Sequence graphs have emerged as an important tool in two distinct areas of computational genomics: genome assembly and pangenomics. However, despite this shared basis, subtly different graph formalisms have hindered the flow of methodological advances from pangenomics into genome assembly. In genome assembly, edges typically indicate overlaps between sequences, with the overlapping sequence expressed redundantly on both nodes. In pangenomics, edges indicate adjacency between sequences with no overlap—often called blunt adjacencies. Algorithms and software developed for blunt sequence graphs often do not generalize to overlapped sequence graphs. This effectively silos pangenomics methods that could otherwise benefit genome assembly. In this paper, we attempt to dismantle this silo. We have developed an algorithm that transforms an overlapped sequence graph into a blunt sequence graph that preserves walks from the original graph. Moreover, the algorithm accomplishes this while also eliminating most of the redundant representation of sequence in the overlap graph. The algorithm is available as a software tool, GetBlunted, which uses little enough time and memory to virtually guarantee that it will not be a bottleneck in any genome assembly pipeline.

Reference-free genome phasing is vital for understanding allele inheritance and the impact of single-molecule DNA variation on phenotypes. To achieve thorough phasing across homozygous or repetitive regions of the genome, long-read sequencing technologies are often used to perform phased de novo assembly. As a step toward reducing the cost and complexity of this type of analysis, we describe new methods for accurately phasing Oxford Nanopore Technologies (ONT) sequence data with the Shasta genome assembler and a modular tool for extending phasing to the chromosome scale called GFAse. We test using new variants of ONT PromethION sequencing, including those using proximity ligation, and show that newer, higher accuracy ONT reads substantially improve assembly quality.

Compared to its predecessors, the Telomere-to-Telomere CHM13 genome adds nearly 200 million base pairs of sequence, corrects thousands of structural errors, and unlocks the most complex regions of the human genome for clinical and functional study. We show how this reference universally improves read mapping and variant calling for 3202 and 17 globally diverse samples sequenced with short and long reads, respectively. We identify hundreds of thousands of variants per sample in previously unresolved regions, showcasing the promise of the T2T-CHM13 reference for evolutionary and biomedical discovery. Simultaneously, this reference eliminates tens of thousands of spurious variants per sample, including reduction of false positives in 269 medically relevant genes by up to a factor of 12. Because of these improvements in variant discovery coupled with population and functional genomic resources, T2T-CHM13 is positioned to replace GRCh38 as the prevailing reference for human genetics.

Long-read sequencing technologies substantially overcome the limitations of short-reads but have not been considered as a feasible replacement for population-scale projects, being a combination of too expensive, not scalable enough or too error-prone. Here we develop an efficient and scalable wet lab and computational protocol, Napu, for Oxford Nanopore Technologies long-read sequencing that seeks to address those limitations. We applied our protocol to cell lines and brain tissue samples as part of a pilot project for the National Institutes of Health Center for Alzheimer's and Related Dementias. Using a single PromethION flow cell, we can detect single nucleotide polymorphisms with F1-score comparable to Illumina short-read sequencing. Small indel calling remains difficult within homopolymers and tandem repeats, but achieves good concordance to Illumina indel calls elsewhere. Further, we can discover structural variants with F1-score on par with state-of-the-art de novo assembly methods. Our protocol phases small and structural variants at megabase scales and produces highly accurate, haplotype-specific methylation calls.