ReviewNature reviews. Genetics2026
Small variant calling in the era of long-read sequencing.
Review in Nature reviews. 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Long-read sequencing enables accurate analysis of complex and repetitive genomic regions but has distinct error profiles compared to short-read sequencing. Consequently, the tools for calling small variants - the predominant source of human genetic diversity - have shifted towards deep learning-based methods. This transition has reshaped variant calling across biological contexts, including germline, de novo, somatic and mosaic variants, and sequencing modalities for capturing both DNA and RNA at different resolutions. These deep learning frameworks are being developed in combination with alignment-based, assembly-based and pangenome-based strategies for small variant detection. In this Review, we chart the trajectory towards complete telomere-to-telomere profiling of small variants and consider the computational innovations needed to benchmark newly accessible repetitive regions and complex variants.
Identifiers
42902030What 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.