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On the 11th day of OpenCRAVAT, we’re diving into a standout feature: widgets. Widgets transform data into interactive visualizations and tables, giving you the flexibility to explore your results like never before.

Why Widgets Matter

Widgets provide a dynamic, user-friendly way to interpret variant annotation data. Whether you’re a clinician seeking a clear view of pathogenicity predictions or a researcher investigating population allele frequencies, widgets can adapt to suit your needs.

Widgets in Action

• Interactive Graphs: Visualize trends and patterns across datasets with bar charts, scatter plots, and more. These graphs are perfect for high-level overviews and identifying outliers.
• Detailed Tables: Dive deep into individual annotations with sortable and filterable columns. Tables make it easy to focus on the specific information you need for reporting or further analysis.
• Specialized Widgets: Use tools tailored for specific data types, like conservation scores, protein domain impacts, or ACMG/AMP strength mappings for predictors.

Customize Your Analysis

The real power of widgets lies in customization. In OpenCRAVAT’s interface, you can:

1. Select Your Widgets: Choose only the widgets you need for your analysis, decluttering your workspace.
2. Reorganize: Rearrange widgets for a layout that suits your workflow.
3. Save Preferences: Save configurations to streamline future analyses.

Have ideas for a widget? Join our community discussions to share your thoughts!

Documentation: Installing Widgets &  Widget Tutorial

As we approach the end of our 12 Days of OpenCRAVAT, let’s dive into how the OpenCRAVAT API can revolutionize your genomic workflows. The API offers a programmatic way to submit jobs, monitor progress, and generate reports—ideal for users looking to automate variant analysis.

With the API, you can:

• Submit input files directly for annotation.
• Monitor job statuses in real-time to stay updated.
• Request reports in various formats (e.g., TXT, Excel, VCF).
• Perform single-variant annotations instantly.

Whether you’re integrating OpenCRAVAT into a pipeline or scaling your research with automation, the API opens a world of possibilities.

Learn how to get started and explore example scripts:
👉 API Documentation

Cloud computing has transformed genomic variant analysis by enabling scalable and accessible workflows. With OpenCRAVAT, you can run your analyses seamlessly on the cloud, eliminating hardware constraints and making complex computations faster and more efficient. Whether you’re processing a single variant or large-scale datasets, the cloud-based option ensures flexibility and reliability.

Running OpenCRAVAT on the cloud offers significant advantages. First, you save local resources—no need to upgrade your computer’s hardware to handle intensive analyses. Second, cloud environments are optimized for scalability, allowing you to process datasets with thousands of variants efficiently. Finally, cloud platforms support team collaboration, making it easier to share data, annotations, and results with colleagues across the globe.

Getting started is simple thanks to our collaborators at Microsoft Genomics. Set up OpenCRAVAT on Microsoft Azure, and upload your input data. You can leverage cloud-based storage for large files and take advantage of parallel processing to speed up your workflow. Explore how the cloud can elevate your genomic research today!

👉 Learn more about running OpenCRAVAT on the cloud

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!

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:

1. Filter by gene names to focus on specific targets.
2. Narrow by variant type (e.g., missense or frameshift).
3. Set thresholds for pathogenicity scores like REVEL or CADD.
4. Use population frequency filters to flag rare variants.
5. Exclude variants found in controls using dbSNP.
6. Prioritize by conservation scores such as PhyloP.
7. Explore gene-disease associations for relevance.
8. Apply cross-database filters to ensure robust findings.

Take advantage of these tools to make your data more actionable.
👉 Learn more about filtering options

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.

1. 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.
2. Keep OpenCRAVAT up to date: Regular updates ensure you have the latest features, annotations, and improvements.
3. 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:

4. 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.
5. 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.

 HGVS reference

Getting Started with OpenCRAVAT