Golden Helix provides software solutions for clinical NGS workflows to meet scalability and data privacy requirements. Their software suite includes tools for variant calling, annotation, filtering, interpretation and reporting. They aim to help labs customize validated workflows to scale sample processing volume through batch processing strategies like parallelization across multiple machines. Golden Helix works with various customers globally, from regional testing labs to pharmaceutical companies, to develop scalable automated pipelines from panels to whole genomes.

1. Evaluating Cloud vs On-Premises
for NGS Clinical Workflows
NGS lab infrastructure choices to to meet requirements for cybersecurity,
patient data privacy, and scalable unit economics
Presented by Gabe Rudy, VP of Product & Engineering

2. NIH Grant Funding Acknowledgments
2
• Research reported in this publication was supported by the National Institute Of General Medical Sciences of
the National Institutes of Health under:
o Award Number R43GM128485-01
o Award Number R43GM128485-02
o Award Number 2R44 GM125432-01
o Award Number 2R44 GM125432-02
o Montana SMIR/STTR Matching Funds Program Grant Agreement Number 19-51-RCSBIR-005
• PI is Dr. Andreas Scherer, CEO of Golden Helix.
• The content is solely the responsibility of the authors and does not necessarily represent the official views of the
National Institutes of Health.

3. Who Are We?
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Golden Helix is a global bioinformatics company founded in 1998
Filtering and Annotation
ACMG & AMP Guidelines
Clinical Reports
CNV Analysis
Pipeline: Run Workflows
CNV Analysis
GWAS | Genomic Prediction
Large-N Population Studies
RNA-Seq
Large-N CNV-Analysis
Variant Warehouse
Centralized Annotations
Hosted Reports
Sharing and Integration

4. Cited in 1,000s of Peer-Reviewed Publications
4

5. Over 400 Customers Globally
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6. When you choose Golden Helix, you receive
more than just the software
6
Software is Vetted
• 20,000+ users at 400+ organizations
• Quality & feedback
Simple, Subscription-
Based Business Model
• Yearly fee
• Unlimited training & support
Deeply Engrained in Scientific
Community
• Give back to the community
• Contribute content and support
Innovative Software Solutions
• Cited in 1,000s of publications
• Recipient of numerous NIH grant and other
funding bodies

7. Genetic Testing Process
   
Sample Prep Sequencing Align & Call Annotate
& Filter
Variant
Interpretation
Report
Sentieon
&VS-CNV
V
arSeq VSReports
VSClinical
Golden Helix Clinical Suite
VSWarehouse
Aggregate Variants, Reports, Knowledgebase

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• Decision support for 33 criteria to classify a variant
from Benign to Pathogenic for Mendelian disorders
• Recommendation engine auto-scores 18 criteria
covering population catalogs, functional
predictions, and clinical annotations
• Interpretations saved into assessment catalogs and
re-used next time a variant is seen
• Guided workflows for interpretation of somatic
variants in cancer
• Includes coverage QC, somatic variant scoring,
biomarker evaluation of AMP evidence tier levels,
drug and trial evaluation, and clinical report
building
• Golden Helix CancerKB included with pre-written
report ready evaluations of most common
biomarkers in most common cancers
VSClinical (ACMG) VSClinical (AMP)

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• Widely adopted over the last five years
serving clinical labs in calling CNVs from
NGS data
• Validated as a replacement for MLPA as
more cost-effective, with no reduced
sensitivity, and with wider coverage of
evaluated genes
• Calls include metrics for QC, augmented by
annotation library that can be used for
further filtering and prioritization

11. Building an NGS Analysis Workflow
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• Laboratory Developed Tests must adapt and
customize workflows to fit the test uniquely
• Each part of the workflow requires tuning against
validation data:
o Levels of detection thresholds at each stage
o Filters that reduce hands-on interpretation but retain
sensitivity
o End-to-end validation of samples of positive and
negative controls
• A validated pipeline is ready for scaling to high sample
volumes
Seq
CNVs SVs
Variant
Annotate
Reads
Alignment
Annotate
Annotate
Filter Filter Filter
Interpret
Warehouse Report
Cohort
Analysis
Re-Analysis

12. Scaling an NGS Analysis Workflow
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• Automated workflow:
o Align & Call
o Annotate & Filter
• Interactive workflow:
o QC samples and variants
o Classify and interpret variants
o Review and report
• Constraints
o Compute resources
o Personnel
o Turn-around time
Seq
CNVs SVs
Variant
Annotate
Reads
Alignment
Annotate
Annotate
Filter Filter Filter
Interpret
Warehouse Report
Cohort
Analysis
Re-Analysis

13. Scaling Batch Processing
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• Parallelize by sample
• Strategies:
o Many machines running single process
o Many processes on large machines
o The strategy should match the storage and infrastructure
of the organization
o Existing infrastructure and policy for data security
o Cloud infrastructure requires security engineering
o On-premises Linux clusters if available are a simpler
compute model to orchestrate.

14. Scaling Scenarios
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• Region Testing Lab Scaling Exomes
• New contracts increase volume to hundreds of
exomes a week
• Batch work must complete over the weekend
• Windows-centric IT infrastructure
• Pharmaceutical Services Co Scaling Genomes
• High volume, batch-oriented WGS processing
• Fully automated locked-down services
deliverables
• Docker-centric pipeline construction and
automation

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• Prefers fewer large servers
• Target time limit of 24 hours to process 300 exomes
(buffer time for re-run if failure)
• Windows server with Powershell scripts
• Analysis workflow includes:
• Coverage stats and QC
• VS-CNV calling
• Annotation and filtering
• Gene panel and phenotype automation
Region Testing Lab Scaling Exomes

16. Pharma Services Scaling Genomes
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• Deliverables are locked down through versioned
pipelines
• All steps inside read-only Docker containers
• VSPipeline + Annotations + Project Templates
• Local Linux cluster and cloud deployment scenarios
• Secure Cloud deployments in China with no
outbound internet access
• Batch node job:
• Download docker containers
• [Cloud only] Download BAM, VCF from storage
• Docker run pipeline
• [Cloud only] Upload results to storage

17. Windows/Linux Server Running Desktop App
Network Attached Server (5+TB)
VSWarehouse Server (Linux)
Automated Workflow Servers
• Sequencer Output (FASTQ)
• Secondary Analysis Data (VCFs/BAMs)
• VarSeq Projects
• Annotation Data
• User Preferences
VSPipeline
• VS-CNV
• BAM/VCF => VarSeq Projects
• Aggregate Variant Projects
• Interpretation Catalogs
• Monthly ClinVar Changes
• Sample Reports
Users Interact through Remote Desktop
Golden Helix Deployment Diagram

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19. NIH Grant Funding Acknowledgments
19
• Research reported in this publication was supported by the National Institute Of General Medical Sciences of
the National Institutes of Health under:
o Award Number R43GM128485-01
o Award Number R43GM128485-02
o Award Number 2R44 GM125432-01
o Award Number 2R44 GM125432-02
o Montana SMIR/STTR Matching Funds Program Grant Agreement Number 19-51-RCSBIR-005
• PI is Dr. Andreas Scherer, CEO of Golden Helix.
• The content is solely the responsibility of the authors and does not necessarily represent the official views of the
National Institutes of Health.

20. ESHG 2022
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Develop repeatable cancer and germline interpretation workflows
that scale from panels to whole exomes and genomes
Maximizing Profitability in your NGS Testing Lab
Monday, June 13, 12:00-13:00 PM | Room number – 2.32 & – 2.33, level -2
Seating is limited, light refreshments will be offered
Visit our booth #X5-476
• Discussions with Golden Helix team about your lab’s specific needs
• See our product in action during our in-booth demos
• Don’t leave ESHG without one of our famous t-shirts
Attend our Corporate Satellite!

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