ArticleNucleic acids research2024
A deep learning-based method enables the automatic and accurate assembly of chromosome-level genomes.
Article in Nucleic acids research, 2024. 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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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.
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Who cites it
9 citing papers in PubMed.
- Evolution and domestication-trait associations of ultra-long centromere haplotypes in pepper plants.Nature ecology & evolution · 2026Article
- HiCPotts: an R/bioconductor package to identify significant interactions in chromosome conformation capture data and model sources of bias.Bioinformatics (Oxford, England) · 2026Article
- Chromosome-level assembly of spotted steed (Hemibarbus maculatus Bleeker, 1871) genome.Scientific data · 2026Article
- PHScaffolding: a hypergraph clustering and dual-weight integration strategy for scaffolding with Pore-C reads.Briefings in bioinformatics · 2026Article
- Chromosome-level genome assembly of Agaricogonopus acrotrifoliolatus (Diplopoda: Spirostreptida).Scientific data · 2025Article
- Better together: Subgenomes for allotetraploid potato wild relative Solanum acaule Bitt. reveal origins in Petota Clade 3 and 4.The plant genome · 2025Article
- Review
- A chromosome-level genome assembly of the polyembryonic endoparasitoid Macrocentrus cingulum.Scientific data · 2025Article
- Chromosome-level genome assembly and annotation of the White-spotted spinefoot Siganus canaliculatus.Scientific data · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
The application of high-throughput chromosome conformation capture (Hi-C) technology enables the construction of chromosome-level assemblies. However, the correction of errors and the anchoring of sequences to chromosomes in the assembly remain significant challenges. In this study, we developed a deep learning-based method, AutoHiC, to address the challenges in chromosome-level genome assembly by enhancing contiguity and accuracy. Conventional Hi-C-aided scaffolding often requires manual refinement, but AutoHiC instead utilizes Hi-C data for automated workflows and iterative error correction. When trained on data from 300+ species, AutoHiC demonstrated a robust average error detection accuracy exceeding 90%. The benchmarking results confirmed its significant impact on genome contiguity and error correction. The innovative approach and comprehensive results of AutoHiC constitute a breakthrough in automated error detection, promising more accurate genome assemblies for advancing genomics research.
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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.