ArticleNature biotechnology2018
A universal SNP and small-indel variant caller using deep neural networks.
Article in Nature biotechnology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1,021 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
1,021 citing papers in PubMed.
- Development of Single-Nucleotide Polymorphism (SNP) Markers in Sesame (Sesamum indicum L.).Methods in molecular biology (Clifton, N.J.) · 2027Article
- Telomere-to-telomere CHM13 reference reveals missing truth variants and improves deep learning-based variant calling in long-read sequencing data.Quantitative biology (Beijing, China) · 2026Article
- Haplotype-resolved genome of a dry-preservable cell line reveals chromosome evolution and DNA damage.Life science alliance · 2026Article
- Small variant calling in the era of long-read sequencing.Nature reviews. Genetics · 2026Review
- Association between human genomes and mycobacterial genomes contributes to severity and transmissibility of pulmonary tuberculosis.iScience · 2026Article
- Message in a bottleneck: Nested founder effects from French Polynesia to Rapa Nui and Hawai'i.Science (New York, N.Y.) · 2026Article
- Review
- Pileup-hi: an ultra-high-throughput, customizable alignment pileup program for large datasets.Bioinformatics (Oxford, England) · 2026Article
- Chromosome-level genome of wild-simulated Panax ginseng identifies SNP markers for germplasm and medicinal quality evaluation.Journal of advanced research · 2026Article
- Germline homologous recombination deficiency influences TP53-mutant clonal hematopoiesis fitness during platinum and PARP inhibitor treatment.Nature genetics · 2026Article
- An Icelandic pangenome reference.Nature · 2026Article
- Structural variant calling using Sniffles2.Nature protocols · 2026Review
- Diagnostic yield of cancer predisposition in a nationwide prospective childhood acute leukemia cohort.Nature communications · 2026Article
- Patient-governed agentic health data exchange.npj health systems · 2026Review
- The rate and spectrum of germline mutations in chicken from a commercial pedigree line.Genetics, selection, evolution : GSE · 2026Article
- Artificial Intelligence in Pediatric Cardiovascular Genetics: From Multimodal Diagnosis to Risk Prediction and Precision Therapeutics.International journal of molecular sciences · 2026Review
- Himito: a graph-based toolkit for mitochondrial genome analysis using long reads.Nature communications · 2026Article
- COSIGT: population-scalable genotyping of complex loci from low-coverage sequencing data using pangenome graphs.Genome biology · 2026Article
- Genetic Heterogeneity of Inborn Errors of Immunity Revealed by Whole-Genome Sequencing: Insights from a Russian Patient Cohort.International journal of molecular sciences · 2026Article
- Cost-efficient long-read trio-barcoded adaptive sequencing improves rare disease diagnosis.Nature communications · 2026Article
961 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite rapid advances in sequencing technologies, accurately calling genetic variants present in an individual genome from billions of short, errorful sequence reads remains challenging. Here we show that a deep convolutional neural network can call genetic variation in aligned next-generation sequencing read data by learning statistical relationships between images of read pileups around putative variant and true genotype calls. The approach, called DeepVariant, outperforms existing state-of-the-art tools. The learned model generalizes across genome builds and mammalian species, allowing nonhuman sequencing projects to benefit from the wealth of human ground-truth data. We further show that DeepVariant can learn to call variants in a variety of sequencing technologies and experimental designs, including deep whole genomes from 10X Genomics and Ion Ampliseq exomes, highlighting the benefits of using more automated and generalizable techniques for variant calling.
Indexed as
Identifiers
30247488What 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.