ArticleScientific reports2023
GeneToCN: an alignment-free method for gene copy number estimation directly from next-generation sequencing reads.
Article in Scientific reports, 2023. 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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9 citing papers in PubMed, 10 citations in OpenAlex.
- Nanopore sequencing reveals coordinated host and microbiome responses across barley genotypes.BMC biology · 2026Article
- gAIRR-wgs: high-resolution T cell receptor allele typing in biobank-scale whole-genome sequencing data.Briefings in bioinformatics · 2026Article
- Rapid adaptive increase of amylase gene copy number in Indigenous Andeans.Nature communications · 2026Article
- ntStat: k-mer characterization using occurrence statistics in raw sequencing data.PLoS computational biology · 2026Article
- Y-mer: a k-mer based method for determining human Y chromosome haplogroups from ultra-low sequencing depth data.Genome biology · 2025Article
- Population-level gene copy number variations reveal distinct genetic properties of different Malus species.BMC genomics · 2025Article
- Direct long-read visualization reveals hidden variation in GCH1 gene copy number and precise expansion steps.BMC genomics · 2025Article
- Reconstruction of the human amylase locus reveals ancient duplications seeding modern-day variation.Science (New York, N.Y.) · 2024Article
- Early Detection of BothInternational journal of molecular sciences · 2024Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genomes exhibit large regions with segmental copy number variation, many of which include entire genes and are multiallelic. We have developed a computational method GeneToCN that counts the frequencies of gene-specific k-mers in FASTQ files and uses this information to infer copy number of the gene. We validated the copy number predictions for amylase genes (AMY1, AMY2A, AMY2B) using experimental data from digital droplet PCR (ddPCR) on 39 individuals and observed a strong correlation (R = 0.99) between GeneToCN predictions and experimentally determined copy numbers. An additional validation on FCGR3 genes showed a higher concordance for FCGR3A compared to two other methods, but reduced accuracy for FCGR3B. We further tested the method on three different genomic regions (SMN, NPY4R, and LPA Kringle IV-2 domain). Predicted copy number distributions of these genes in a set of 500 individuals from the Estonian Biobank were in good agreement with the previously published studies. In addition, we investigated the possibility to use GeneToCN on sequencing data generated by different technologies by comparing copy number predictions from Illumina, PacBio, and Oxford Nanopore data of the same sample. Despite the differences in variability of k-mer frequencies, all three sequencing technologies give similar predictions with GeneToCN.
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