ArticleGenome research2020
Detection of simple and complex de novo mutations with multiple reference sequences.
Article in Genome research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Germline de novo mutation rate of the highly heterozygous amphioxus genome.Molecular biology and evolution · 2026Article
- BPIFB4 rs4339026 A > G polymorphism impacts COPD susceptibility in the Kashi population, China.Scientific reports · 2025Article
- The Great Genotyper: a graph-based method for population genotyping of small and structural variants.GigaScience · 2025Article
- RecGraph: recombination-aware alignment of sequences to variation graphs.Bioinformatics (Oxford, England) · 2024Article
- Role for gene conversion in the evolution of cell-surface antigens of the malaria parasite Plasmodium falciparum.PLoS biology · 2024Article
- Pedigree-based and phylogenetic methods support surprising patterns of mutation rate and spectrum in the gray mouse lemur.Heredity · 2021Article
- Article
- Whole-Exome Sequencing Implicates theFrontiers in cell and developmental biology · 2021Article
- Evidence for linkage of pfmdr1, pfcrt, and pfk13 polymorphisms to lumefantrine and mefloquine susceptibilities in a Plasmodium falciparum cross.International journal for parasitology. Drugs and drug resistance · 2020Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
The characterization of de novo mutations in regions of high sequence and structural diversity from whole-genome sequencing data remains highly challenging. Complex structural variants tend to arise in regions of high repetitiveness and low complexity, challenging both de novo assembly, in which short reads do not capture the long-range context required for resolution, and mapping approaches, in which improper alignment of reads to a reference genome that is highly diverged from that of the sample can lead to false or partial calls. Long-read technologies can potentially solve such problems but are currently unfeasible to use at scale. Here we present Corticall, a graph-based method that combines the advantages of multiple technologies and prior data sources to detect arbitrary classes of genetic variant. We construct multisample, colored de Bruijn graphs from short-read data for all samples, align long-read-derived haplotypes and multiple reference data sources to restore graph connectivity information, and call variants using graph path-finding algorithms and a model for simultaneous alignment and recombination. We validate and evaluate the approach using extensive simulations and use it to characterize the rate and spectrum of de novo mutation events in 119 progeny from four
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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.