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VarSeq 2.6.0: Advancing Pharmacogenomics and Genomic Analysis
The document details the presentation of VarSeq 2.6.0 by Gabe Rudy, highlighting advancements in pharmacogenomics and genomic analysis. Supported by NIH funding, Golden Helix is recognized as a leading provider of bioinformatics software for next-generation sequencing data analysis, enabling efficient workflows and variant detection for various clinical applications. The presentation also covers updates in genomic databases and highlights the importance of comprehensive testing in clinical decision-making.
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VarSeq 2.6.0: Advancing Pharmacogenomics and Genomic Analysis
- 1. April, 10, 2024 Presentedby Gabe Rudy VarSeq 2.6.0: Advancing Pharmacogenomics and Genomic Analysis
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- 3. VarSeq 2.6.0: AdvancingPharmacogenomics and Genomic Analysis April, 10, 2024 Presented by Gabe Rudy
- 4. NIH Grant FundingAcknowledgments 4 • 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 o NIH SBIR Grant 1R43HG013456-01 • 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.
- 5. Golden Helix at-a-Glace 5 CompanySnapshot: Leading SaaS provider of tertiary genomic analysis solutions for NGS labs Golden Helix is a SaaS bioinformatics solution provider specializing in next-gen sequencing (“NGS”) data analysis The Company’s software enables automated workflows and variant analysis for gene panels, exomes, and whole genomes Key Clinical Applications Prenatal testing Hereditary disease testing Reproductive testing Oncology Marquee Global Clients Golden Helix’s solutions allow clients to increase throughput, ensure consistent quality, maximize revenue, and save time 1998 Company Founded Bozeman, Montana Headquarters Recognitions Government Research Pharmaceuticals Agrigenomics Testing Labs Translational Labs Human Genetics Research Hospitals Academia Publications Content & Resources Pharmacogenetics testing
- 6. NGS Clinical Workflow GoldenHelix provides comprehensive data analytics software that scales across gene panels, whole exomes, and whole genomes DNA Extraction in Wet Lab and Sequence Generation Interpretation and Result Reporting Primary Read Processing and Quality Filtering Alignment and Variant Calling Secondary *Golden Helix provides Secondary Analysis through a reseller agreement Tertiary Golden Helix’s software and primary focus Comprehensive secondary and tertiary analysis solutions for primary data aggregated by all commercially available sequencers Type Size Gene Panel Small (100MB) Whole Exome Medium (1GB) Whole Genome Large (100GB) Cancer use case Hereditary use case Process Analysis … and scales across multiple data set sizes for cancer and hereditary use cases Filtering and Annotation Data Warehousing Workflow Automation Golden Helix works with all major sequencers…
- 7. NGS Clinical Workflow GoldenHelix provides comprehensive data analytics software that scales across gene panels, whole exomes, and whole genomes DNA Extraction in Wet Lab and Sequence Generation VSClinical Primary Sentieon PLOT & UMI Sentieon DNAseq Secondary *Golden Helix provides Secondary Analysis through a reseller agreement Tertiary Golden Helix’s software and primary focus Comprehensive secondary and tertiary analysis solutions for primary data aggregated by all commercially available sequencers Type Size Gene Panel Small (100MB) Whole Exome Medium (1GB) Whole Genome Large (100GB) Cancer use case Hereditary use case Process Analysis … and scales across multiple data set sizes for cancer and hereditary use cases VarSeq VSWarehouse VSPipeline Golden Helix works with all major sequencers…
- 8. ISO Certification 13485:2016 8 •ISO 13485:2016 from TÜV SÜD • ISO 13485:2016 is an international standard that specifies requirements for a quality management system (QMS) for organizations involved in the design, development, production, and servicing of medical devices. o maintain a quality management system o demonstrate sufficient risk management o show consistent tracking of customer satisfaction and safety in the market o demonstrate continued improvement efforts on the product and system level. • ISO 13485:2016 is designed to objectively document that we are holding ourselves to the highest quality standards as we are providing innovative solutions to hospitals, testing labs, and research institutions globally.
- 9. 10 Streamlining Clinical Decision-MakingThrough Comprehensive Genomic Testing • Operational efficiency: Consolidate multiple tests (Microarray, RT-PCR, MLPA) into one comprehensive NGS panel or exome, reducing time and cost. • Enhanced variant detection: Identify rare and private variants that may be missed by traditional methods, leading to more accurate diagnoses and treatment and higher clinical yield. • One test for multiple outcomes: Gain insights into diagnosis, prognosis, and potential treatment response with a single test, simplifying care and improving efficiency. Adoption of NGS and Exomes in Genetic Testing MicroArray RT-PCR Next Generation Sequencing +
- 10. Clinical Analysis forNGS 11 Up to Date Annotations Making accurate clinical determinations requires the latest annotations from various sources Workflow and Interpretation Support Each clinical use case requires specialized workflows to go from variants to report Variant Detection and Calling Secondary analysis tools to process NGS data and needed and require specialized algorithms
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- 12. VarSeq 2.6: EnablingNGS adoption 13 VS-CNV optimized for exomes VSPGx support for NGS and transition from microarrays New and updated annotations gnomAD v4
- 13. 14 Small:1 50b+ Medium: 1 – 10Kb Large: 10Kb+ Gene panel Whole exome Whole genome MLPA CMA VS-CNV Detectable events Supported Data types One single testing paradigm True simplification of clinical workflow Saves time and money – all on site Value of CNV Detection in NGS
- 14. 15 VS-CNV Exome Studywith Twist Exome + Backbone Kit Twist Exome 2.0 Plus Comprehensive Exome Spike-in Coriell CNVPANEL01 reference set of 43 samples. Benchmark driven optimizations Sensitivity of the VS-CNV algorithm is 100% across all probe densities Filtering workflow has no impact for the 100 kb probe density The 25 kb and 50 kb probe densities result in reduced filter workflow sensitivity. P715: Analyzing performance of the Twist exome with CNV backbone at various probe densities leveraging Golden Helix VS- CNV https://www.sciencedirect.com/science/article/pii/S2949774424007659
- 15. 16 • NGS andArray-based Pharmacogenomic Workflow: • Gene panels, exomes and genome support • Call diplotypes (star-alleles) in known pharmacogenes • Phenotype and drug recommendation annotation • Based on CPIC Tier 1 database of gene-drug interactions • PGx Reporting from VarSeq • Current Patient Medications and Recommendations • Gene-Drug Interactions • Prescribing Recommendations • Phenotype Associations • Tested Alleles • Word-based reports are fully customizable
- 16. Golden Helix AnnotationUpdates 22 Snapshot of Databases •ClinVar •CIViC •ClinGen Interpretations •ClinGen Genes •dbVar •Decipher •BRCA Exchange •Genetic Home Reference •InterPro •UK PanelApp Project Releases •RefSeq Genes •gnomAD •1000 Genomes •Allele Frequency Aggregator •Mitomap •ICGC Somatic Mutations •TCGA Variants •dbSNP •dbNSFP Custom & Licensed •Golden Helix CancerKB •Cancer Ontology •DrugBank •CPIC PGx •OMIM •Clinical Trials •CADD •SIFT + PolyPhen2 Predictions on MSA
- 17. 23 Classifier Algorithms: VariantAnalysis with VSClinical In-silico annotations and in-silico algorithms to determine criteria and classification • ACMG Classifier (Default Choices) • 1kG Phase3 Variants Frequencies • gnomAD Joint Variant Frequencies 4.0 • gnomAD Gene Constraints 4.0 • ClinVar Variants • ClinVar Transcript Counts • RefSeq Genes (gene impact / splicing analysis) • Conservation Scores • SIFT/PolyPhen2 Missense Predictions (MSA) • Cancer Classifer (in Addition to the ACMG Classifier) • CIViC Variant Clinical Evidence Summaries • CIViC Region Clinical Evidence Summaries • Cancer Variant Frequencies • InterPro Regions • Cancer Hotspots
- 18. Gnomad V4 summary 24 •Released November 2023 • Total individuals: 807,162 • Exomes: 730,947 (416,555 from UK Biobank) • Genomes: 76,215 (from gnomAD v3) • Major “Continental” groups: • Admixed American • African • East Asian • European (Non-Finnish) • South Asian • Remaining • Middle Eastern, European Finish, Ashkenazi Jewish, Amish 6503 61486 125748 730947 15708 76156 76215 0 100000 200000 300000 400000 500000 600000 700000 800000 NHLBI ESP6500SI ExAC gnomAD v2 gnomAD v3 gnomAD v4 Exome Genome
- 19. Golden Helix Exclusive: “Joint” and GRCh37 tracks 25 Displaying variants in between gnomAD Exomes (top), Genomes (bottom), and Joint Frequency (middle) tracks • Frequency Tracks (Number of Variants) • gnomAD Exomes Variant Frequencies 4.0: 76,108,118 • gnomAD Genomes Variant Frequencies 4.0: 681,676,844 • gnomAD Joint Variant Frequencies 4.0: 736,032,502 • Additional Tracks: • gnomAD Structural Variants 4.0 • gnomAD High Frequency CNV Regions • gnomAD - Gene Constraint 4.0 • gnomAD Joint Predictions v4.0 • All tracks Lifted “Under” to GRCh37 as well as native GRCh38
- 20. Caution : PopulationCatalog, not “Healthy” Controls 26
- 21. VarSeq 2.6: EnablingNGS adoption 27 VS-CNV optimized for exomes VSPGx support for NGS and transition from microarrays New and updated annotations gnomAD v4
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- 23. NIH Grant FundingAcknowledgments 29 • 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 o NIH SBIR Grant 1R43HG013456-01 • 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.
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- 26. PacBio Prism 2024 32 Ifyou are attending any of the upcoming PacBio Prism events, please visit us in the following cities! • May 2nd – Bethesda • Darby Kammeraad, Director of Field Application Services • May 8th – Houston • Casey Fullem, Director of Marketing • May 14th – San Francisco • Andreas Scherer, President and CEO • Gabe Rudy, VP of Product and Engineering • May 21st – Boston • Gabe Rudy, VP of Product and Engineering
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Editor's Notes
- #2 Thanks Casey! We can’t wait to dive in to this subject
- #4 Thanks Casey! We can’t wait to dive in to this subject
- #5 Before we start diving into the subject, I wanted mention our appreciation for our grant funding from NIH. The research reported in this publication was supported by the National institute of general medical sciences of the national institutes of health under the listed awards. We are also grateful to have received local grant funding from the state of Montana. Our PI is Dr. Andreas Scherer who is also the CEO at Golden Helix and the content described today is the responsibility of the authors and does not officially represent the views of the NIH. So with that covered, lets take just a few minutes to talk a little bit about our company Golden Helix.
- #7 Slide Goal / Importance: - Explain the NGS value chain for an industry outsider and how Golden Helix fits within it
- #8 Slide Goal / Importance: - Explain the NGS value chain for an industry outsider and how Golden Helix fits within it
- #9 Golden Helix is proud to announce that we have received the certification for ISO 13485:2016 from TUV SUD.
- #12 Review the steps and various technology components of an NGS test This will make it clear that there is a need to integrate quite a few pieces of technology Review how automation can tackle this problem, both the complexity of the integration but also improving quality of your test output Demonstrations in two parts: How far automation can take you: fastq to report, and how human interpretation and review is still incorporated into the process with the rich VSClinical interface
- #13 VarSeq facilitates handling of all your variant types for both somatic and germline analysis. The utility of the software can be broken into stages. The first being the import of your SNVs/indels, CNVs and fusions or breakends, then passed through a user defined variant filter coupled with many annotations and algorithms to isolate the clinically relevant variant. These filters and project structure are saved as templates to facilitate automation with our VSPipeline command line tool. Once the clinically relevant variant is isolated, it is then moved into stage 2 or VSClinical which serves as the interpretation hub to collect all relevant evidence for germline or somatic variants via the ACMG and AMP guidelines. Once the variant is evaluated, it is saved locally in a user database and carried into the final report stage. You’ll learn today that the reporting feature comes with quite expansive options for the user to customize, but overall, think of VarSeq as the one software suit solution to handle full import of all variants to isolating the reported findings of clinically relevant variants. So now that you have a high level understanding of the tools purpose, lets move into discussing today’s topic. VSClinical’s oncogenicity scoring system was developed in consultation with the GA4GH variant interpretation in Cancer Consortium (VICC) as a criteria-based system that parallels the ACMG guidelines to rank variants according to their pathogenicity in the context of cancers and is useful for determining whether a variant is likely to be a cancer driver mutation. The oncogenicity scoring algorithm is also now available in VarSeq to use as a filter for prioritizing the most impactful cancer mutations in any somatic workflow. Our oncogenicity score is additive such that scores exceeding ‘3’ indicating an ‘oncogenic’ or ‘likely oncogenic’ effect.
- #14 ESHG talk pitch Labs are successful when then have a test in production that is economical to run. But this is a changing landscpape Need to respond to the marketplace, improving tests and adding new tests So for both new labs and existing labs, the time it takes to take a test through the design and validation process and move to production can be the key to profitability Choosing your vendors wisely can make all the difference. Do they have the capability, flexibility and ability to automate these process. Once in production, do they scale with you. Both performance and per-unit costs.
- #15 To best explain the value of VS-CNV’s detection capabilities, we can compare against traditional best-practice methods. One traditional methods is MLPA which is ideally tailored for detecting smaller CNVs across a small number of pre-defined genes. In addition to being expensive, one additional disadvantage of MLPA is the inability to detect larger events, which chromosomal microarrays can handle. The large aneuploidy level of CMA event detection is typically from 10 kb or larger. When used in conjunction with Whole Exome Sequencing data VS-CNV accurately detects not only the 10kb and larger events but can detect events down to a single gene and even exon. VarSeq breaks down the barriers of the limitations across CMA and MLPA methods and gives the user the full-scale capability to analyze both small variants and CNVs in a single software suite while saving you a fortune on assays. Another value here is that the CNV detection is performed by you, eliminating the need to outsource this process which only adds time and inefficiency in both producing and understanding the results. Each of these approaches have their nuances, and NGS is no exception, lets discuss some of the associated challenges and how they can be addressed.
- #16 The sensitivity of the VS-CNV algorithm is 100% across all probe densities, with all events in the benchmark dataset being called over at least 75% of overlapping targets. The filtering workflow does not have any impact on sensitivity for the 100 kb probe density, with no true positive events being filtered out at this probe density. However, the 25 kb and 50 kb probe densities do result in a slightly reduced sensitivity for the filtering workflow, with two benchmark CNVs filtered out due to being flagged has having High Controls Variation. This flag indicates a high degree of variation between the reference samples for the region of interest. When examined, the normalized coverage of the targets in these regions appears to have much more variation across samples for the higher probe densities, which results in these CNVs being flagged. This may be an indication that the backbone probe regions exhibit higher variation in coverage between samples resulting in increased coverage variably at higher probe densities. Based on these results, a conclusion can be made on the question of probe density. For the samples in this experiment, the probe density does not impact the sensitivity of the VS-CNV algorithm. However, the higher probe densities of 25 kb and 50kb do result in reduced sensitivity for the quality filtering workflow due to increased variation in normalized coverage between reference samples.
- #17 After running the PGx Variant Detection and Recommendations algorithm, a clinical report can be generated using VarSeq’s customizable reporting system. Clinical reports are generated using an easy-to-modify Microsoft Word report template and VarSeq comes shipped with an initial PGx report template that serves as an excellent starting point for creating custom reports. Information included in this report includes Implications for Current Patient Medications, Gene-Drug Interactions, Prescribing Recommendations, Phenotype Associations, and a description of all tested alleles. The calling of diplotypes, annotation of phenotypes, and reporting of recommendations is performed by VS-PGx in just a few simple steps with minimal user involvement. While the annotations and report templates can be customized, the annotation tracks and report templates provided by VarSeq have everything you need to start annotating and reporting all alleles defined in the CPIC database.
- #18 Detection of CNVs starts with the coverage data in the BAM file. This is not a straightforward task and our development team has worked extremely hard to develop an algorithm that addresses the associated challenges with coverage data. These challenges are that coverage can vary between samples and individual targets. To account for this variability, we normalize the coverage data for given sample and compare it to a normalized mean value of diploid coverage across a collection of germline reference samples. It is important to keep in mind that the reference set does not need to be solely control samples with no CNV events. The benefit of having multiple reference samples and averaging the normalized coverage is to prevent any event in a single reference sample from skewing the reference based normal region overall. However, for this approach to work effectively, there are some requirements which include having an adequate number of references, no less than 30, and making sure they come from the same platform and prep methods though not necessarily the same run while also having adequate coverage ideally 100x or greater. This image is a great example of the need for reference samples. When looking at the 3 samples on the right and each of their coverage data across BRCA2, we may guess that sample 11 possibly has a heterozygous deletion since the coverage is nearly half as much as samples 12 and 13. Unfortunately it isn’t this simple and detecting any CNV is essentially impossible to tell from the naked eye since a single samples coverage doesn't provide enough information alone to detect these events. This is apparent in the next slide when after running these samples through VarSeq, where no deletion is detected in sample 11, but we do find a duplication in sample 13.
- #19 This is meant to be a simplified representation of how VarSeq is handling the sample coverage data comparison against a series of reference samples and ultimately make a CNV call. Samples 1 through 5 are meant to represent the reference set and their averaged normalized coverage will be compared to the sample of interest, sample 13. So first things first, we need to normalize the coverage data for the calculated mean coverage depth in each target region. Then the normalized values will be averaged across the reference set and compared to the normalized coverage in sample 13. One additional note here: we naturally expect CNVs to be rare in occurrence and with the method of normalizing the coverage data to get a sense of diploid regions, the CNV caller is going to build a unique reference set for each imported sample by selecting samples with the lowest percent difference, or most similar coverage profile. In VarSeq you will have access to the list of reference samples used for normalization and the percent difference in coverage. The algorithm will also automatically match genders for the reference samples when investigating non-autosomal regions so you don’t have to worry about making male or female specific reference sets. Users will find a comprehensive output of all target coverage comparisons between the sample of interest and the reference samples. You can see this in the image in the bottom right were we are comparing the normalized mean depth values and we see a trend in higher normalized coverage for sample 13 for these 4 exons in BRCA2. The next question is “are the increased values enough to justify the CNV call as a duplication?
- #20 The answer is yes. VarSeq has detected a consistently significant increase in coverage for these regions and has called the duplication in Sample13. VarSeq uses key metrics to calculate the strength of the call and these metrics are: Ratio: which is simply the samples normalized coverage divided by the reference set normalized coverage. A ratio ~1 means the region can be interpreted as having equal coverage for your sample and reference set, whereas a ratio of 0.5 is indicative of heterozygous deletion and a ratio of 1.5 being a possible duplication. This you can see in the top of the image where the ratios for each region in this CNV near 1.5 for this duplication The other critical metric is Z-score or standard deviation of the samples normalized coverage relative to the controls. The next plot on the image shows our standard deviations ranging from over 6 to 3.5, strong evidence for the call. And lastly the Variant Allele Frequency, and while this is a secondary metric, it does allow us to reduce our rate of false positives and exclude problematic regions from the algorithm. The VAF is used to detect regions of Loss of Heterozygosity to remove large regions of non normal coverage that can skew the CNV detection, we utilize this as a quality control process when running the CNV caller on exome or whole genome scale data. Now the computational process used by the algorithm depends on the size of the events. If you are performing CNV analysis on gene panels or exomes, these metrics are fed into a probabilistic model used to call CNVs. Alternatively, the algorithm will use an optimal segmentation approach for large cytogenetic events. Rest assured we have plenty of documentation to guide our users through the CNV project creation process and our support staff is available for training as well. But now that we’ve covered the fundamentals of CNV detection, lets move into the evaluation and reporting discussion in VSClinical.
- #21 Within VarSeq is VSClinical which serves as our ACMG and AMP guideline interpretation hub. It is worth discussing the value points in having a true automated guideline interface. First off, you want to maintain consistency in results. This is relevant even for a single user suffering from potential workflow fatigue or comparing multiple user’s interpretations. More discretely, is the added value of getting new users familiar with the guidelines more quickly. The interpretation hub serves as a great educational interface to account for all relevant guideline criteria. Lastly, but critical is our support for integrating these guidelines into the software so that users spend more time processing variant and less time tweaking their bioinformatic pipeline. Case and point is our recent development of automating the ACMG and ClinGen interpretation and reporting standards for copy number variants.
- #22 Overall our goal is to provide a streamlined approach of processing the 5 distinct sections of approximately 80 scoring criteria for the evaluation of everything from small intragenic CNVs to large aneuploidy level events. The scoring system is on a scale of -1 benign to +1 pathogenic which you can see is represented on the right with a detect pathogenic heterozygous deletion in BRCA2. Alongside the release of these standards in early 2020, our development team attended all available web sessions provided from the ClinGen group and even got direct feedback from the authors to ensure that our software achieves all goals of the guidelines.
- #24 Clinical Decision Support following best-practice industry standard guidelines
- #25 Amish (Genomes only)
- #27 10 3 or 4 star variants in ClinVar in gnomAD 2 with Pathogenic variants in a homozygous state, there are 33 variants, around 70 individuals alone have Cystic Fibrosis Also, allele frequency went up from 1.2% to 1.5% with the massive UK biobank samples, which probably reflects a more natural population state, but is a very high frequency for a pathogenic variant.
- #28 ESHG talk pitch Labs are successful when then have a test in production that is economical to run. But this is a changing landscpape Need to respond to the marketplace, improving tests and adding new tests So for both new labs and existing labs, the time it takes to take a test through the design and validation process and move to production can be the key to profitability Choosing your vendors wisely can make all the difference. Do they have the capability, flexibility and ability to automate these process. Once in production, do they scale with you. Both performance and per-unit costs.
- #30 Before wrapping up, we'd like to again state our appreciation for the grants included here. And with that, I'll hand things back to Casey to talk about some exciting marketing updates and take us through a Q&A session.
- #34 Questions: Are we limited to TSO500 data for somatic analysis or can we build our own panel and include any number of customer genomic signatures? Answer: No limitations at all. User can build any somatic panel or workflow overall and include any specific sample level data either in the variant tables or from the sample manifest. TSO data is just one example among any physical or virtual panel your lab would run 2. Can the CNV calling in the whole exome example be automated with pipeline too? A: Yes! When you build the original workflow template, you can include CNV calling, or importing externally called CNVs and SVs. These will also go through the annotation process. 3. How to save time and effort in variant interpretation if you or a teammate need to sequence the whole genome for a sample already analyzed in VarSeq as a gene panel?A: You can save already assessed variant interpretations to assessment catalogs and reuse to avoid rework.