  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.
