{"paper_id":"03b1fb98-e82d-4a51-b376-c05a6e79e4d3","body_text":"Adaptively integrated sequencing and assembly of near-complete genomes \n \nHasindu Gamaarachchi1,2 *, Igor Stevanovski1 *, Jillian M. Hammond1, Andre L. M. Reis1,3, Melissa Rapadas1,  \nKavindu Jayasooriya1,2, Tonia Russell1, Dennis Yeow1,4-7, Yvonne Hort1, Andrew J. Mallett8,9,10, Elaine \nStackpoole11, Lauren Roman12,13, Luke W. Silver14,15, Carolyn J. Hogg14,15, Lou Streeting16, Ozren Bogdanovic17,18, \nRenata Coelho Rodrigues Noronha19, Luís Adriano Santos do Nascimento19, Adauto Lima Cardoso18-20, Arthur \nGeorges21, Haoyu Cheng22, Hardip R. Patel23, Kishore Raj Kumar1,3,4,5, Amali C. Mallawaarachchi1,3,24, Ira W. \nDeveson1,3 # \n \n1. Genomics and Inherited Disease Program, Garvan Institute of Medical Research, Sydney, NSW, Australia \n2. School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia \n3. Faculty of Medicine and Health, St Vincent's Healthcare Clinical Campus, University of New South Wales, Darlinghurst, \nNSW, Australia \n4. Molecular Medicine Laboratory and Neurology Department, Concord Repatriation General Hospital, Concord, NSW, \nAustralia \n5. Faculty of Medicine and Health, University of Sydney, Camperdown, Australia. \n6. Neurodegenerative Service, Prince of Wales Hospital, Randwick, Australia \n7. Neuroscience Research Australia, Randwick, Australia \n8. College of Medicine and Dentistry, James Cook University, Townsville, Australia \n9. Department of Renal Medicine, Townsville University Hospital, Townsville, Australia \n10. Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia \n11. Genetic Health Western Australia, King Edward Memorial Hospital, Perth, Australia \n12. Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Australia \n13. CSIRO Environment, Hobart, Australia \n14. School of Life and Environmental Sciences, The University of Sydney, Sydney, Australia \n15. Australian Research Council Centre of Excellence for Innovations in Peptide and Protein Science, University of Sydney, \nSydney, Australia \n16. School of Environmental and Rural Science, University of New England, Armidale, Australia \n17. School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, Australia \n18. Centro Andaluz de Biología del Desarrollo, CSIC-Universidad Pablo de Olavide-Junta de Andalucía, Seville, Spain \n19. Laboratório de Genética e Biologia Celular, Centro de Estudos Avançados da Biodiversidade, Instituto de Ciências \nBiológicas, Universidade Federal do Pará, Belém, Brazil \n20. Instituto de Biociências de Botucatu, Universidade Estadual Paulista, Botucatu, Brazil \n21. Institute for Applied Ecology, University of Canberra, Bruce, Australia \n22. Department of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, USA \n23. National  Centre  for  Indigenous  Genomics, John Curtin School of Medical Research, Australian National University, \nActon, Australia \n24. Clinical Genetics Service, Institute of Precision Medicine and Bioinformatics, Royal Prince Alfred Hospital, Sydney, \nAustralia \n \n* Contributed equally \n# Correspondence: i.deveson@garvan.org.au \n \n \n  \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 4, 2025. ; https://doi.org/10.1101/2025.03.31.646505doi: bioRxiv preprint \n\n \n1 \nABSTRACT \nRecent advances in long-read sequencing (LRS) and assembly algorithms have made it possible to create highly \ncomplete genome assemblies for humans, animals, plants and other eukaryotes. However, there is a need for \nongoing development to improve accessibil ity and affordability of the required data, increase the range of \nusable sample types, and reliably resolve the most challenging, repetitive genome regions. ‘Cornetto’ is a new \nexperimental paradigm in which the genome assembly process is adaptively integr ated with programmable \nselective nanopore sequencing, with target regions being iteratively updated to focus LRS data production onto \nthe unsolved regions of a nascent assembly. This improves assembly quality and streamlines the process, both \nfor human individuals and diverse non-human vertebrates, including endemic Australian endangered species, \ntested here. Cornetto enables us to generate highly complete diploid human genome assemblies using only a \nsingle LRS platform, surpassing the quality of previous efforts at a fraction of the cost. Cornetto enables genome \nassembly from challenging sample types like human saliva, for the first time, further enhancing accessibility. \nFinally, we obtain complete and accurate assemblies for clinically-relevant repetitive loci at the extremes of the \ngenome, demonstrating valid approaches for genetic diagnosis in facioscapulohumeral muscular dystrophy \n(FSHD) and MUC1-autosomal dominant tubulointerstitial kidney disease (MUC1-ADTKD) – inherited diseases for \nwhich diagnosis is  complicated by an inability to sequence the genes involved. In summary, Cornetto will \nimprove, accelerate and democratise genome assembly, delivering impacts across a range of bioscience \ndomains. \nINTRODUCTION \nThe capacity to obtain high quality and even complete telomere -to-telomere (T2T) assemblies for large \neukaryotic genomes is transforming our understanding of genome architecture, variation and evolution, and \nwill lead to improvements in genomic disease diagnosis1,2. The first complete T2T human genome was published \nin 2022, overcoming technical challenges that had left the final 8% of its sequence unsolved for two decades \nafter the conclusion of the Human Genome Project 3. Recent advances in the field have been driven \npredominantly by a handful of current US -led consortium projects, including the T2T Consortium3, the Human \nPangenome Reference Consortium (HPRC) 4 and Vertebrate Genome Project (VGP) 5. These critical initiatives \nhave led the way in molecular and computational methods development for eukaryotic genome assembly and \nevaluation3–14. \nHowever, concentration of research and innovation within major consortium projects also reflects the high cost \nand high degree of technical expertise involved in producing a complete and accurate genome assembly. \nCurrent best practices call for a combinati on of deep Pacific Biosciences (PacBio) ‘HiFi’ LRS data, coupled with \nOxford Nanopore Technologies (ONT) ‘ultra-long’ LRS data and Illumina ‘HiC’ chromatin conformation capture, \nor an analogous ‘long -range’ sequencing method 1. Integration of these different data types helps to address \nblindspots in each. For example, the higher accuracy of PacBio HiFi is useful for untangling segmentally \nduplicated DNA with fine sequence differences between copies, ONT’s longer reads have capacity to span large \nrepeats or extended regions of homozygosity, and HiC enables long -range phasing to resolve haplotypes at \nchromosome scale1. This recipe requires access to three sequencing platforms, comes with onerous sample \nrequirements and considerable cost. \nTherefore, there is a need for ongoing development to improve the affordability and accessibility of data \nproduction; increase the breadth of usable sample types and qualities; and improve data quality and assembly \nalgorithms to better resolve the genome’s most challenging regions. Failure to address these barriers will ensure \nthe continued exclusion of many potential research projects, cohorts and species from this new era of complete \ngenomes. \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 4, 2025. ; https://doi.org/10.1101/2025.03.31.646505doi: bioRxiv preprint \n\n \n2 \nHere we present a novel genome assembly strategy designed to meet this need. ONT’s ‘ReadUntil’ or ‘adaptive \nsampling’ functionality enables programmable selective LRS by accepting or rejecting DNA fragments, based on \ntheir sequence, in real-time15. This can be used to enrich genomic regions of interest, enabling targeted analysis \nof clinically relevant genes, for example 16–18. We have adapted this capability to the challenge of genome \nassembly, integrating selective sequencing with the assembly process to enrich LRS data where it is most \nneeded, thereby reducing production costs, sample requirements and improving assembly quality (Fig1a). Our \nnew strategy is suitable for human and non-human genomes, and resolves highly repetitive medically-relevant \nloci and hemizygous sex chromosomes, all with exceptional accuracy. \nRESULTS \nCornetto: integrated adaptive sequencing and assembly \nMost of the euchromatic human genome is relatively easy to assemble using current LRS data. For example, we \nsequenced DNA from the hg002 reference sample on a single PacBio SMRT cell (~25x depth) and assembled the \ndata with hifiasm7. In the resulting primary assembly, less than 10% of the genome sequence remains in contigs \nshorter than ~5 Mbase ( Fig1b-i). The assembly may be improved with further whole -genome LRS, however, \nthere is diminishing marginal utility because most of the genome is already resolved ( Fig1b-i). Instead, we \nreasoned that ONT ReadUntil15 could be used to enrich for regions of the genome that are difficult to assemble. \nRather than defining these target regions within the human reference genome based on prior knowledge, a \nmore agnostic and efficient approach is to identify solved regions wi thin a starting assembly of moderate \nquality, then program these for rejection during subsequent ONT sequencing so as to enrich for unsolved \nregions. \nThis idea forms the basis for an integrated sequencing and assembly paradigm, which we nickname ‘Cornetto’ \n(see Methods). To establish the method, we took the hg002 primary assembly above as an initial reference, \nidentifying short contigs (< 800 Mbase), regions adjacent to the end of a contig (within 200 kb), and regions with \npoor coverage, mapability or assembly quality, then labelling the remaining ~89% of the assembly as ‘boring \nbits’. We then performed ONT duplex sequencing using the software ReadFish15 to programmably reject DNA \nfragments originating from any of the boring bits, in real-time (Fig1a). We used ONT duplex data here, because \nit is sufficiently similar in per-base accuracy to be co-assembled with PacBio HiFi data (Extended Data Fig1a-c). \nThe experiment was paused at regular intervals, allowing new and existing data to be aggregated and \nreassembled (Fig1a). The experiment was then resumed using the new assembly and an updated list of boring \nbits for rejection. The assembly and target selection  processes are automated, with no manual curation \nrequired. The assembly was iteratively improved, expanding the boring bits and, thereby, focusing new data \nonto an increasingly small, unsolved fraction of genome (Fig1a). \nHuman genome assembly with PacBio and ONT data \nWe performed two independent experiments with DNA from hg002 (hg002-Cornetto-1 and hg002-Cornetto-2). \nEach was sequenced with one PacBio SMRT cell and three ONT duplex flow cells, which were run in succession \naccording to the iterative Cornetto process above (3x cycles per flow cell). Comparing the primary assemblies, \nwe observed incremental improvement s in contiguity over the course of the experiments, resulting in \nsubstantial overall gains ( Fig1b-iii,iv). For example, hg002-Cornetto-1 was improved from 49 6 contigs with an \nN50 length of 54.5 Mbase and N90 of 6.3 Mbase to a final assembly of 130 contigs with N50 of 134.1 Mbase (2.5-\nfold improvement) and N90 of 50.4 Mbase (8.0-fold improvement; Fig1b-iii,iv; Extended Data Table 1). Whereas \nno chromosome was assembled as a single primary contig in the initial assemblies, we obtained 15 in the final \nassembly for hg002-Cornetto-1 and 11 for hg002-Cornetto-2 (Fig1c). \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 4, 2025. ; https://doi.org/10.1101/2025.03.31.646505doi: bioRxiv preprint \n\n0 10 20 30 40 50 60 70 80 90 100\n0\n50\n100\n150\n200\n250\n300Contig length (MBase)\n0 10 20 30 40 50 60 70 80 90 100\n0\n50\n100\n150\n200\n250\n300Contig length (MBase)\n0 10 20 30 40 50 60 70 80 90 100\n0\n50\n100\n150\n200\n250\n300Contig length (MBase)\n0 10 20 30 40 50 60 70 80 90 100\n0\n50\n100\n150\n200\n250\n300\nCumulative size (% of genome)\nContig length (MBase)\n0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9\n1\n2\n3\n4\n5\n6\n7\n8\n9\n10\n11\n12\n13\n14\n15\n16\n17\n18\n19\n20\n21\n22\nX\nY\n1 2 3\nn SMRT\ncells\n1 2\n1 SMRT cell\n2 SMRT cell\n3 SMRT cell\nT2T-chm13\n• Poor coverage\n• Low mapQ\n• Low asm qual.\n• Contig ends\n• Short contigs \n‘boring bits’ (reject reads)\nContig\nCoverage\nLabel regions:\nWhole-genome LRS (HiFi or ONT)\nCoverage enrichment\nIterative\nsequencing\n& re-assembly:\n24 hrs...\n48 hrs...\n72 hrs...\nContigs\nHiFi hg002-Cornetto-1\n(HiFi + duplex)\n+3 / +9\nduplex\nHiFi +\nduplex\n1 2 3\n+n duplex\n1 2 3\n+n duplex\nasm.\na Cornetto adaptive genome assembly Assembly contiguityb \nc Assembly of human chromosomes\nTargeted LRS (ONT ReadUntil)\nhg002-Cornetto-1\nhg002-Cornetto-2\n9\n8\n7\n6\n5\n4\n3\n2\n1\n1 HiFi\n+1 duplex\n+2 duplex\n+3 duplex\n0\n9\n8\n7\n6\n5\n4\n3\n2\n1\n1 HiFi\n+1 duplex\n+2 duplex\n+3 duplex\n0\nhg002-NonCornetto-1 / -2\n2\n1\n1 HiFi\n+3 ONT duplex\n+9 ONT duplex\n0\nhg002 assembly w. PacBio HiFi\nhg002-Cornetto-2\n(HiFi + duplex)chr.\nchr\nL90: 1 2 3 4  >41 (w. 2x telo.)\ni\nii\niii\niv\nFigure 1. Human genome primary assemblies. (a) Overview of the Cornetto method. A primary assembly generated from whole-genome \nLRS data (PacBio HiFi or ONT), provides the starting reference. Regions with poor coverage, mappability, assembly quality, short contigs \nand contig ends are identified. The remainder are considered ‘boring bits’ and labelled for rejection by ONT ReadUntil. Sequencing is \npaused at regular intervals (e.g. 24, 48, 72hrs) and new assembly is generated, providing an updated reference and boring bits. \nSequencing is resumed after washing and re-loading flow cell. Data is focused onto an increasingly small and challenging portion of the \ngenome. (b) For a given primary assembly, Nx plots show contigs sizes sorted from largest to smallest, relative to cumulative assembly \nsize, as a percentage of human genome (3.1 Gbase). Assemblies were generated using LRS data from hg002. T2T-chm13 is shown for \ncomparison (grey line). Sub-panels show: (i) Assemblies of HiFi data from 1, 2 or 3 SMRT cells. (ii) Combined data from 1 SMRT cell and 3 \nONT duplex flow cells (hg002-NonCornetto-1); or 1 SMRT cell and 9 duplex flow cells (hg002-NonCornetto-2). (iii, iv) Two Cornetto \nexperiments (hg002-Cornetto-1 / -2), each using 1 SMRT Cell and 3 duplex flow cells run sequentially with 3 cycles per cell, resulting in 9 \nintermediate assemblies. (c) For the same assemblies, tile plot shows contiguity of human chromosomes. Colour scale encodes L\n90 values: \nnumber of contigs encompassing >90% of the reference sequence for a given chromosome. Dark purple tiles show chromosomes with L90 \n= 1 and a telomere detected at each end, indicating the whole chromosome is assembled as a single primary contig.\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 4, 2025. ; https://doi.org/10.1101/2025.03.31.646505doi: bioRxiv preprint \n\n \n3 \nTo put these results in context, we generated a matched assembly from the same PacBio HiFi data, this time \naugmented with three ONT duplex flow cells run without adaptive sequencing (hg002-NonCornetto-1; Extended \nData Fig1a-c). The initial assembly was improved, but the gains were small compared to those achieved using \nCornetto. For example, N 50 and N 90 lengths were improved by 1.5 -fold and 2.7 -fold, respectively, in hg002-\nNonCornetto-1 compared to 2.5 -fold and 8 -fold for hg002-Cornetto-1 (Fig1b-ii,c). We f urther augmented the \nnon-Cornetto assembly by adding published ONT duplex data8 (hg002-NonCornetto-2). However, even with up \nto nine duplex flow cells – beyond which there were no further gains – we were unable to obtain a primary \nassembly of comparable contiguity to hg002-Cornetto-1 or -2 (Fig1b-ii,c). Assemblies generated using Cornetto \nwere also equivalent or superior across a range of standard quality metrics, including per -base accuracy (QV), \nBUSCO gene completeness, and rates of duplicated or fragmented genes (Extended Data Table 1). These results \nestablish the capacity of our Cornetto strategy to efficiently harness LRS data for improved assembly of human \ngenomes. \nONT-only diploid human genome assemblies \nTo further streamline the assembly process, we next tested the Cornetto paradigm using ONT data alone. We \ngenerated a new hg002 assembly (hg002-Cornetto-3), this time with data from a standard ONT flow cell (LSK114; \nsimplex reads; Extended Data Fig1a -c) augmented with a second flow cell run with Cornetto adaptive \nsequencing (3x cycles). As above, the primary assembly was improved via Cornetto, obtaining a final N50 of 154.4 \nMbase (1.6-fold improvement) and N90 of 79.1 Mbase (2.1-fold improvement; Extended Data Fig2a-d; Extended \nData Table 2). \nGiven this promising result, we adapted the Cornetto paradigm toward the challenge of producing diploid \ngenome assemblies, noting that all results reported so far refer to primary assemblies (i.e. where maternal and \npaternal haplotypes remain collapsed into a single, linear representation). To do so, phased contigs produced \nby hifiasm during each Cornetto cycle were aligned to their corresponding primary assembly, to identify regions \nnot spanned by contigs from both haplotypes ( Fig2a). These unphased regions were excluded from the list of \nboring bits, which were otherwise defined as above (see Methods). We reasoned that the enrichment of \ncoverage in these regions may be beneficial for closing gaps in phasing – by providing additional reads that may \nspan a homozygous region, for example – thereby improving the resulting diploid assembly (Fig2a). \nWe used our modified Cornetto strategy to generate a diploid hg002 assembly (hg002-Cornetto-4), again using \none standard ONT flow cell and a second run with Cornetto adaptive sequencing. We obtained a highly complete \ndiploid assembly, with haplotypes exhibiting N50 lengths of 132.2 and 135.7 Mbase (2.6-fold improvement) and \nN90 lengths of 35.6 and 61.3 Mbase (8.1 -fold improvement; Fig2b; Extended Data Fig3a ). Hg002-Cornetto-4 \ncontained 27 complete chromosomes out of a possible 46, compared to just 3/46 prio r to Cornetto ( Fig2c,d; \nExtended Data Fig3b). This included complete pairs for ten autosomes and, notably, both chrX and chrY were \nfully assembled despite their repetitive architectures ( Fig2d). Alignment of hg002-Cornetto-4 to the Q100 \nproject T2T-hg002 reference, taken here as a ground truth, confirmed the assembly is highly complete, accurate \nand free of large misassemblies (Fig2c; Extended Data Fig3c). \nA recent T2T Consortium study presented a strategy for assembling diploid human genomes using data from \nONT instruments alone, doing so with a combination of 50x duplex, 30x ultra-long and 50x pore-C data, utilising \n>15 ONT flow cells in total 8. We evaluated our hg002-Cornetto-4 assembly, which was created using a single \nONT ligation library prep and just two flow cells, by comparison to this published assembly ( T2TC-ONT-only). \nHg002-Cornetto-4 and T2TC-ONT-only showed similar contiguity and contained 27 vs 26 complete \nchromosomes, respectively (Fig2b,d; Extended Data Table 2). Hg002-Cornetto-4 was somewhat more complete \nand accurate than T2TC-ONT-only (BUSCO 99.0% vs 98.1%; QV 56 vs 53), and switch error rates were equivalent \n(1.2%; Fig2e). The use of pore -C data (analogous to Hi C) for chromosome scaffolding and phasing means that \nonly 13 of the 26 complete chromosomes in T2TC-ONT-only are free of gaps, whereas hg002-Cornetto-4 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 4, 2025. ; https://doi.org/10.1101/2025.03.31.646505doi: bioRxiv preprint \n\nAligned-invertedAligned\nAligned contigs (hg002-Cornetto-4 hap1)\nReference chromosomes (T2T-hg002)\nc Alignment to Q100 T2T-hg002 reference\na b\n0 10 20 30 40 50 60 70 80 90 100\n0\n50\n100\n150\n200\n250\nCumulative size (% of genome)\nContig length (MBase)\nWhole-genome LRS (ONT)\nContigs\nCoverage\nPoor cov.\nor quality\nh1\nh2\nPrimary assembly\nCoverage enrichment on unphased regions\nGaps in\nphasing‘boring bits’ (reject reads)\nDiploid\ncontigs\nCornetto strategy for diploid assemblies Comparison to T2T Consortium assembly\nT2TC-ONT-only (scafs.)\nT2TC-ONT-only (contigs)\nhg002-Cornetto-4 (2 FC)\nhg002-base (1 FC)\nT2T-hg002\nh1 h2\n/\n/\n/\n/\n/\n1\n2\n3\n4\n5\n6\n7\n8\n9\n10\n11\n12\n13\n14\n15\n16\n17\n18\n19\n20\n21\n22\nX\nY\nhg002-\nCornetto-4\nT2TC-ONT-only\nh1 h2 h1 h2 h1 h2 h1 h2\nctgs. scafs.\nhg002-\nbase\nL90: 1 2 3 4  >41 (w. 2x telo.)\nd\n95\n96\n97\n98\n99\n100\nBUSCO\nGenes (%)\nAccuracy\n(QV)\n10\n30\nGaps\n0.0\n0.5\n1.0\n1.5\nSwitch\nerrors (%)\n0\n20\n40\n45\n50\n55\n40\nComp. Dup. Frag.\nHamming\nerrors (%)\nBase\nCornetto\nT2TC-ONT-only\nctgs scafse\n1 2 3 4 5 6 7 8 9 10 11 12 14 16 18 20 22 X Y\nFigure 2. Nanopore-only diploid human genome assemblies. ( a) Overview of the updated Cornetto method for improved diploid \nassemblies. Same process as shown in Figure 1 is followed with the additional step of excluding unphased regions from the list of boring \nbits. These are determined by aligning all contigs from haplotype 1 & 2 to their primary assembly and identifying any region not spanned \nby a contig on both haplotypes. Enrichment of ONT data in these regions may help to close phase gaps. (b) Nx plot shows contig/scaffold \nsizes sorted from largest to smallest, relative to cumulative assembly size, as a percentage of human genome (3.1 Gbase). The plot \ncompares hg002 diploid assemblies generated with haplotypes plotted separately (h1/h2). Assemblies generated using ONT data from one \nflow cell (hg002-base) then augmented with a second flow cell run with Cornetto (hg002-Cornetto-4) are compared to an ONT-only \nassembly from the T2T Consortium (T2TC-ONT-only), plotted as scaffolds vs contigs. The Q100 T2T-hg002 assembly provides a reference. \n(c) Dot plot shows alignment of hg002-Cornetto-4 contigs (vertical axis) to chromosomes in Q100 T2T-hg002 (horizontal axis). The plot \nshows haplotype 1 and haplotype 2 is in Extended Data Fig3. (d) Tile plot shows contiguity of human chromosomes in same assemblies as \nb. Colour scale encodes L\n90 values: number of contigs encompassing >90% of the reference sequence for a given chromosome. Dark purple \ntiles show chromosomes with L90 = 1 and a telomere detected at each contig end, indicating the whole chromosome is assembled into a \nsingle contig. (e) Bar charts compare assembly quality metrics: proportion of BUSCO genes detected as complete, duplicated, fragmented \nor missing; total number of gaps; sequence accuracy, as per QV values (k-mer size of 21); switch errors (%); hamming errors (%).\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 4, 2025. ; https://doi.org/10.1101/2025.03.31.646505doi: bioRxiv preprint \n\n \n4 \ncontains no gaps (Fig2d,e). Conversely, chromosomes in hg002-Cornetto-4 are only partially phased, reflected \nin its high rate of hamming errors (30.2%; Fig2e). We noted this could be addressed by using parental \nsequencing data for long-range phasing of the hg002-Cornetto-4 (Extended Data Table 2). Overall, while not a \nperfect like -for-like comparison, our ONT -only diploid human assembly hg002-Cornetto-4 is equivalent or \nsuperior to T2TC-ONT-only on most metrics, despite being created with a fraction of the resources. \nAssembling medically-relevant repetitive loci \nThe human genome contains hundreds of analytically challenging repetitive loci with known roles in disease12. \nTo illustrate the potential for Cornetto to improve inherited disease diagnosis, we next explored two such loci, \nwhich are among the most extreme known examples. In both cases, a current inability to sequence the causative \nlocus is a barrier to effective diagnosis for its relevant disease, namely facioscapulohumeral muscular dystrophy \n(FSHD) and MUC1-autosomal dominant tubulointerstitial kidney disease (MUC1-ADTKD). The unmet needs and \nchallenges involved are described in more detail in Supplementary Notes 1 and 2, respectively. \nFSHD is a progressive myopathy resulting from aberrant expression of the DUX4 gene residing within the 4q \nD4Z4 macrosatellite repeat, a polymorphic n x 3.3kb tandem repeat in the sub-telomeric region of chr4q19. The \nrepeat typically ranges in size from ~11 –100 copies 19. FSHD most commonly presents in individuals with a \ncontracted 4q D4Z4 haplotype (<10 copies), which must also harbour a permissive sequence variant (4qA) \nfollowing the distal-most DUX4 copy19. To assess our capacity to accurately assemble this locus, we extracted \nthe sub -telomeric region from both copies of chr4q in the hg002-Cornetto-4 assembly, annotated known \nsequence features relevant to FSHD, then compared them to the equivalent regions of the T2T-hg002 reference, \nagain taken as ground truth. In both assemblies we identified one D4Z4 haplotype at 42 copies in length (~139 \nkb) of subtype 4qA and a second at 26 copies (~86 kb) of subtype 4qB (Fig3a). Aligning corresponding haplotypes \nbetween the two assemblies, we observed 99.99% and 99.97% sequence concordance across entire 4q D4Z4 \nregions (Fig3a). We next performed targeted ONT sequencing and assembly of this region in four patients with \ndiagnostically confirmed FSHD. In each case we identified one D4Z4 haplotype of the permissive 4qA sub -type \nwith fewer than 10 copies, sufficient for a positive diagnosis, and observed repeat lengths that were concordant \nwith previous molecular genetic testing (Fig3b; see Supplementary Note 1). \nADTKD is a chronic kidney disease typically caused by variants in one of four genes, UMOD, MUC1, REN and \nHNF1B20. MUC1 is thought to account for around ~20% of cases, however, diagnosis of MUC1-ADTKD is obscured \nby technical challenges in resolving this gene. MUC1 contains a n x 60bp variable number tandem repeat region \n(VNTR)21. This is highly polymorphic, varying in length (20 –125 copies per haplotype) and differing in the \ncomposition of imperfect sequence subunits within and between individuals4,21. Duplication of a cytosine (dupC) \nwithin this VNTR, which results in a frameshifting variant, has been identified as the predominant cause of \nMUC1-ADTKD22 (Fig4a). We identified both copies of the MUC1 locus within our hg002-Cornetto-4 assembly, \nannotated the VNTR region for known and novel 60bp subunits, and compared them to T2T-hg002 (Fig4b). In \nboth assemblies we identified one VNTR haplotype with 65 x 60bp copies (~3.9 kb) and one with 78 x 60bp \ncopies (~4.7 kb). The composition and order of VNTR subunits was matched and, aligning corresponding \nhaplotypes between assemblies, we observed pe rfect sequence concordance across the entire VNTR region \n(Fig4b). We next performed targeted ONT sequencing and MUC1 assembly in ten patients with diagnostically \nconfirmed MUC1-ADTKD. Across 11 individuals (including hg002-Cornetto-4), we observed 20 unique VNTR \nhaplotypes which ranged in size from 40 –83 copies, with no individuals sharing the same pair of haplotypes \n(Fig4b). In each patient (but not hg002) we identified a single dupC frameshift variant within the VNTR occurring \non a single haplotype, sufficient for a positive diagnosis. Notably, 8/10 pathogenic haplotype s were unique, \nimplying frequent independent origins of the dupC variant ( Fig4b; see Supplementary Note 2). Overall, these \nresults establish the capacity to assemble both the 4q D4Z4 and MUC1 loci with exceptional accuracy, providing \nviable new avenues to improve the genetic diagnosis of FSHD and ADTKD. \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 4, 2025. ; https://doi.org/10.1101/2025.03.31.646505doi: bioRxiv preprint \n\nchromosome 4\n4qB probe\nTelomere\n4qA probe\npLAM\nP13-E11 probe\nProximal sequence\n10 kb scale\nh1 (26 copies)\nh2 (42 copies)\nh1 (26 copies; 99.99% identity)\nh2 (42 copies; 99.97% identity)\nd4z4 repeat (3.3 kb)\na D4Z4 macrosatellite (n x 3.3kb copies)\nT2T-hg002hg002-Cornetto-4\nPatient 1\n(9x D4Z4 copies)\nsub-telomeric region\n>10 copies\n(pathogenic cut-off)b\nAssembly validation\nFSHD patient haplotypes\nPatient 2\n(6x D4Z4 copies)\nPatient 3\n(5x D4Z4 copies)\nPatient 4\n(4x D4Z4 copies)\nFigure 3. Assembly and genotyping of 4q D4Z4 for genetic diagnosis of FSHD. ( a) Genome browser views show annotated sequence \nfeatures within the 4q subtelomeric region on each haplotype (h1 / h2) for the Q100 T2T-hg002 reference assembly (upper) and the \nhg002-Cornetto-4 assembly (lower). The D4Z4 macrosatellite repeat is annotated with recurring 3.3 kb subunits in yellow. A range of other \nsequence features relevant for 4q D4Z4 genotyping are shown, including markers for the permissive (4qA) and non-permissive (4qB) distal \nDUX4 sequence variants (see Supplementary Note 1). The 4q D4Z4 length is stated above each haplotype. Identity scores stated for \nhg002-Cornetto-4 were determined by aligning the entire 4q D4Z4 sequence to the corresponding haplotype in the T2T-hg002 reference, \ntaken as a ground truth. (b) Same plots as above, this time showing pathogenic haplotypes assembled using targeted ONT sequencing of \nthe 4q D4Z4 region for four patients with diagnostically confirmed FSHD. In each case, the 4q D4Z4 length is shorter than 11 copies and \nharbours the permissive 4qA sequence variant, sufficient for a positive genetic diagnosis. Expected repeat sizes from previous genetic \ntesting are stated for each patient (see Supplementary Note 1).\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 4, 2025. ; https://doi.org/10.1101/2025.03.31.646505doi: bioRxiv preprint \n\nhg002-Cornetto-4\nQ100 T2T-hg002\n1\n2\n3\n4\n5\nC\nX\nD\nE\nA\nB\nG\nV\n6\n7\n8\n9\n6.2\nL\nI\nJ\n6p\nF\nX.3\nX.2\nP\nX!\nPat\nMat\nh1\nh2\nh1\nh2\nh1\nh2\nh1\nh2\nh1\nh2\nh1\nh2\nh1\nh2\nh1\nh2\nh1\nh2\nh1\nh2\nh1\nh2\nNovel\nCanonical\nvs\npathogenic\nVNTR subunits\nVNTR length (n x 60bp subunits)\n0 20 40 60 80\nVNTR region: n x 60bp subunits\nGCCCACGGTGTCACCTCGGCCCCGGAGAGCAGGCCGGCCCCGGGCTCCACCGCGCCCGCA\nGCCCACGGTGTCACCTCGGCCCCGGAGAGCAGGCCGGCCCCGGGCTCCACCGCCCCCCCA\nGCCCACGGTGTCACCTCGGCCCCGGACACCAGGCCGGCCCCGGGCTCCACCGCCCCCCCA\nGCCCACGGTGTCACCTCGGCCCCGGACACCAGGCCGGCCCCGGGCTCCACCGCCCCCCCCA\nX\nX!\nA\nB\n... ...\ndupC\npathogenic\nvariant\nMUC1\n60 bp imperfect recurring motifs\np1\np2\np3\np4\np5\np6\np7\np8\np9\np10\na\nb\nArchitecture of MUC1 VNTR\nMUC1 assembly with targeted ONT sequencing\nFigure 4. Assembly and genotyping of MUC1 for genetic diagnosis of ADTKD. ( a) Schematic of MUC1 variable number tandem repeat \nregion (VNTR) and known genetic basis for MUC1-ADTKD. Briefly, MUC1 contains a large VNTR comprising recurring imperfect 60bp \nsubunits, which varies in length and sequence composition within and between individuals. Duplication of a cytosine (dupC) within the \nVNTR, resulting in a frame-shift causes MUC1-ADTKD (see Supplementary Note 2). (b) Tile-bar plots show the VNTR length and subunit \ncomposition identified for each MUC1 haplotype (h1 / h2) for the Q100 T2T-hg002 reference (upper) and hg002-Cornetto-4 assembly, \nwhich show identical length and sequence composition between corresponding haplotypes. Below are VNTR haplotypes assembled via \ntargeted ONT sequencing in ten patients with diagnostically confirmed MUC1-ADTKD. Different coloured tiles indicate known and novel \n60bp sequence subunits, with the known dupC pathogenic subunit (X!) shown in red. A single X! subunit was identified on one haplotype \nin each patient, sufficient for a positive diagnosis (see Supplementary Note 2).\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 4, 2025. ; https://doi.org/10.1101/2025.03.31.646505doi: bioRxiv preprint \n\n \n5 \nGenome assemblies from human saliva \nAnother benefit of Cornetto is to enable production of high quality assemblies from challenging and/or limited \nsample types. Human saliva is one such sample type where there would be significant utility. Saliva is more \neasily accessible than blood or other  human tissues and can be collected, shipped and stored at room \ntemperature. This can be advantageous in some clinical contexts, for field studies in remote communities 23 or \neven for direct -to-consumer genomics24. However, saliva is less amenable than blood to extraction of high -\nmolecular weight (HMW) DNA; is not compatible with ONT’s ultra-long protocol, nor HiC or other related long-\nrange methods; and often suffers from relatively high levels of non -human DNA contamination25. Given these \nchallenges, we are not aware of previous attempts to assemble a human genome from saliva. \nWe collected saliva from a male and female participant, extracted HMW DNA, then conducted Cornetto \nsequencing and assembly on each. We tested a combined PacBio HiFi and ONT duplex approach ( saliva-A-\nCornetto-1; saliva-B-Cornetto-1) and an ONT-only approach (saliva-A-Cornetto-2; saliva-B-Cornetto-2). Because \nnon-human DNA was present ( Fig5a), we additionally included non -human contigs identified in the initial \nassemblies in the target list for rejection, selecting against further contamination (see Methods). Both Cornetto \napproaches yielded high -quality genome assemblies with improved contiguity and completeness relative to \ntheir starting assemblies ( Fig5b-d; Extended Data Fig4a -c; Extended Data Table 3 ). The improvements were \nparticularly pronounced for ONT-only diploid assemblies saliva-A-Cornetto-2 and saliva-B-Cornetto-2, for which \nwe obtained final contig N 90 lengths of 46.5 Mbase (15 -fold) and 50.1 Mbase (27 -fold), and 27/46 and 26/46 \ncomplete chromosomes, respectively ( Fig5b,c). Despite the use of saliv a as input material, these results are \ncomparable to the hg002-Cornetto-4 and T2TC-ONT-only assemblies above. \nFor further context, we compared our saliva assemblies to 47 assemblies released in the first phase of the HPRC, \nwhich were generated using cultured cells and a combination of LRS and long -range techniques4. Saliva-A-\nCornetto-2 and Saliva-B-Cornetto-2 exhibited comparable or superior BUSCO gene completeness and \nsubstantially better contiguity than any available HPRC assembly (Fig5b,d). Although assembly accuracy cannot \nbe directly measured, as no ground truth is available, the results presented above for hg002-Cornetto-4 imply \ncomparability with HPRC assemblies on these parameters. In summary, Cornetto can be used to obtain highly \ncomplete assemblies from human saliva, which are in line with (or surpass) quality standards at the leading edge \nof the genomics field. \nGenome assemblies for non-human vertebrates \nCornetto is sequence-agnostic and does not rely on any prior knowledge of the genome being assembled. In \ntheory, the method is suitable for any species. To establish this, we next assembled genomes for a selection of \nnon-human vertebrates from diverse line ages, prioritised for their salience in research and conservation. The \ncritically endangered orange -bellied parrot ( Neophema chrysogaster ) and endangered western saw -shelled \nturtle (Myuchelys bellii) were assembled using only ONT data, while Gould's petrel  (Pterodroma leucoptera; a \nthreatened seabird) and the redstriped eartheater cichlid ( Geophagus surinamensis; an Amazonian fish) were \nassembled with PacBio HiFi and ONT duplex data (see Supplementary Note 3). \nFor each species, Cornetto delivered improvements in genome assembly outcomes, compared to base \nassemblies generated with standard LRS data ( Fig6a-c; Extended Data Fig5). For example, we obtained a 3.9 -\nfold increase in contig length N50 for the petrel primary assembly and an increase in the number of chromosomes \nassembled as single primary contigs from 6 to 27 ( Fig6a-c). ONT-only diploid assemblies were also strongly \nimproved. In the turtle genome, for example, each haplotype harboured ten complete chromosomes, including \nexamples of both macro and microchromosomes, and complete single copies for 99.8% and 99.6% of BUSCO \ngenes ( Fig6b,c). Importantly, these improvements were obtained despite wide variation in genome sizes, \narchitecture, depth of starting LRS data and initial assembly quality (see Supplementary Note 3). For example, \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 4, 2025. ; https://doi.org/10.1101/2025.03.31.646505doi: bioRxiv preprint \n\na Genome assemblies from saliva\n1\n2\n3\n4\n5\n6\n7\n8\n9\n10\n11\n12\n13\n14\n15\n16\n17\n18\n19\n20\n21\n22\nX\nY\n94 96 98 100\nBUSCO (%)\nHPRC Maternal\n0\n50\n100\n150\n200\n250\nContig length (MBase)\n0 10 20 30 40 50 60 70 80 90 100\n0\n50\n100\n150\n200\n250\nContig length (MBase)\nHuman\nNon-human\n0 20 80 1000\n0.25\nRead length (kb)\nFraction\n-BSaliva-A\n Fraction\n99% 91%\nHaplotype 2\n-B\nHPRC\nhg002\nBase\nCornetto\nSaliva -A\nHaplotype 1\n-B\nHPRC\nhg002\nBase\nCornetto\nSaliva -A\nCornetto\n(2 ONT)\nh1 h2 h1 h2\nBase\n(1 ONT)\nCornetto\n(2 ONT)\nh1 h2 h1 h2\nBase\n(1 ONT)\nSaliva-A Saliva-B\nL90: 1 2 3 4  >41\nHPRC Paternal\nSaliva-B\nBase\nCornetto\nSaliva-A\nBase\nCornetto\n94 96 98 100\nComp.\nDup.\nFrag.\nMiss.\nb Contiguity\nc Assembed\nchromosomes\n(w. 2x telo.)\nd Completeness\nCumulative size (% of genome)\nFigure 5. Genome assemblies from human saliva. (a) Histograms show read length profiles and pie charts show proportion of non-human \nreads from standard ONT sequencing on saliva samples from two participants: Saliva-A (male) and Saliva-B (female). (b) Nx plot shows \ncontig sizes sorted from largest to smallest, relative to cumulative assembly size, as a percentage of the human genome size (3.1 Gbase). \nFor each participant, assembly generated using ONT data from one flow cell (base) then augmented with a second flow cell run with \nCornetto are shown (Saliva-A-Cornetto-2, Saliva-B-Cornetto-2). Non-human reads were excluded prior to assembly. For comparison, thin \ngrey lines show contig sizes for all assemblies released in the first phase of the HPRC (n = 47) and thick grey lines show the Q100 T2T-hg002 \nassembly. Diploid haplotypes for each assembly are divided between the two plots. (c) For the same assemblies, tile plot shows contiguity \nof human chromosomes. Colour scale encodes L\n90 values: number of contigs encompassing >90% of the reference sequence for a given \nchromosome. Dark purple tiles show chromosomes with L 90 = 1 and a telomere detected at each contig end, indicating the whole \nchromosome is assembled as a single contig. (d) For the same assemblies as b, stacked bar charts show the proportion of BUSCO genes \ndetected as complete, duplicated, fragmented or missing. Haplotype groups containing the Y-chromosome sequence have a larger \nproportion of missing genes (designated with male marker symbol).\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 4, 2025. ; https://doi.org/10.1101/2025.03.31.646505doi: bioRxiv preprint \n\n \n6 \nthe cichlid genome was initially sequenced to 78x depth with HiFi data, yielding a base assembly of 142 contigs. \nCornetto still found room for improvement, reducing the number of primary contigs to 75, with a 3.8 -fold \nimprovement in contig length N 90 and 12 chromosomes added ( Fig6a; Extended Data Fig5a). In contrast, the \nparrot genome was sequenced to 35x depth with standard ONT data on DNA extracted from a frozen liver \nsample, yielding a base assembly of >2000 contigs. With a single additional ONT flow cell run with Cornetto we \nwere able to obtain a diploid assembly with a 4.5-fold increase in contig length N50 and a 4.6% increase in BUSCO \ncompleteness (92.1% vs 96.7%; Fig6a,c; Extended Data Fig5n). Notably, our updated assembly for the orange \nbellied parrot contained an 80kb region with three identifiable Major Histocompatibility Complex genes (MHCI, \nII-A, II-B), which are of critical importance for understanding the decline of immunogenetic diversity that \nthreatens the survival of the species, whereas a recent assembly created with HiFi and HiC data was unable to \nresolve the MHC region26 (see Supplementary Note 3 ). In summary, this establishes the suitability of our \nCornetto assembly paradigm for assembling diverse non-human genomes. \nDISCUSSION \nCornetto is a novel approach to genome assembly, applicable to both human and non -human genomes. We \nhave used Cornetto to obtain highly complete, diploid human genome assemblies for hg002 (using cultured \ncells) and from saliva samples. These are notable bo th for their completeness, accuracy and the modest \nresources used in their creation. Our best assemblies used data from a single ONT library sequenced on two \nflow cells on a portable ‘P2 Solo’ device (roughly the size of a brick), without the need for PacB io and Illumina \ndata, which require large instruments with substantial capital costs. Similarly, we did not use ONT ultra-long or \npore-C methods. These are sensitive preparations typically requiring access to cultured cells or large volumes \nof freshly drawn blood. Although we show that Cornetto is compatible with ONT’s highly accurate duplex data \ntype, this was not needed to produce our best assemblies, nor were computationally expensive error correction \nmethods using deep learning ( HERRO27 and Dorado Correct) – although these could foreseeably be used to \nfurther improve on Cornetto assemblies. Overall, Cornetto improves genome assembly outcomes, while \nstreamlining the process and enhancing accessibility. \nAlthough we obtained high quality genomes, the best Cornetto assembly ( hg002-Cornetto-4) lacked complete \ncontigs for 19 out of 46 human chromosomes. None of the acrocentric chromosomes (chr13, chr14, chr15, \nchr21, chr22) were fully assembled. These are characterised by repetitive ribosomal DNA arrays, which are \nlargely intractable for current assembly algorithms1,3. Centromere regions were similarly problematic, as these \nregions have typically been tackled using ONT ultra -long reads previously 28. Recent improvements to the \nhifiasm7 software have been critical to the viability of the Cornetto paradigm and we anticipate future updates \nmay help to resolve these remaining genome regions. Another limitation is the lack of full-length chromosome \nphasing, given HiC/pore-C was not used13. Where accessible, trio sequencing data can be used to address this. \nWe also note that both ONT ultra-long and pore-C preparations are compatible with ONT selective sequencing, \nso may be successfully integrated with Cornetto in the future. Another intende d improvement is to enable \niterative updating of the genome assembly and its associated ‘boring bits’ in real-time during ONT sequencing. \nCurrently, re -assembly is performed at experimental pause -points, requiring around ~4 -6 hours. Significant \nsoftware acceleration is needed to enable real-time assembly. \nCornetto works by selectively enriching LRS data onto unsolved regions of a nascent assembly. An alternative \napproach would be to select static, predefined target regions within the human reference genome, which are \nknown to be challenging. However, this requires a high quality existing reference genome and prior knowledge \nto define target regions and is therefore unsuitable for most non-human species. The optimal target space may \nalso differ between individuals based on their specific genome architectures,  being strongly influenced by \nfeatures such as repeat lengths and homozygous regions, which vary between individuals. The optimal target \nspace may also differ depending on the nature of available data (read length, depth, accuracy). We therefore \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 4, 2025. ; https://doi.org/10.1101/2025.03.31.646505doi: bioRxiv preprint \n\n20 40 60 80 100\n0\n50\n100\n150\n200\n250Contig length (MBase)\n0\n20\n40\n60\nPetrel\nCichlid\nParrot\n100\n200\n300\n0\n40\n80\n120\nTurtle\n0 10 20 30\nComplete\nchroms\n0 50 100150\nN50 (Mbase)\n300\n200\n100\n100\n200\n300\n200 200\n100\n00\nPetrel\nCichlid\nParrot\nTurtle\nbasecorn.\nbase\ncorn.\nbase\ncorn.\nContig length\n(MBase)\nBase\nCornetto\nBase\nCornetto\nCumulative size (%)\n20 40 60 80 100\nCumulative size (%)\n20 40 60 80 100\nCumulative size (%)\n20 40 60 80 100\nCumulative size (%)\n300\n200\n100\n100\n200\n300\nHaplotype 1\nHaplotype 2\nHaplotype 1\nHaplotype 2\na Genome assemblies for non-human vertebrates\n85 100BUSCO (%)\nbasecorn.\n2 telomere\n1 telomere\n0 telomere\nHiFi + Duplex\nprimary asm.\nBase\nCornetto\nONT only\ndiploid asm.\nh2\nBase\nCornetto\nh1\n2 telomere\n1 telomere\n0 telomere\nONT only\ndiploid asm.\nh2\nBase\nCornetto\nh1\nHiFi + Duplex\nprimary asm.\nBase\nCornetto\nb\nc\nContig length (MBase)\nComp. Dup. Frag.\nTurtle\nPetrel\nFigure 6. Genome assemblies for non-human vertebrates. (a) Nx plot shows contig sizes sorted from largest to smallest, relative to \ncumulative assembly size, as a percentage of the haploid genome size for each species. From left to right the plots show genome \nassemblies for: Gould’s petrel (Pterodroma leucoptera); redstriped eartheater cichlid (Geophagus surinamensis); orange-bellied parrot \n(Neophema chrysogaster); western saw-shelled turtle (Myuchelys bellii; see Supplementary Note 3). The petrel and cichlid were \nassembled with PacBio HiFi (1 SMRT cell; base) plus ONT duplex data (2 duplex cells; cornetto). The parrot and turtle were assembled \nusing ONT data, one or two standard flow cells (base) plus another with adaptive sequencing (Cornetto; simplex reads; LSK114). For \nONT-only experiments, diploid assemblies were generated and haplotypes are plotted separately. (b) For the petrel and turtle \nassemblies above, bar plots show sizes of the fifty largest contigs in descending order, coloured according to presence of telomere \nsequences at contigs ends (both ends = purple; one end = pink). Equivalent plots for the cichlid and parrot are shown in Extended Data \nFig5. (c) Bar plots show standard quality metrics for the same assemblies as in a. From left to right, these are: contig N\n50 lengths in \nMbases; number of complete chromosomes (>1 Mbase and telomere detected at both ends); proportion of BUSCO genes detected as \ncomplete, duplicated, fragmented or missing.\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 4, 2025. ; https://doi.org/10.1101/2025.03.31.646505doi: bioRxiv preprint \n\n \n7 \nbelieve the most efficient approach is simply to select for any region that has not yet been confidently \nassembled, defining this in the context of a specific genome and all available data. \nHowever, there are scenarios where targeting static reference regions may be preferable. For example, we used \nthis approach to specifically enrich 4q D4Z4 and MUC1 to efficiently and reproducibly assemble these loci in \npatient samples. An inability to effectively sequence these loci is a major barrier to genetic diagnosis of FSHD \nand MUC1-ADTKD, respectively (see Supplementary Notes 1 & 2). There is also much that remains to be learned \nabout these diseases. For example, we show here that the dupC variant kn own to cause MUC1-ADTKD has \nreproducibly, but independently, emerged within different families or ancestry groups, reflected in the lack of \nshared pathogenic haplotypes between patients. Although dupC is overwhelmingly the most common causative \nvariant reported in MUC1-ADTKD29, this is heavily biased by the available diagnostic technique being targeted \nto this specific variant 21. We anticipate that the accurate, haplotype -resolved assembly of the MUC1 VNTR in \ncurrently undiagnosed patients will reveal additional pathogenic variants. \nThere is clear clinical utility in being able to genotype these and other challenging medically -relevant loci from \npatient saliva, but developing the capacity to assemble highly complete human genomes from saliva is an even \ngreater priority for our Australian Indigenous genomics research program23. Collection of blood during visits to \nremote Aboriginal communities is impractical and blood is culturally sensitive, thus limiting us to work with \nsaliva23. We hope that our demonstration of scalable, affordable genome assembly from saliva – with assembly \nquality at least on par with the HPRC – may open the door to even greater diversity and inclusion in the growing \npangenome field30. Applications for Cornetto are not limited to human genomics, as we have shown here by \nproducing highly complete genomes for diverse non-human vertebrates, including three critically endangered, \nendangered or threatened endemic Australian species. By impr oving costs, sample input requirements and \nproviding higher quality reference genomes than previously possible, we hope Cornetto will empower other \ngenomically-informed conservation initiatives, similar to the orange -bellied parrot 26, described above. We \nprovide all laboratory and computational methods for Cornetto assembly as a free, open source resource to \nstreamline, improve and democratise genome assembly. \n \nETHICS AND INCLUSION \nSaliva samples were collected from individuals not known to be affected by inherited disease under Human \nResearch Ethics Committee (HREC) approval 95179. Whole blood was collected from patients with FSHD under \nHREC/2019/ETH12538 and patients with MUC1-ADTKD under HREC/18/RPAH/726, HREC/83945/RCHM -2022 \nand HREC/16/MH/251. Relevant approvals for non-human vertebrates are provided in Supplementary Note 3. \nMETHODS \nCornetto adaptive sequencing and assembly method \nCornetto is a new experimental paradigm in which the genome assembly process is integrated with ONT \nReadUntil programmable selective sequencing (also known as ‘adaptive sampling’). Cornetto encompasses both \nlaboratory and computational protocols, all of wh ich are described here. Computational steps are executed \nusing the open source Cornetto software package ( https://github.com/hasindu2008/cornetto) in addition to \nother third-party open source software. Most importantly, we have used the excellent software hifiasm7 for \ngenerating assemblies and Readfish15 for executing ONT targeted sequencing. Hifiasm can be run on the user’s \npreferred machine using commands provided in Supplementary Note 4  or Cornetto online documentation. \nReadFish must be executed on the computer that runs ONT sequencing experiments, using reference files \ngenerated by Cornetto. Cornetto is also compatible, in principle, with ONT’s in -built ‘adaptive sampling’ \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 4, 2025. ; https://doi.org/10.1101/2025.03.31.646505doi: bioRxiv preprint \n\n \n8 \napplication, which is configured within MinKNOW, and with alternative assembly software, however, these have \nnot been extensively tested. \nThe Cornetto method starts with a primary assembly of moderate quality, generated with PacBio HiFi or ONT \nLRS data. For a human genome, LRS data from a single PacBio SMRT cell or a single ONT PromethION flow cell \ntypically yields a suitable base assembly. Our recommended protocols for HMW DNA extraction, DNA shearing, \nsize selection and library preparation, for both PacBio and ONT, are outlined in detail below. A human base \nassembly is created from the initial LRS data using hifiasm (versions and commands in Supplementary Note 4). \nAfter generating a base assembly, the available LRS data is realigned to this assembly (using minimap231) to \nassess regional coverage and mappability. Cornetto software is then used to identify assembly regions that are \nnot yet confidently resolved. These are defined as: extended regions of low or high coverage depth (< 40% or > \n250% of genome average); low mappability (where coverage depth in uniquely aligned MapQ20+ reads is < 40% \nof total mean coverage); low assembly quality (‘lowQ’ regions of > 8 Kbase output by hifasm); short primary \ncontigs (< 800Mbase) and; regions adjacent to the end of a primary cont ig (within 200 kb). For diploid \nassemblies, Cornetto additionally identifies unphased regions in the assembly. To do so, all contigs from \nhaplotype 1 and haplotype 2 (output by default by hifiasm) are aligned to the primary assembly to identify any \nregion that is not spanned by a contig in both haplotypes. These unphased regions are combined with the other \nlabelled regions above; coordinates of all regions are then extended with 40 kb buffers in either direction before \nmerging all overlapping and adjacent features (within 200 kb). All sequences outside of these merged regions, \nwhich typically encompass ~10 -20% of the genome, are considered to be already confidently assembled and \nwill not benefit from additional LRS data. Hence these regions are considered ‘b oring bits’ and printed to a \nstandard coordinate file ‘boringbits.bed’ and corresponding file ‘boringbits.txt’, which is used for ReadFish \nconfiguration. Commands to execute this process are provided in Supplementary Note 4. \nNext, the user should perform ONT sequencing with ReadFish (or the ONT adaptive sampling app) configured \nto reject reads originating from any of the boring bits within the initial assembly. The relevant base assembly is \nprovided as a reference, after indexing with minimap2. Commands, software versions and a templ ate with \nparameters for ReadFish configuration are provided in Supplementary Note 4. \nAfter configuring and launching ReadFish (or the ONT adaptive sampling app within MinKNOW) the user should \nload their sequencing library onto their ONT flow cell and initiate the sequencing experiment. When starting \nwith a base assembly of PacBio HiFi data , we recommend to run ONT ‘duplex’ sequencing because HiFi and \nduplex data are sufficiently similar in accuracy to be co -mingled during assembly. If starting from a base \nassembly generated with ONT simplex data, the user should continue with ONT simplex da ta. Protocols used \nduring our study for both ONT sequencing options, including basecalling, are outlined in detail below. \nTo maximise yields during ONT sequencing, it is standard practice to pause the sequencing process at regular \nintervals, wash the flow cell with a nuclease solution, reload with fresh library, then resume sequencing. During \na Cornetto experiment, the user should take advantage of these pause points in order to update their assembly \nand regenerate the boringbits reference files. When updating the reference files at each pause point, Cornetto \nuses the same rules as above, with the exception that poor coverage and mappability rules are not applied \nbeyond the first cycle (because adaptive sequencing introduces uneven coverage which would conflict with \nthese rules). When working with saliva samples, nonhuman contigs may also be added to the list of boringbits \nfor rejection at this point (see below). This process serves to focus ongoing data generation onto an increasingly \nsmall and challenging portion of the genome that remains unassembled. We typically perform 3 or 4 Cornetto \ncycles for a single ONT flow cell, with pause points at ~24 hr, ~48 hr and ~72 hr (if the flow cell remains viable). \nEach new assembly should be generated by aggregating all existing and new data (see Supplementary Note 4). \nAt the end of the process, the user should obtain a final assembly encompassing LRS data from all previous \nsteps. The Cornetto software also provides a simple wrapper script used to evaluate this assembly with a range \nof standard metrics. The evaluation  process run by Cornetto and employed during this study are outlined in \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 4, 2025. ; https://doi.org/10.1101/2025.03.31.646505doi: bioRxiv preprint \n\n \n9 \ndetail below with software versions and commands in Supplementary Note 4. Specifics of the process used for \nFSHD and ADTKD patients are outlined in Supplementary Note 1 and 2, respectively. Specifics of the process \nused for non-human vertebrates are outlined in Supplementary Note 3. \nHigh-molecular weight DNA extractions \nHigh–molecular weight (HMW) genomic DNA was extracted from cultured cells from human reference sample \nhg002, obtained from the Coriell Institute for Medical Research (B -Lymphocyte cell line GM24385), and \nperipheral blood samples from patients with FSHD and  ADTKD, using the PacBio Nanobind CBB kit (102 -301-\n900) or PacBio PanDNA kit (103 -260-000) according to the manufacturer’s protocol. For saliva experiments, \n~5mL of saliva was collected from healthy donors using Oragene self -collection kits (DNA Genotek) and stored \nat room temperature. Saliva was extracted using the PacBio Nanobind CBB kit with an optimised protocol that \nis now supported by the manufacturer (103-544-000). For experiments involving non-human vertebrates, HMW \nDNA was extracted from blood for the petrel, cichlid and turtle and from snap frozen liver tissue for the orange \nbellied parrot (see Supplementary Note 3). For blood samples, approximately 20 -70 ul was used as input (or \nequivalent for ethanol stored blood samples) and these were extracted as per the PacBio Nanobind protocol for \nnucleated blood. For the liver tissue, 20 mg was used as input and this was extracted using the PacBio Nanobind \nkit standard Dounce homogenizer protocol. QC checks were performed on all extracted DNA samples using a  \nThermoFisher NanoDrop (purity), ThermoFisher Qubit (DNA concentration) and Agilent Femto Pulse (Genomic \nDNA 165 kb Kit; DNA fragment size profiles). \nLong-read sequencing methods \nFor PacBio sequencing experiments, DNA samples were sheared to average fragment lengths of 15–24 Kb using \na Diagenode Megaruptor 3 with Shearing Kit at a speed of 30 or 31. Sheared DNA was cleaned, concentrated, \nthen subject to PacBio SMRTbell library prep aration, all according to the manufacturer’s protocol. Prepped \nlibraries were size selected  with a 35% v/v dilution of AMPure PB beads  at a ratio of 2.9x (i.e. 50 ul of sample : \n145 ul 35% beads) or using a PippinHT (Sage Science) with a 10 kb cut -off, followed by ABC loading procedure \nand sequencing on a PacBio Revio instrument with 30 hour movie time. \nFor ONT sequencing experiments, DNA samples were sheared to average fragment lengths of 43–66 Kb using a \nDiagenode Megaruptor 3 with Shearing Kit and a shearing speed of 27. Sheared samples were treated with \nPacBio Short-Read Eliminator kit to deplete fragments < 10 kb. ONT libraries were then prepared from ~5–9 µg \nof sheared HMW genomic DNA using a ligation prep (SQK -LSK114). The resulting libraries were loaded on an \nONT PromethION R10.4.1 flow cell (FLO-PRO114M) or a PromethION high-duplex flow cell (FLO-PRO114HD) and \nsequenced on a PromethION instrument (P2 Solo or P48). Sequencing experiments were typically run for 72 \nhours, with washes (EXP -WSH004) and library reloading performed at approximately 24 and 48 -hour time \npoints. Where flow cells were still viable, an additional wash was performed at 72 hours, followed by a further \n24 hr runtime. For Cornetto experiments, live target selection/rejection was executed during the run by the \nReadfish15 software package with commands and configuration parameters in Supplementary Note 4. \nRaw ONT sequencing data was converted from POD5 to BLOW5 format 32 in real-time during sequencing. At \npause-points during sequencing, or after completion of a run, data was base-called using slow5-dorado (v0.8.3; \nhttps://github.com/hiruna72/slow5-dorado) with a recent ‘super -accuracy’ model \n(dna_r10.4.1_e8.2_400bps_sup@v5.0.0) and a qscore cut off of 10 (--min-qscore 10). For high-duplex flow cells, \nduplex basecalling was run using slow5-dorado (v0.3.4;  dna_r10.4.1_e8.2_400bps_sup@v4.2.0) and non -\nduplex reads were removed prior to downstream analysis. For ONT -only Cornetto experiments, simplex reads \nwere filtered to exclude reads shorter than 30kb (seqkit-v2.3.0)33, to avoid excessive coverage within on-target \nregions (noting that this filtering was not performed on reads used to generate the base assembly). \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 4, 2025. ; https://doi.org/10.1101/2025.03.31.646505doi: bioRxiv preprint \n\n \n10 \nSaliva assemblies and nonhuman reads \nFor human saliva samples, non -human reads were removed before running hifiasm to generate the base \nassembly. This was performed by running Centrifuge34 on the FASTQ input file (see Supplementary Note 4 ). \nAdditionally, non-human contigs were appended to the reference assembly and boringbits during each cornetto \niteration, which facilitates rejection of nonhuman DNA during subsequent sequencing. To do this, non -human \ncontigs must be identified by assembling all the reads in the base assembly (both human and non-human) with \nhifiasm, and then running Centrifuge on the assembly. Any contig in this assembly not assigned with the Homo \nsapiens species code and co vered by a minimum of 100 reads are included (see Supplementary Note 4 ). \nCentrifuge index used was Bacteria, Archaea, Viruses, Human (compressed) index (p_compressed+h+v). \nEvaluating genome assemblies \nTo evaluate human genome assemblies, all contigs are first aligned to the Q100 T2T -hg002 \n(https://github.com/marbl/HG002) paternal haplotype with chrX added, using minimap2 2.24 with preset asm5 \nand --eqx -c options. Any contigs in the assembly whose sum of aligned lengths for ‘-’ is greater than for ‘+’ with \nrespect to the reference contigs, are reverse complemented. Dot plots are generated using the minidot tool in \nthe miniasm repository35. Telomeres are identified in assembled contigs using the telomere analysis script from \nthe VGP project  with default options.  To obtain per-chromosome metrics of an assembly, each contig in the \nthe assembly is assigned to the corresponding contig in the hg002-paternal reference based on the contig that \nwas most aligned with. Contig L 90 values are defined as the number of contigs encompassing >90% of the \nreference sequence for a given chromosome. A chromosome is considered to be complete if it has a L90 = 1 and \nhas a telomere detected at both contig ends. All these operations are carried out using wrapper scripts provided \nin the Cornetto repository (see Supplementary Note 4). \nTo evaluate assembly contiguity, we generated Nx plots by calculating contig sizes and cumulative assembly \nsize for each additional contig, sorted from largest to smallest. N50 and N90 values are the minimum contig size \nfor which 50% and 90%, respectively, of the genome is assembled into contigs larger than N Mbase. \nCompleasm 0.2.636 was used for calculating the BUSCO scores with lineage set to primates for human; \nactinopterygii_odb10 for cichlid; and, tetrapoda_odb10 for both birds and turtles. Yak (v0.1) was used to \ncalculate the QV, hamming error and switch error rates for assemblies. For QV value, separate k-mer count \nindexes are created using yak count with k-mer sizes 21 and 31 (-k option) using the Q100 T2T-hg002 \nreference genome which includes both paternal and maternal haplotypes. These indexes are used with yak qv \nsubtool to calculate the QV. For calculating hamming and switch errors, first the parental yak indexes for \nHG002 were downloaded from the human-pangenomics project (https://s3-us-west-2.amazonaws.com/human-\npangenomics/index.html?prefix=submissions/6040D518-FE32-4CEB-B55C-504A05E4D662--\nHG002_PARENTAL_YAKS/HG002_PARENTS_FULL/). Then yak trioeval was used. Example commands are provided in \nSupplementary Note 4. \nDuring evaluation, primary assemblies the user may optionally refine the assembly by retaining complete \nchromosomes from earlier cornetto cycles, which are sometimes broken during subsequent cycles. The \nCornetto software contains a script to execute this, using the following logic. Suppose the base assembly is \ncalled asm-0.fasta and the cornetto iterations are named asm-1.fasta, asm-2.fasta, asm-3.fasta, ..., asm-n.fasta.  \nStarting from asm -1.fasta, asm-2.fasta, asm-3.fasta, ..., asm -n.fasta are iterated un til any contigs are found \nlonger than the expected minimum chromosome size and have telomeres in both ends (considered to be \n‘complete chromosomes’). Suppose complete chromosomes are found in asm -k.fasta. Now such contigs are \nextracted from asm -k.fasta int o a file called asm.fasta. Now starting from asm -(k+1).fasta, assemblies are \niterated till asm-n.fasta (including asm-n.fasta). At each iteration, any complete chromosomes in the assembly \nare mapped to asm-k.fasta. Any newly found complete chromosomes are appended to asm.fasta (those contigs \nwhich map <50% of their length to a contig into asm.fasta). At the last iteration (asm-n.fasta), any other contigs \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 4, 2025. ; https://doi.org/10.1101/2025.03.31.646505doi: bioRxiv preprint \n\n \n11 \n(that are not considered complete chromosomes) are also mapped to asm.fasta. Any contigs which map <50% \nof their length to a contig are appended into asm.fasta. At the end, asm.fasta is the final curated primary \nassembly. \n \nDATA AVAILABILITY \nRaw sequencing data is deposited at ENA project PRJEB86853 and will be made public at the time of publication. \nRaw datasets for human participants may be accessed upon reasonable request. Genome assemblies are \navailable at Dryad ( 10.5061/dryad.kkwh70sfr) and will be made public at the time of publication. Genome \nassemblies for human participants may be accessed upon reasonable request. The following publicly accessible \ndatasets were also used in this study: \nhg002 PacBio HiFi data from the T2T Consortium: \nhttps://s3-us-west-2.amazonaws.com/human-\npangenomics/T2T/scratch/HG002/sequencing/hifirevio/m84005_220827_014912_s1.hifi_reads.fastq.gz  \nhg002 ONT duplex data from the T2T Consortium: \nhttps://human-pangenomics.s3.amazonaws.com/index.html?prefix=submissions/0CB931D5-AE0C-4187-8BD8-B3A9C9BFDADE--\nUCSC_HG002_R1041_Duplex_Dorado/Dorado_v0.1.1/stereo_duplex/ \nT2TC-ONT-only assembly (Koren et al 50x duplex + 30x UL + PoreC ) assembly:  \nhttps://obj.umiacs.umd.edu/marbl_publications/duplex/HG002/asms/duplex_50x_30xUL_poreC.tar.gz \n \nCODE AVAILABILITY \nCornetto software is open source and freely available: https://github.com/hasindu2008/cornetto  \nAll original code has been deposited at Zenodo and is publicly available: 10.5281/zenodo.15075988. \n \nACKNOWLEDGEMENTS \nThe authors acknowledge the traditional custodians of the land upon which the orange-bellied parrot, western \nsaw-shelled turtle, Gould's petrel and redstriped eartheater cichlid reside, as well as the custodians of the \nhistoric range of each species. We thank Deborah Bower and Yuna Kim for involvement in specimen collection \nfor the turtle and petrel, respectively. We thank Priam Psittaculture Centre for sampling of the parrot. This \nproject was undertaken with services from the National Computational Infrast ructure (NCI). We thank Tim Ho \nfor expert technical support and allowing us to use Garvan Institute data science infrastructure sometimes in \nquite exotic ways. \nWe acknowledge the following funding support: Australian Medical Research Futures Fund grants, 2023126, \n2041648, 2025138, 2008249, National Health and Medical Research Council (NHMRC) grant 2035037 (to I.W.D.), \nAustralian Research Council (ARC) DECRA Fellowship DE230100178 and ARC Discovery Project DP230100651 (to \nH.G.). A.J.M was supported by a Queensland Health Advancing Clinical Research Fellowship. L.S. is supported by \nthe ARC Centre of Excellence in Innovations in Peptide and Protein Science (CE200100012). Work on the orange-\nbellied parrot was supported by Australian Biocommons which is enabled by National Collaborative Research \nInfrastructure Scheme via Bioplatforms Australia Threatened Species Initiative funding; DNRF143. The views \nexpressed herein are those of the authors and are not necessarily those of the Australian Government or the \nARC, NHMRC or MRFF. \n \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 4, 2025. ; https://doi.org/10.1101/2025.03.31.646505doi: bioRxiv preprint \n\n \n12 \nAUTHOR CONTRIBUTIONS \nH.G., H.R.P. & I.W.D. conceived the project. \nH.G., K.J., & I.W.D. developed the Cornetto software. \nI.S., J.M.H., M.R., T.R. & Y.H. performed laboratory experiments. \nI.S., D.Y., Y.H., A.J.M., E.S., K.R.K. & A.C.M. recruited, collected and processed patient samples. \nJ.M.H., L.R., L.W.S., C.J.H., L.S., O.B., R.C.R.N., L.A.S.N., A.L.C. & A.G. coordinated, collected and processed non-\nhuman samples. \nH.G., I.S., J.M.H., A.L.M.R., L.W.S., D.Y., H.C., H.R.P. & I.W.D. performed bioinformatics analysis. \nH.G., A.L.M.R., D.Y., A.C.M. & I.W.D. prepared the figures and tables. \nH.G., I.S. & I.W.D. wrote the manuscript, with contributions from all co-authors. \n \nDECLARATIONS \nI.W.D. manages a fee -for-service sequencing facility at the Garvan Institute and is a customer of Oxford \nNanopore Technologies and Pacific BioSciences but has no further financial relationship. H.G., A.L.M. and I.W.D. \nhave received travel and accommodation expenses from Oxford Nanopore Technologies. The authors declare \nno other competing financial or nonfinancial interests. \n \n  \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 4, 2025. ; https://doi.org/10.1101/2025.03.31.646505doi: bioRxiv preprint \n\n \n13 \nREFERENCES \n1. Li, H. & Durbin, R. Genome assembly in the telomere-to-telomere era. Nature Reviews Genetics 25, 658–670 (2024). \n2. Completing human genomes. Nat Methods 19, 629 (2022). \n3. Nurk, S. et al. The complete sequence of a human genome. Science 376, 44–53 (2022). \n4. Liao, W.-W. et al. A draft human pangenome reference. Nature 617, 312–324 (2023). \n5. Rhie, A. et al. 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Bioinformatics 39, (2023). \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 4, 2025. ; https://doi.org/10.1101/2025.03.31.646505doi: bioRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}