Helix Pathogenicity
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About
Helix Pathogenicity offers state-of-the-art pathogenicity predictions for clinically relevant genes. It outperforms competitors and provides insight with high-quality 3DM data, augmented by Bio-Prodict 3DM. Users gain access to structural, alignment, literature, and gene data. Transparent decision-making is facilitated through detailed variant reports, including certainty estimates and in-depth feature statistics. Helix is designed for solid integration into existing workflows.
Platform
Task
Features
• alignment data
• structural data
• available for all clinically relevant genes
• solid integration
• transparent decision making with helix pathogenicity reports
• insight with high quality 3dm data
• best in class pathogenicity predictions
• state of the art pathogenicity predictions
FAQs
Who can use Helix?
Helix has many applications, but current users are those in the clinical, pharmacological and health sectors. Both commercial and academic actors make use of Helix.
What is Helix used for?
Helix can be used for human missense variant analysis for clinical diagnostics, disease association, and patient stratification. Helix provides in depth missense variant reports as well as structured data.
How do I access Helix data?
Helix data can be accessed using our website, our API, or under special circumstances we can supply you with flat file access to all SNPs in the Helix database.
Can I test Helix?
Please [contact us](/contact) if you would like to test Helix on your data. We can assess Helix on your dataset in a secure environment and work with you to assess the results.
Does Helix provide predictions for InDels, CNVs or non-coding variants?
Currently Helix only predicts pathogenicity for missense variants only, and the web application, API and flat file only include those. We are currently integrating methods that allow for indel prediction.
What is the best cutoff to use?
Helix scores are presented from 0 to 1, or from 0 to 100%. Helix is optimized for a cutoff of 0.5 (50%), with below 0.5 indicating a benign prediction and above 0.5 indicating a pathogenic prediction.
Why do you use MCC in your analyses instead of ROC AUC?
MCC (Matthew's Correlation Coefficient) is a metric that measures the quality of binary predictions. Pathogenicity datasets have binary labels (pathogenic or benign).
How does Helix compare to a predictor like REVEL?
Helix is available for every variant in the Human exome. Helix provides in-depth variant reports with access to literature for every variant.
Are Helix predictions available for other organisms?
Yes, please [get in touch](/contact) to discuss options.
Is the data that Helix predictions are built on available as a separate resource that can be used to develop other applications or integrate with in-house pipelines?
Yes, please [get in touch](/contact) to discuss options.
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