ArticleBioinformatics advances2025
AlphaMissenseR: an integrated framework for investigating missense mutations in human protein-coding genes.
Article in Bioinformatics advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Technological Advances in Molecular Diagnostic Methods for Hereditary Diseases in Preconception and Prenatal Settings.Current issues in molecular biology · 2026Review
- First Exonic Cryptic Branchpoint Variant in an Inherited Retinal Degeneration Detected in an IrishGenes · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Summary: AlphaMissense is an AI model from Google DeepMind that predicts the pathogenicity of every possible missense mutation in the human proteome. We present AlphaMissenseR, an R/Bioconductor package that facilitates performant and reproducible access to these predictions and that provides functionality for analysis, visualization, validation, and benchmarking. AlphaMissenseR integrates with Bioconductor facilities for genomic region analysis, and provides multi-level visualization and interactive exploration of variant pathogenicity in a genome browser and on 3D protein structures. In addition, AlphaMissenseR integrates with major clinical and experimental variant databases for contrasting predicted and clinically derived pathogenicity scores, and for systematic benchmarking of existing and new variant effect prediction methods across a large collection of deep mutational scanning assays. Availability and implementation: AlphaMissense data resources are distributed under the CC-BY 4.0 license and the AlphaMissenseR package is available from Bioconductor (https://bioconductor.org/packages/AlphaMissenseR) under the Artistic 2.0 license.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.