This collection published on July 3rd, 2023 in Genome Biology, titled "Towards an atlas of variant effects” highlights the power of approaches of Multiplex assays of variant effect (MAVEs) as well as computational tools for understanding gene function, disease variants and biology.

The stage for this collection of articles is set by a short correspondence piece from the Atlas of Variant Effects (AVE) Alliance. In the perspective, we outline our vision and specific approach for creating a comprehensive Atlas, which would characterize the function of every possible single nucleotide change in most genes in the human genome.

Articles in the collection include:

• An Atlas of Variant Effects to understand the genome at nucleotide resolution (correspondence)

• Benchmarking computational variant effect predictors by their ability to infer human traits

• Minimum information and guidelines for reporting a multiplexed assay of variant effect

• Characterizing glucokinase variant mechanisms using a multiplexed abundance assay

• Benchmarking splice variant prediction algorithms using massively parallel splicing assays

• Cross-protein transfer learning substantially improves disease variant prediction

• mutscan—a flexible R package for efficient end-to-end analysis of multiplexed assays of variant effect data

• High-throughput deep learning variant effect prediction with Sequence UNET

• A comprehensive map of human glucokinase variant activity

• satmut_utils: a simulation and variant calling package for multiplexed assays of variant effect

• DIMPLE: deep insertion, deletion, and missense mutation libraries for exploring protein variation in evolution, disease, and biology

• Leveraging massively parallel reporter assays for evolutionary questions

• Saturation-scale functional evidence supports clinical variant interpretation in Lynch syndrome

• MAVE-NN: learning genotype-phenotype maps from multiplex assays of variant effect