Guidelines for releasing a variant effect predictor (2404.10807v1)

Abstract: Computational methods for assessing the likely impacts of mutations, known as variant effect predictors (VEPs), are widely used in the assessment and interpretation of human genetic variation, as well as in other applications like protein engineering. Many different VEPs have been released to date, and there is tremendous variability in their underlying algorithms and outputs, and in the ways in which the methodologies and predictions are shared. This leads to considerable challenges for end users in knowing which VEPs to use and how to use them. Here, to address these issues, we provide guidelines and recommendations for the release of novel VEPs. Emphasising open-source availability, transparent methodologies, clear variant effect score interpretations, standardised scales, accessible predictions, and rigorous training data disclosure, we aim to improve the usability and interpretability of VEPs, and promote their integration into analysis and evaluation pipelines. We also provide a large, categorised list of currently available VEPs, aiming to facilitate the discovery and encourage the usage of novel methods within the scientific community.

• The paper emphasizes open-source development and transparency to ensure reproducibility and credibility in genetic variant assessments.
• It recommends a standardized scoring system and detailed training data disclosure to mitigate biases and enhance interpretability.
• The guidelines aim to harmonize VEP dissemination, improving accessibility and integration into genomic research and clinical practice.

Assessment of Variants through Variant Effect Predictors: Recommendations for Development and Dissemination

The paper "Guidelines for releasing a variant effect predictor" offers an expansive examination of the development, dissemination, and assessment practices associated with Variant Effect Predictors (VEPs). These computational tools are essential for determining the impacts of genetic variants, especially in clinical genetics, evolutionary biology, and protein engineering.

Overview and Challenges

This work acknowledges the proliferation of VEPs, each with diverse algorithms, outputs, and dissemination practices. Such heterogeneity presents considerable challenges for users in selecting and effectively employing appropriate VEPs. Moreover, the authors highlight the prevalent problem of biased performance comparisons, often resulting from an overestimation of the superiority of newly released VEPs. To mitigate these challenges, there has been a concerted push toward the independent benchmarking of VEPs.

Recommendations for VEP Development

The paper provides a comprehensive set of guidelines to standardize and enhance the usability and credibility of VEPs:

• Open-source and Transparency: It is emphasized that VEP methodologies should be open-source, providing the scientific community with access to code, detailed documentation, and reproducibility standards. Making models and results openly accessible encourages innovation, collaborative improvement, and reliability in clinical genetic applications.
• Score Interpretation: The paper discusses the interpretation of VEP scores, advocating for standardization across predictors to avoid interpretive confusion. The authors propose a consistent scale from zero (least damaging) to one (most damaging) for VEP outputs, aligning with most current methodologies.
• Accessibility and Availability: The authors stress the importance of freely available variant effect scores, in line with the FAIR Guiding Principles (Findable, Accessible, Interoperable, and Reusable), to enhance the integration of VEPs into broader genomic research and clinical practice.
• Training Data Disclosure: The importance of disclosing training datasets is highlighted to prevent circularity—a scenario where evaluations overestimate performance due to overlap with the training or related datasets. Recommendations include sharing detailed lists of training data and excluding certain genes from evaluation sets to reduce bias.

Practical and Theoretical Implications

The recommendations provided are aimed at standardizing and enhancing the integrity of VEPs, which have far-reaching applications, ranging from predicting human genetic pathogenicity to informing evolutionary studies. These guidelines promote the credibility and applicability of VEPs in genomic research and personalized medicine, ensuring that they contribute reliably to genetic diagnosis and inform treatment.

Future Directions

Moving forward, the harmonization of VEPs will facilitate their integration into sophisticated genomic research frameworks and clinical decision-making processes. Continued efforts to adopt and refine these guidelines can significantly impact personalized medicine and genetic research fields, supporting the development of more accurate, interpretable, and reliable computational predictions.

In conclusion, the paper delineates crucial strategies for the development and distribution of VEPs, emphasizing transparency, accessibility, and methodological rigor. These practices are instrumental in fostering trust and innovation within the scientific community, advancing the utility and credibility of VEPs in understanding and interpreting genetic variations.