PhoRank 2.0: Improved Phenotype-Based Gene Ranking in VarSeq
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When performing variant analysis on whole exome or large gene panels, clinicians must sort through thousands of variants to determine which variants are most likely to be associated with the patient’s phenotypes. To assist with this process, we have implemented the PhoRank algorithm, which incorporates phenotypic associations to highlight the most relevant genes with potentially damaging variants. PhoRank 1.0 supports researchers leveraging all possible gene-disease associations by traversing multiple gene and pathway ontologies. Recent papers have demonstrated new techniques that have improved ranking performance in a clinical context. We have incorporated these new strategies into PhoRank 2.0: providing better ranking and improved computational performance for most clinically diagnostic and testing scenarios. Join us in this webinar as we cover: Utility of gene ranking in genetic testing Scenarios that warrant the use of PhoRank 2.0 New ranking strategies provided by recent papers Benchmarks of PhoRank 2.0 on published datasets Golden Helix provides a comprehensive solution for NGS testing labs to perform best practice guidelines such as ACMG and AMP. Our gene ranking methods provide a vital role in scaling tests to large gene panels and exomes. Please join us as we review the testing workflow and how this significant update to our gene ranking algorithm fits into the testing workflow.
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PhoRank 2.0: Improved Phenotype-Based Gene Ranking in VarSeq
- 1. PhoRank 2.0: Improved Phenotype- Based Gene Ranking in VarSeq June 9, 2021 Presented by Nate Fortier, Ph.D, Director of Research
- 3. PhoRank 2.0: Improved Phenotype- Based Gene Ranking in VarSeq June 9, 2021 Presented by Nate Fortier, Ph.D, Director of Research
- 4. NIH Grant Funding Acknowledgments 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 • 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. Who Are We? 5 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
- 6. Cited in 1,000s of Peer-Reviewed Publications 6
- 7. Over 400 Customers Globally 7
- 8. When you choose Golden Helix, you receive more than just the software 8 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
- 9. 9
- 10. PhoRank 10 • Prioritizes genes based on phenotypic associations • Initial algorithm inspired by methods used by the Phevor algorithm (Singleton et al.) • Uses ontology propagation to combines information across multiple biomedical ontologies • Designed for identification of genes without established phenotypic associations
- 11. Advancements in Phenotype Ranking 11 • PhoRank was first released in 2015 • Innovation has continued in the phenotype-gene ranking space • After review of literature, three algorithms were selected for comparison: • Phenolyzer: uses a multiple gene-disease databases and ontologies in conjunction with machine learning model to prioritize genes • Exomiser: combines ontology-based gene prioritization with model organism data and protein-protein interaction data • Masino’s Algorithm: orders genes by the semantic similarity between the phenotypes associated with each gene and those associated with the patient
- 12. Benchmarking 12 • Compared using dataset from Pengelly et al. • Phenotypes for 21 individuals with confirmed molecular diagnosis • Authors selected HPO terms based on review of clinical notes • Compared ranking of causal genes across four algorithms
- 14. Benchmark Results 14 Phenolyzer Exomizer Masino PhoRank 1.0 Average (min, max) 453 (1, 6588) 3461 (6, 6228) 89 (1, 762) 478 (7, 1729) Samples with Gene in Top 10 6 2 9 1 Runtime (minutes) 38 5 16 84
- 15. Discussion 15 • Masino’s algorithm demonstrated superior performance on the benchmark dataset • Use of other gene-disease databases, model organism data, and protein-protein interaction data failed to improve performance • More complex methods may be better suited to ranking genes without established phenotypic associations • Masino’s algorithm used a basis for new VarSeq PhoRank Clinical algorithm
- 16. PhoRank Updates 16 • Excels at finding gene associations in individuals with atypical disease presentations • Falls short on phenotypes with well-established gene associations • Developed based on literature published since development of PhoRank • Association search restricted to HPO • Provides better rankings for genes with established phenotypic associations PhoRank Research PhoRank Clinical
- 17. 17 Product Demo
- 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. Summer 2021 Software Specials 20 Golden Helix will be offering a limited number of the following deals through June 30th, 2021: VarSeq w/ CADD & OMIM (3-users) – $8,995 VSClinical, CNV, Sentieon Tier 1 (2-users) – $19,995 VSClinical, AMP, CNV, Sentieon Tier 1 (2-users) – $29,995 Small Warehouse License: VS-CNV, VSClinical+ AMP, Sentieon Tier 1, VSReports, VSPipeline – (2-users) – $48K Large Warehouse License: VS-CNV, VSClinical + AMP, Sentieon Tier 1, VSReports, VSPipeline (<10 users) – $120K Commit to a two-year purchase with payments upfront, then you will receive a third year at no cost. Very competitive throughout our global community. If you are interested, we encourage you to reserve a bundle as soon as possible by emailing info@goldenhelix.com. Remaining
- 21. eBook Update: Clinical Variant Analysis for Cancer 21 Want a free copy? Request one in the questions or chat panel and our team will follow up with you!
- 22. COVID-19 Publications & Articles 22 Investigating the Global Spread of SARS-CoV-2 Leveraging Next-Gen Sequencing and Principal Component Analysis European Journal of Clinical and Biomedical Sciences Christiane Scherer, James Grover, Darby Kammeraad, Gabe Rudy, Andreas Scherer Diagnosing and Tracking COVID-19 Infections Leveraging Next-Gen Sequencing The Journal of Precision Medicine Feature | Andreas Scherer, Christiane Scherer Golden Helix: Enabling Precision Medicine with Cutting- Edge NGS Technologies Clinical OMICs Feature Leveraging Next-Generation Sequencing Technology in the Fight Against COVID-19 Clinical Lab Manager Feature | Andreas Scherer SARS-CoV-2 Global Spreading Investigation using Principal Component Analysis of Sequence Variants Journal of Genetics and Genome Research Christiane Scherer, James Grover, Darby Kammeraad, Gabe Rudy, Andreas Scherer Analysis of 46,046 SARS-CoV-2 whole-genomes leveraging principal component analysis (PCA) Pre-Release | Submitted for Publication Christiane Scherer, James Grover, Darby Kammeraad, Gabe Rudy, Andreas Scherer
Editor's Notes
- 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. Additionally we are also grateful for receiving 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.
- GoldenHelix is a global bioinformatics software and analytics company that enables research and clinical practices to analyze large genomic datasets. We were originally founded in 1998 based of pharmacogenomics work performed at GalxoSmithKline who still is a primary investor in our company. We currently have two flagship products Varseq and SNP and Variation Suite (SVS) for short. VarSeq serves as a clinical tertiary analysis tool tailored for basic variant annotation and filtration but additionally, users have access to automated AMP or ACMG variant guidelines. VarSeq also has the capability to detect copy number variations scaling from single exome to large aneuploidy level events. Additionally, the finalization of variant interpretation and classification is further optimized with the VarSeq clinical reporting capability. Users can integrate all of these features into a standardized workflow which can be automated further with batch runs via VSPipeline. Paired with VarSeq is VSWarehouse which serves as the repository for this large amount of useful genomic data. Warehouse not only solves the issue of data storage for ever increasing genomic content, but also is fully query able and auditable with definability of user access for project managers or collaborators. Lastly, our research platform, SVS, enables researchers to perform complex analysis and visualizations on genomic and phenotypic data. SVS has a range of tools to perform GWAS, Genomic Prediction, RNA-Seq analysis and the ability to process CNVs
- Our software has been very well received by the industry. We have been cited in thousands of peer-reviewed publications and that’s a testament to our customer base.
- We work with over 400 organizations all over the globe. top-tier institutions, Stanford and yale government organizations, NCI clinics, Sick kids genetic testing labs, prevention genetics and lineage With now well over 20,000 installs of our products and with 1,000’s of unique users. So why is this relevant to you?
- This means that over the course of 20 years our products have received a lot user feedback, which we immediately incorporate into developing and releasing newer versions of our products. We receive active research grants to support the advancement of our software capability which is always directed from our user feedback and awareness of the industry needs. We also stay relevant in the community by regularly attending conferences and providing useful product information via eBooks, tutorials, and blog posts. Your access to the software is a simple subscription based model where we don’t charge per sample nor per version. You also maintain full access to our support and training staff to get you up to speed quickly with your analysis
- The Golden Helix stack provides the capability to start with an initial FASTQ file all the way down to a clinical report. This is achievable through our partnership with Sentieon providing the alignment and variant calling steps to produce the VCF and BAM files. This output serves as the basis for CNV detection and import data for your tertiary analysis in VarSeq. If you are performing NGS based CNV analysis, Golden Helix is the market leader; supported by studies like Robarts Research Institute showing 100% concordance with MLPA. Additionally, the imported variants in your VarSeq project can be run through VSClinical’s automated ACMG and AMP guidelines. After completing secondary and tertiary processing, all analysis can be rendered into a clinical report which can be stored in VSWarehouse providing researchers and clinicians with access to this information and to view previous findings.
- When performing variant analysis on whole exome sequencing data, clinicians must sort through thousands of variants to determine which variants are most likely to be associated with the patient’s phenotypes. To assist with this process, we have implemented the PhoRank algorithm, which incorporates phenotypic associations to highlight the most relevant genes with potentially damaging variants. The initial implementation of the PhoRank algorithm in VarSeq was based on the methods utilized by the Phevor algorithm published by Singleton et al. This approach utilizes a method called ontology propagation to combine information across multiple biomedical ontologies. This method is particularly useful in research settings where the clinician is interested in finding possible gene-disease associations in individuals with previously undescribed or atypical disease presentations.
- Since the release of PhoRank in 2015, researchers have continued to innovate in the field of phenotype-based gene ranking. During our review of the literature, three algorithms stuck out to us as offering the ability to improve our existing PhoRank algorithm. Phenolyzer: uses a multiple gene-disease databases and ontologies in conjunction with machine learning model to prioritize genes Exomiser: combines ontology-based gene prioritization with model organism data and protein-protein interaction data Masino’s Algorithm: orders genes by the semantic similarity between the phenotypes associated with each gene and those associated with the patient We have compared these algorithms to evaluate the possibility of utilizing their methods in an updated PhoRank algorithm.
- We compared these algorithms using a dataset developed by Pengelly et al. for the purpose of benchmarking phenotype-gene ranking algorithms. This dataset consists of 21 individuals with previously established clinically confirmed molecular diagnoses determined through traditional testing. Phenotypes for each individual were described through a comprehensive set of HPO terms, which were selected based upon review of the clinical notes for each patient. We compared the ranking of the causal gene for each individual across the four algorithms discussed previously.
- For this benchmark dataset, Masino’s algorithm demonstrated superior performance in terms of both average ranking of the causal gene and runtime. The competing approaches incorporation of additional gene-disease databases, model organism data, and protein-protein interaction data did not seem to provide any advantage on this benchmark dataset. It should be noted that we selected a benchmark that includes many phenotypes with well-established gene relationships, as would be expected in a clinical setting. We suspect that these more complex methods may be better suited to ranking genes without established phenotypic associations, but further testing would be needed to demonstrate this. Based on these results, we’ve chosen to use Masino’s algorithm as the basis for a new VarSeq algorithm that we have dubbed PhoRank Clinical.
- While PhoRank Research Excels at finding gene associations in individuals with atypical disease presentations, this method falls short when applied to individuals presenting with disease phenotypes that have established gene associations. The use of ontology propagation across multiple ontologies produces less optimal gene rankings compared Masino’s algorithm when applied to phenotypes with well-established gene associations. To address this, we have incorporated the methods used by Masino’s algorithm into PhoRank Clinical, a new phenotype ranking algorithm in VarSeq which utilizes semantic similarity to rank genes based on their phenotype associations in the Human Phenotype Ontology. Our empirical analysis demonstrates that this new algorithm provides better gene rankings when applied to phenotypes with established gene associations, while producing results in a fraction of the time required by our existing PhoRank algorithm. This feature will be available in the next VarSeq release which is slated for this fall.
- Let’s go ahead and transition over to my VarSeq project. This project will have 3 whole exome samples already imported and I will also be using a bone disease gene panel to find clinically relevant variants and CNVs.
- 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. Additionally we are also grateful for receiving 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.
- 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. Additionally we are also grateful for receiving 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.
- Again, I want to mention how grateful we are we are thankful of grants such as this which support the advancement and development of our software to create the high quality software you'll see today. So with that covered, lets take a few minutes to talk a little bit about our company Golden Helix.
- Over the course of the 2020 period, we worked with a clinical lab in Germany to do the analysis of 46k samples to break down population structure for the genomic variability among those samples. Excited for this upcoming publication coming up for everyone to read.