Telomere-to-telomere phased assemblies are emerging as a benchmark for reference-quality genomes1,2, although they remain technically and financially demanding, particularly at scale. Generating such assemblies for diploid and polyploid genomes typically involves combining high-accuracy long reads, such as PacBio HiFi3 or the now-deprecated Oxford Nanopore Technologies (ONT) Duplex4 reads, with ultra-long ONT Simplex reads. Using multiple platforms or methods increases the cost and the required amount of genomic DNA. Here we show that comparable results are possible using error correction of ultra-long Simplex reads and then assembling them using state-of-the-art de novo assembly methods. To achieve this, we developed the deep learning-based HERRO (haplotype-aware error correction) framework, which corrects Simplex reads while carefully preserving differences in related genomic sequences. Taking into account informative positions that differentiate the haplotypes or genomic repeat copies, HERRO achieves an increase of read accuracy of up to 100-fold for diploid human genomes. By combining HERRO with the Verkko2 assembler, we reconstruct up to 32 chromosomes telomere-to-telomere, including chromosomes X and Y, and consistently achieve NGA50 (normalized genome assembly 50) values of 100 Mb or higher across several human genomes. HERRO supports both R9.4.1 and R10.4.1 Simplex reads and generalizes well to other species. These results show that error-corrected ONT reads can lower sequencing costs and improve the quality of genomic analyses.
