Evaluation of haplotype-aware long-read error correction with hifieval

Repeat and haplotype aware error correction in nanopore sequencing reads with DeChat

Error self-correction is crucial for analyzing long-read sequencing data, but existing methods often struggle with noisy data or are tailored to technologies like PacBio HiFi. There is a gap in methods optimized for Nanopore R10 simplex reads, which typically have error rates below 2%. We introduce DeChat, a novel approach designed specifically for these reads. DeChat enables repeat- and haplotype-aware error correction, leveraging the strengths of both de Bruijn graphs and variant-aware multiple sequence alignment to create a synergistic approach. This approach avoids read overcorrection, ensuring that variants in repeats and haplotypes are preserved while sequencing errors are accurately corrected. Benchmarking on simulated and real datasets shows that DeChat-corrected reads have significantly fewer errors-up to two orders of magnitude lower-compared to other methods, without losing read information. Furthermore, DeChat-corrected reads clearly improves genome assembly and taxonomic classification.

Genome assembly in the telomere-to-telomere era

Genome sequences largely determine the biology and encode the history of an organism, and de novo assembly - the process of reconstructing the genome sequence of an organism from sequencing reads - has been a central problem in bioinformatics for four decades. Until recently, genomes were typically assembled into fragments of a few megabases at best, but now technological advances in long-read sequencing enable the near-complete assembly of each chromosome - also known as telomere-to-telomere assembly - for many organisms. Here, we review recent progress on assembly algorithms and protocols, with a focus on how to derive near-telomere-to-telomere assemblies. We also discuss the additional developments that will be required to resolve remaining assembly gaps and to assemble non-diploid genomes.