ReviewNature genetics2026
Interpreting human genetic variation at atomic resolution.
Review in Nature genetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
The widespread adoption of clinical sequencing and large-scale national and international genomics initiatives has transformed genomic medicine. These efforts have enabled new diagnostic tests and computational tools for processing sequencing-derived information, yet interpretation of many variants remains limited. In parallel, over the last two decades, computational structural genomics (CSG) has emerged as a complementary approach that shifts from static genetic annotation to dynamic, mechanistic interpretation by leveraging models of gene products and their organization within biomolecular complexes of protein, DNA, RNA and small molecules. In this Perspective, we highlight how CSG has resulted in a comprehensive and mechanistic understanding of how interindividual genetic variants function, by integrating sequence information, motifs, domains, molecular structures, molecular dynamics simulations, multi-omics data annotations and artificial intelligence-driven tools. These advanced technologies enable prediction of variant effects at atomic resolution and support simultaneous classification according to their mechanisms of dysfunction.
Identifiers
42768124What Socratic 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 Socratic graph.