Computational methods for predicting the effects of genetic variants are used by clinicians for interpreting genetic test results and by researchers for scientific exploration. However, their utility is limited by several issues. First, hundreds of methods are available without clear standards for selection. Additionally, discrepancies among methods regarding the impact of specific variants; challenges in interpreting numerical or categorical predictions (“deleterious”, “pathogenic”, “likely pathogenic” etc.); and the details about the data and logic used for predictions aren’t clear, making it hard to understand results. Furthermore, popular “meta-predictors” designed to combine results from many prediction tools and new AI-based methods have made these transparency and interpretability issues even worse. Users of meta-predictors often don’t know which core predictors matter the most, or how the core predictors redundantly utilize the same features. AI-based predictions hide their processing, taking raw data through a black box and producing outputs without explanation.
This year OpenCRAVAT and CIViC will collaborate in a joint effort to address these issues. As a first step, we will be disseminating pioneering work led by Drs. Vikas Pejaver, Pedja Radivojac, and Steven Brenner, to calibrate the scores of variant effect predictors to strengths of evidence within the ACMG/AMP framework for clinical variant classification. A detailed description of the calibration algorithm can be found in Pejaver et al. 2022.
Clinical geneticists are tasked with interpreting inherited variants identified in genetic testing. This is done with protocols set by professional organizations such as the American College of Medical Genetics (ACMG), the Association for Molecular Pathology (AMP) and others. The ACMG/AMP protocol published in Richards et al. in 2018 classifies variants as Pathogenic or Benign, according to a series of criteria that include molecular properties, family history, population frequencies, functional assays, and computational variant effect predictors, which are the topic of this blog. Each criteria is assessed by rules that measure its “strength of evidence”. In the 2018 guidelines, computational variant effect predictors were not given much weight, because of concerns about their accuracy, interpretability, and transparency. Guidelines for usage of computational tools suggested looking for consensus among multiple variant effect predictors. Many of us disagree with this approach, and we have worked to address these concerns and provide updated recommendations. The work presented in Pejaver et al. AJHG 2022 centers on two criteria known as “BP4” and “PP4”.
The criteria BP4 and PP3 for computational variant effect predictors are included in the RIchards classification framework as supporting criteria for benignity and pathogenicity, respectively:
Table 4 in Pejaver et al. summarizes recommendations for changes in these criteria:
Pejaver et al. suggest that to map variant effect predictor scores onto ACMG/AMP evidence strengths for PP3 and BP4, the first step is to transform the raw scores from a predictor into posterior probabilities of pathogenicity or benignity. The next step is to transform posterior probabilities into positive likelihood ratios, which are more intuitively understood. Finally, raw scores from a variant predictor can be mapped onto posterior probabilities of pathogenicity or benignity. All mathematical details are provided in Pejaver et al.
Over the first half of 2025, we hope to make these mappings for all variant effect predictors easily available in the OpenCRAVAT store and to disseminate them on CIViC. You can find the following calibrated variant effect predictors in the OpenCRAVAT store as of 12/9/2024. The store is available both through run.opencravat.org and your local installation of OpenCRAVAT. More calibrated variant effect predictors will be coming soon!
| Predictor Name | Methodology | Citation |
| BayesDel | Meta-score to assess deleteriousness | Feng, Bing-Jian. “PERCH: A Unified Framework for Disease Gene Prioritization.” Human mutation vol. 38,3 (2017): 243-251. |
| CADD | convolutional neural network | Rentzsch P, Witten D, Cooper GM, Shendure J, Kircher M. CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Res.2018 Oct 29. |
| CADD exome | convolutional neural network | Rentzsch P, Witten D, Cooper GM, Shendure J, Kircher M. CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Res.2018 Oct 29. |
| FATHMM | Hidden Markov models | Shihab, H. A., Gough, J., Mort, M., Cooper, D. N., Day, I. N., & Gaunt, T. R. (2014). Ranking non-synonymous single nucleotide polymorphisms based on disease concepts. Human genomics, 8(1), 11. |
| GERP++ | Nucleotide conservation | Davydov, E. V., Goode, D. L., Sirota, M., Cooper, G. M., Sidow, A., & Batzoglou, S. (2010). Identifying a high fraction of the human genome to be under selective constraint using GERP++. PLoS computational biology, 6(12), |
| PHYLOP | Nucleotide conservation | Pollard, K. S., Hubisz, M. J., Rosenbloom, K. R., & Siepel, A. (2010). Detection of nonneutral substitution rates on mammalian phylogenies. Genome research, 20(1), 110-121. |
| REVEL | Meta-predictor, Random Forest | Ioannidis, N. M., Rothstein, J. H., Pejaver, V., et al. (2016). REVEL: an ensemble method for predicting the pathogenicity of rare missense variants. The American Journal of Human Genetics, 99(4), 877-885. |
| SIFT | Multiple sequence alignment | Vaser R, Adusumalli S, Leng SN, Sikic M, Ng PC (2016) SIFT missense predictions for genomes. Nat Protocols 11: 1-9. |
| VEST4 | Random forest | Carter H, Douville C, Stenson PD, Cooper DN, Karchin R. Identifying Mendelian disease genes with the variant effect scoring tool. BMC Genomics. 2013;14 Suppl 3(Suppl 3):S3. |
| PrimateAI | Deep residual neural network | Sundaram, L., Gao, H., Padigepati, S.R. et al. Predicting the clinical impact of human mutation with deep neural networks. Nat Genet 50, 1161–1170 (2018). |
When you run an annotation job with a calibrated variant effect predictor, you will see two new columns: ACMG/AMP Benign (BP4) and ACMG/AMP Pathogenic (PP3).
The mapping to clinical evidence strength may be different from the threshold for pathogenicity (or deleteriousness, damage etc) For example, in the results shown below, the PrimateAI raw score of 0.835 is above the recommended threshold for a damaging variant, but with respect to ACMG/AMP clinical Pathogenicity, the evidence that the variant is pathogenic is only “supporting” (vs. very strong, strong, or moderate).
BP4 and PP3 strength of evidence can also be visualized in the widget for each variant effect predictor, such as this one for REVEL.
Please let us know your thoughts and experience with this new feature at https://github.com/KarchinLab/open-cravat/discussions or by emailing us at support@opencravat.org
Efficient data filtering is critical to making sense of your analysis. OpenCRAVAT offers eight customizable filtering options to help you zero in on meaningful results:
- Filter by gene names to focus on specific targets.
- Narrow by variant type (e.g., missense or frameshift).
- Set thresholds for pathogenicity scores like REVEL or CADD.
- Use population frequency filters to flag rare variants.
- Exclude variants found in controls using dbSNP.
- Prioritize by conservation scores such as PhyloP.
- Explore gene-disease associations for relevance.
- Apply cross-database filters to ensure robust findings.
Take advantage of these tools to make your data more actionable.
👉 Learn more about filtering options
OpenCRAVAT is designed with flexibility in mind, offering users the ability to tailor their genomic variant analyses to specific research needs. Whether you’re working with rare disease variants, population studies, or cancer genomics, customization ensures that you focus on the most relevant data. Start by selecting specific annotations from OpenCRAVAT’s extensive module library, including tools like REVEL for pathogenicity predictions or COSMIC for cancer mutations. These modules allow you to target insights that matter most to your research goals.
Customizing your workflow goes beyond choosing modules. OpenCRAVAT empowers users with advanced filtering options to narrow results to specific genes, regions, or variant types. You can also convert your input formats effortlessly—whether you’re starting with VCF, HGVS, or dbSNP IDs—and export results in formats like TXT, Excel, or SQLite for downstream analysis. Additionally, OpenCRAVAT enables you to tailor visualizations, ensuring you present your findings clearly and effectively to collaborators or in publications.
For users looking to optimize performance or scale, OpenCRAVAT offers flexibility in execution. Analyses can be run locally for speed or on the cloud for large-scale datasets. Python-savvy users can even automate workflows with custom scripts, enhancing efficiency. Whether you’re exploring a single variant or working on a cohort study, OpenCRAVAT’s customizable features streamline your journey from data to discovery. Start exploring these tools today and elevate your analysis!
On the sixth day of OpenCRAVAT, we’re highlighting six essential tools that transform your genomic data into actionable insights. These tools are designed to help researchers evaluate the functional impact of genetic variants with precision and reliability.
1️⃣ CHASMplus: Pinpoint cancer driver mutations with this powerful tool, specifically designed to differentiate between drivers and passengers in tumor development. It’s an invaluable asset for cancer researchers. Learn more about CHASMplus here.
2️⃣ REVEL: This tool predicts the pathogenicity of rare missense variants by integrating multiple scores into a single, comprehensive metric. Perfect for clinical researchers and diagnosticians. Explore REVEL here.
3️⃣ PolyPhen-2: Assess the structural and functional impacts of amino acid changes caused by missense mutations. This tool helps you identify damaging mutations quickly and effectively. Discover PolyPhen-2.
4️⃣ gnomAD: Understand population-level variant frequencies with data from the Genome Aggregation Database. This resource is key for identifying rare variants. Visit gnomAD.
5️⃣ ClinVar: Access clinical classifications for your variants with links to supporting evidence. ClinVar is a cornerstone of clinical variant interpretation. Search ClinVar.
6️⃣ COSMIC: Investigate somatic mutations in cancer with data from the Catalogue of Somatic Mutations in Cancer. Explore COSMIC.
Each of these tools is seamlessly integrated into OpenCRAVAT, giving you access to cutting-edge resources without the need to juggle multiple platforms. Whether you’re researching cancer, rare diseases, or population genetics, these tools can provide critical insights.
Ready to explore these powerful tools in your own analysis?
👉 Start with OpenCRAVAT
It’s Day 5 of the 12 Days of OpenCRAVAT, and today we’re sharing five essential tips to help you make the most of your OpenCRAVAT experience. Whether you’re new to the platform or looking to streamline your workflow, these tips will set you on the path to success.
- Create an OpenCRAVAT account on the web: Registering an account allows you to save your projects, access advanced tools, and manage your analysis more effectively. Plus, it opens up additional features like cloud-based processing and result storage.
- Keep OpenCRAVAT up to date: Regular updates ensure you have the latest features, annotations, and improvements.
- Convert your input format: OpenCRAVAT supports various input formats such as VCF, HGVS, and dbSNP. Use the input converters to easily prepare your data for annotation, no matter what format you’re starting with.
But that’s not all! Two more tips are essential for efficient interpretation and analysis:
- Filter your results: OpenCRAVAT’s advanced filtering tools help you zero in on variants of interest, whether by pathogenicity, frequency, or other criteria. Tailor your output to ensure you’re only working with the most relevant data.
- Visualize your results: Don’t just analyze—visualize! With OpenCRAVAT’s interactive viewer, you can explore annotations in a dynamic format, making it easier to interpret patterns and identify meaningful insights.
By following these tips, you’ll unlock the full potential of OpenCRAVAT and simplify your variant analysis. Ready to dive in?
👉 Get started today:
🔗 Creating an OC account (web) https://docs.opencravat.org/en/latest/getting_started_web.html#creating-an-opencravat-account-web
🔗Convert your input file format https://docs.opencravat.org/en/latest/getting_started_web.html#convert-to-input-file-format
🔗Update Instructions https://docs.opencravat.org/en/latest/Update-Instructions.html#update-instructions
🔗Filter Your Results https://docs.opencravat.org/en/latest/getting_started_web.html#filter-results
🔗Visualize Your Results https://docs.opencravat.org/en/latest/getting_started_web.html#visualize-results
On the fourth day of OpenCRAVAT, we’re celebrating the power of trusted annotation sources! Annotation is at the heart of variant interpretation, and OpenCRAVAT integrates some of the most reliable and widely used databases to ensure your results are accurate and comprehensive.
Among the many sources available, four stand out for their unique contributions:
- gnomAD4 provides population frequency data, helping you distinguish between common and rare variants.
- dbSNP catalogs known single nucleotide polymorphisms and indels for quick reference.
- ClinVar offers clinical significance classifications, aiding in the identification of pathogenic variants.
- COSMIC focuses on cancer-related mutations, supporting oncological research and interpretation.
Together, these databases empower you to analyze variants with confidence and clarity. By leveraging the latest curated data, you can uncover insights that drive discovery and decision-making. Whether you’re focusing on rare genetic disorders, cancer genomics, or general research, OpenCRAVAT’s comprehensive annotation sources provide the foundation you need.
Explore these trusted sources today and see how OpenCRAVAT transforms your genomic data into meaningful insights!
👉 [Link to annotator list]
On the third day of OpenCRAVAT, we’re diving into how you can tailor your output with custom reports! OpenCRAVAT offers three flexible report formats: TXT, Excel, and SQLite. Whether you’re a data scientist integrating annotations into a pipeline or a researcher needing a sharable, easy-to-read summary, we’ve got you covered.
The TXT format provides a lightweight, simple structure perfect for quick reviews or scripting. If you prefer a tabular view for data exploration, the Excel format is ideal, offering clarity and compatibility with many tools. For those looking to perform advanced queries or store large datasets efficiently, the SQLite format enables powerful database functionality. These options ensure that OpenCRAVAT adapts to your workflow, not the other way around.
Customizing your reports ensures that you get the data you need, in the format you prefer, for seamless integration into your analysis process. Take advantage of these versatile outputs to streamline your variant annotation work and make your results presentation-ready.
👉 Learn more and try OpenCRAVAT reports documentation today –> Viewing Results and Downloadable Reports
On the second day of OpenCRAVAT, we’re introducing two new example input types: hgvs and dbsnp. These inputs are designed to help you explore OpenCRAVAT’s functionality without needing to prepare your own data. Whether you’re testing the platform for the first time or demonstrating its capabilities to colleagues, these pre-configured examples make it easy to see OpenCRAVAT in action.
With these example inputs, you can experience the power of OpenCRAVAT’s annotation capabilities instantly. Simply select an input type, run the analysis, and review the results. It’s a great way to get familiar with the platform and understand how OpenCRAVAT can fit into your workflow.
Try out the new example inputs today and see how OpenCRAVAT simplifies your analysis.
Getting Started with OpenCRAVAT
Welcome to the 12 Days of OpenCRAVAT! 🎄 Each day, we’ll explore a different feature or tool that makes OpenCRAVAT an essential platform for genomic variant analysis. Whether you’re a seasoned researcher or just beginning your journey, our adventurer’s guide will help you unlock the full potential of OpenCRAVAT.
On Day 1, we’re spotlighting the Single Variant Tool—a quick and powerful feature for annotating one variant at a time. Perfect for small-scale queries or rapid testing, this tool delivers fast, reliable results to help you understand the impact and context of a single variant. It’s intuitive, efficient, and now more accessible than ever through our updated website. Start your analysis today with the Single Variant Tool!
This tool is especially useful for those working on clinical or exploratory research, where rapid answers about variant significance can make a big difference. With just a few clicks, you can uncover detailed information about pathogenicity, population frequency, and more, all presented in an easy-to-read report.
Welcome to the world of OpenCRAVAT! Whether you’re a seasoned researcher or just dipping your toes into genomic variant analysis, there’s a place for you in our growing OpenCRAVAT community. By joining us, you’re not just using a tool—you’re becoming part of a dynamic, collaborative ecosystem that’s shaping the future of variant interpretation. In this post, we’ll dive into how you can contribute to OpenCRAVAT, collaborate with others, and connect with a community that’s passionate about accelerating discovery in genomics. Let’s explore what makes the OpenCRAVAT community special and how you can get involved!
Why Community Matters in Genomic Variant Analysis
The volume and complexity of genetic data require not only powerful tools but also a supportive community where researchers can exchange insights, troubleshoot challenges, and share resources. That’s what OpenCRAVAT aims to be—a collaborative space that bridges expertise and experience, all in the spirit of open science.
Our community members are at the heart of OpenCRAVAT’s success. They help make the platform more robust, versatile, and accessible to all, regardless of expertise level. And that’s why we’re always looking for ways to bring new voices, ideas, and talents to our ecosystem.
Ways to Get Involved: Contribute, Collaborate, and Connect
Here are some exciting ways you can join and actively contribute to OpenCRAVAT!
1. Contribute Your Skills and Ideas
There are countless ways to make a meaningful contribution, even if you’re not a coder! Here are a few paths to get started:
– Documentation and Tutorials: Clear, helpful documentation is key for users at all experience levels. By improving the OpenCRAVAT docs, creating tutorials, or sharing examples of your own analysis workflow, you can make the platform easier to use and more accessible to others.
– Suggest New Features: Have an idea for a feature that would streamline your work or add value to the community? We’re all ears! Submit your feature requests or ideas on our GitHub page or community forum, where our team and other users can discuss and prioritize them.
– Report Bugs and Enhance Stability: If you encounter a bug or issue, your feedback helps us fix it faster and improve the user experience. You can submit bug reports through GitHub, where our team and other contributors can track and address them.
2. Collaborate on Annotators and Packages
One of OpenCRAVAT’s most powerful features is its annotators, which allows users to extend the platform’s functionality. Annotators include a database of annotations for fast high-throughput analysis of large variant files. Annotators can add new data sources, analysis capabilities or custom workflows tailored to specific needs. Whether you’re an experienced developer or just learning the ropes, creating or contributing to annotators is a fantastic way to give back to the community.
– Become a developer: Want to create an annotator for your favorite data source or analysis tool? Our annotator developer tutorial has everything you need to get started.
– Showcase Your Work: Created an annotator you’re proud of? Share it with the community! Each month, we feature an “Annotator of the Month” created by community members, giving you the chance to showcase your work and inspire others.
3. Connect and Learn Through Our Community Forum
The OpenCRAVAT community forum is your go-to place to connect with other users, get help, and share insights. It’s a friendly, welcoming space where everyone from beginners to experts can participate. You’ll find discussions on everything from troubleshooting and feature requests to case studies and success stories.
– Ask Questions and Share Solutions: Have a question or run into a challenge? Post it in the forum! Not only will you likely find a solution, but you may also help others who encounter the same issue.
– Attend Webinars and Community Calls: We host quarterly webinars to showcase new features, discuss updates, and answer community questions. Keep an eye on our announcements to stay up-to-date and join in!
– Research Spotlights: Interested in seeing how others use OpenCRAVAT? We often feature community research projects, case studies, and success stories, offering valuable insights into practical applications and inspiring ideas for your own work.
Recognition of Contributions
Your contributions won’t go unnoticed. We believe in celebrating our community members’ hard work, whether you’re improving documentation, creating plugins, or helping others in the forum. Each month, we feature outstanding contributors in our “Contributor Spotlight” to recognize the valuable role they play in OpenCRAVAT’s growth and success.
By contributing to OpenCRAVAT, you’re not only advancing your own research but also helping to build a stronger, more capable platform for everyone. And who knows—you might even discover a new collaborator along the way!
How to Get Started
Ready to jump in? Here’s how to take your first steps as a member of the OpenCRAVAT community:
- Join the Forum: Head over to our community forum and introduce yourself! Let us know about your research and how you use OpenCRAVAT. https://github.com/KarchinLab/open-cravat/discussions
- Explore Contribution Opportunities: Check out our GitHub page for current issues, feature requests, and annotator ideas.
- Join a Webinar: Stay tuned for announcements on upcoming events where you can learn, contribute, and connect with others.
The OpenCRAVAT community is more than just users—it’s a collective of researchers, bioinformaticians, developers, and enthusiasts who are passionate about advancing variant interpretation and making it accessible to all. Whether you’re contributing code, ideas, documentation, or simply sharing your experience, your involvement strengthens the platform and benefits everyone who uses it.
So, what are you waiting for? Join us, and let’s build something amazing together!