the total copy number gain, loss, and neutral (CN=2) values for each target region across all the samples in the analysis.

Example copy number batch summary file content:

Target Region,Total CN gain,Total CN loss,Total CN neutral

CYP2A6.exon.1,0,1,47

CYP2A6.intron.7,0,1,47

CYP2D6.exon.9,2,4,42

CYP2D6.intron.2,7,2,39

CYP2D6.p5,13,2,33

CYP2E1,2,0,46

GSTM1,0,42,6

GSTT1,0,33,15

SULT1A1,0,0,48

UGT2B17,0,34,14

All Target Regions,24,119,337

Warning/Error Messages and Logs

The following scenarios result in a warning or error message:

• Manifest file used to generate GTC is not the same as the manifest file used to generate the CN model.

• FASTA files and FASTA index files do not match.

For the following scenarios, the software reports messages to the terminal output (as either a warning or an error):

• Indel processing for GTC to VCF conversion failed.

• The input folder does not contain the required input files.

• An input file is corrupt.

Examples of such notifications can include the following:

Star allele JSON File

Fields included in the star allele JSON header are described below.

Fields included in the star allele call (locusAnnotations) information are described below.

Example of JSON file content:

{

"softwareVersion": "dragena 1.0.0",

"genomeBuild": "hg38",

"databaseSources": "PharmVar Version: 6.0.5, PharmGKB Database Version: Snapshot-2023.08.30, CPIC Database Version: 1.30.0",

"mappingFile": "gda_mapping_53e0931.zip",

"pgxGuideline": "CPIC",

"sampleId": "204619760027_R01C01",

"locusAnnotations": [

{

"gene": "CYP2C9",

"callType": "Star Allele",

"genotype": "*1/*1",

"activityScore": "2",

"phenotype": "Normal Metabolizer",

"qualityScore": "0.9999",

"rawScore": "0.9999",

"supportingVariants": "Complete: *1 ( )",

"candidateSolutions": [

{

"rank": 1,

"genotype": "*1/*1",

"activityScore": "2",

"phenotype": "Normal Metabolizer",

"qualityScore": 0.9999,

"rawScore": 0.9999,

"alleles": [

{

"solutionLong": "Complete: *1",

"supportingVariants": "Complete: *1 ( )",

"missingVariants": "Complete: *1 ( )",

"collapsedAlleles": "Complete: *1 ( )"

}

],

"copyNumberRegions": "p5,exon.1,intron.1,exon.2,intron.2,exon.3,intron.3,exon.4,intron.4,exon.5,intron.5,exon.6,intron.6,exon.7,intron.7,exon.8,intron.8,exon.9,p3",

"copyNumberSolution": "2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2"

}

],

TBI Index File

Methylation Control Probe Output File

The software produces a control probe output file ({BeadChipBarcode}_{Position}_ctrl.tsv.gz) per sample that includes the raw methylated and unmethylated values for each control probe.

Each control probe has an address, type, color channel, name, and probe ID. It also provides the raw signal for methylated green (MG), methylated red (MR), unmethylated green (UG) and unmethylated red (UR).

The file can help identify which probes are available on a given BeadChip.

Methylation CG Output File

The software produces a CG output file ({BeadChipBarcode}_{Position}_cgs.tsv.gz) per sample that includes beta values, m-values and detection p-values for each CG site.

Beta values measure methylation levels in a linear fashion for easy interpretation. Unmethylated probes are close to zero and methylated probes are close to 1.

M-values are a log transformed beta value which provides a more representative measure of methylation.

Detection p-values measure the likelihood that the signal is background noise. It is recommended that p-value >0.05 are excluded from analysis as they are likely background noise.

Methylation Sample QC Summary Files

The software produces methylation sample QC summary in .xlsx and .tsv file formats (sample_qc_summary.xlsx and sample_qc_summary.tsv) per analysis batch, which provides per sample QC data for all samples in the batch.

The QC summary provides details on 21 controls metrics (see tables below), which are computed in same way as in the BeadArray Control Reporter software from Illumina. In addition, it provides average red and green raw and normalized signals, time of scanning, proportion of probes passing, overall sample pass/fail status, and the failure codes for control metrics that did not pass. The sample pass status is defined as the passing of all 21 control metrics. The QC summary .xlsx file further highlights failing parameters for easy viewing.

The QC summary files contain the following fields:

Methylation Sample QC Summary Plots

The software produces methylation sample QC summary plots (sample_qc_summary.pdf) per analysis batch which provides visual depictions of two QC summary plots for quick visual review.

The file contains the following control plots:

Methylation Principal Component Summary

The software produces a methylation principal component summary file (pcs.tsv.gz) per analysis batch which provides principal component data for each sample within the batch. This can be used to identify the specific samples associated with points on the PCA control plot within the Methylation Sample QC Control Plots output file.

The files contain the following fields:

Methylation Manifest Files

The software produces two methylation manifest files

1. Manifest in Sesame format (probes.csv)

2. Additional information for control probes (controls.csv)

The probes.csv file has the following columns:

The controls.csv file has the following columns:

Methylation Warning/Error Messages and Logs

The following scenarios result in a warning or error message:

• Missing IDATs or manifest

• Incorrect sample sheet formatting

• Duplicate BeadChip Barcode and Position within the sample sheet

• Missing control or assay probes

• Missing required columns in the manifest

• Unable to compute certain metrics

Examples of such notifications can include the following:

The following versions of DRAGEN Array have been released:

RELEASE DATE

March 2024

RELEASE HIGHLIGHTS

• Ability to genotype and produce related reports for human and non-human arrays in the cloud.

• Configureable interfaces in Basespace that allows for flexibility and easy kick off.

NEW FEATURES IN DETAIL

KNOWN ISSUES

• Some multi-nucleotide variant (MNV) designs reverse compliment the "Allele1/2 Top" fields in the Final Report

KNOWN LIMITATIONS

• Genotyping only works on diploid organisms at this time. Polyploid genotyping is not currently supported.