Nanopore adaptive sampling to identify the NLR gene family in melon (Cucumis melo L.)

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Javier Belinchon-Moreno, Aurelie Berard, Aurelie Canaguier, Véronique Chovelon, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4828883/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Feb, 2025 Read the published version in BMC Genomics → Version 1 posted 11 You are reading this latest preprint version Abstract Background Nanopore adaptive sampling (NAS) offers a promising approach for assessing genetic diversity in targeted genomic regions. Here we designed and validated an experiment to enrich a set of resistance genes in several melon cultivars as a proof of concept. Results We showed that, using a single reference, each of the 15 regions we identified in two newly assembled melon genomes (ssp. melo ) was also successfully and accurately reconstructed in a third ssp. agrestis cultivar. We obtained fourfold enrichment regardless of the tested samples, but with some variations according to the enriched regions. The accuracy of our assembly was further confirmed by PCR in the agrestis cultivar. We discussed parameters that could influence the enrichment and accuracy of NAS generated assemblies. Conclusions Overall, we demonstrated that NAS is a simple and efficient approach for exploring complex genomic regions. This approach facilitates resistance gene characterization in a large number of individuals, as required when breeding new cultivars suitable for the agroecological transition. Melon Nanopore adaptive sampling targeted sequencing resistance genes NLR genome assembly Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Background The correct assembly of complex and highly repeated genome regions, which are especially prevalent in plants, remains a challenge. Short-read sequencing is a cost-effective method that is widely used in whole genome sequencing (WGS) approaches, yet it is ineffective in these regions as the read length is not sufficient for proper assessment of repeated elements, copy-number variations and duplication events [ 1 ]. Long-read technologies, such as those developed by Oxford Nanopore Technologies (ONT, Oxford, UK), have demonstrated their potential in accurately resolving complex regions as they are able to span long repetitive elements or areas with tandemly repeated genes [ 2 , 3 ]. Whole genome long-read sequencing is nevertheless still too costly for most studies, particularly when numerous genotypes have to be sequenced to highlight a few specific regions of interest. Targeted sequencing approaches offer a valuable alternative for characterizing specific genomic regions while reducing sequencing and data storage costs as compared to WGS [ 4 ]. Current targeted sequencing protocols have been adapted for long-read sequencing and mainly involve hybridization capture [ 5 ], PCR amplification [ 6 ], Cas9-assisted targeting [ 7 ] or microfluidic-based droplet sorting [ 8 ]. However, these approaches require substantial experimental and design efforts, along with substantial prior knowledge of the sequence to be enriched and its genotypic diversity [ 7 , 4 ]. PCR-based techniques are particularly prone to introducing bias in the enriched sequences, and long amplicons are hard to consistently amplify [ 4 ]. Hybridization-based methods require the construction of complex RNA libraries alongside very specific hybridization and capture conditions [ 4 ]. Cas9-based methods, e.g. Nanopore Cas9-targeted sequencing (nCATS) [ 7 ] or Cas9-Assisted Targeting of CHromosome segments (CATCH) [ 9 ], require the design of multiple guide-RNAs, which may be a challenging task when dealing with complex and repetitive genome regions. Finally, microfluidic-based methods like Xdrop [ 8 ] are highly complex and require specialized microfluidic equipment [ 4 ]. The Nanopore adaptive sampling (NAS) approach recently developed by ONT overcomes these limitations. NAS was first suggested in 2016 [ 10 ] and has been implemented with different algorithms since late 2019 [ 11 , 12 , 13 , 14 ]. This strategy takes advantage of the ability of the pores to control the directional flow of the DNA strand being sequenced through alterations in the applied current polarity. By combining live calling of sequenced bases with real-time mapping to a set of DNA sequences provided by the user for enrichment, the DNA strand may be dynamically discarded or fully sequenced based on the similarity of its initial first few hundred bases to the provided reference [ 10 ]. NAS just requires standard library preparation, while overcoming the need for DNA amplification, laborious and expensive experimental design or probe synthesis, and it offers real-time selective enrichment [ 15 , 16 ]. NAS has been used in clinical settings and for metagenomic sample enrichment [ 17 , 15 , 18 , 19 , 20 , 21 , 22 ]. NAS is therefore a promising approach for studying target regions, especially those that are highly complex, such as disease-associated repeat loci in humans [ 16 , 23 ]. In plants, immunity is encoded by resistance genes (R genes), frequently organized in complex regions [ 24 ]. Among R genes, Nucleotide-binding site leucine-rich repeat resistance genes (NLRs) form the largest family [ 25 ]. These genes encode intracellular receptors that play a central role in the so-called effector-triggered immunity (ETI) against pathogens. NLR genes exhibit a highly conserved structure with three main domains [ 25 , 26 ]: the N-terminal domain, the central domain, and the C-terminal domain. The N-terminal domain can be a Toll/Interleukin-1 receptor (TIR), a Coiled-coil (CC), or a resistance to Powdery Mildew 8-like (RPW8) domain. The central domain, the most conserved one, is a nucleotide-binding adaptor (NB-ARC), also named as NBS (nucleotide-binding site) domain. This domain plays a crucial role in signal transduction. Finally, the C-terminal domain is often composed of leucine-rich repeats (LRR) with ligand-binding functions. A clustered genomic arrangement is a common characteristic of NLR genes [ 27 ]. These clusters often result from unequal crossing overs, tandem duplications, or intra-cluster rearrangements [ 25 ]. In this context, NAS, combining long-read sequencing and target enrichment, should allow the accurate characterization of NLR clusters in plants. We selected melon ( Cucumis melo L.) as a model to investigate the ability of NAS to efficiently sequence a complete set of NLR clusters within a species (or NLRome). The melon genome features: i/ a small genome size; ii/ an NLR content estimated to account for ≈ 1% of the genome [ 28 ], which is in line with ONT target size recommendations [ 29 ]; and iii/ a finely characterized, highly variable complex NLR cluster, Vat [ 30 , 31 ], that is suitable for benchmarking. Among the accessions that are well characterized with regard to the Vat region, we chose Anso77 (ssp. melo ) because it features the highest number of functional Vat genes [ 30 ]. We also selected Doublon (ssp. melo ) as an accession whose Vat region structure contrasts with that of Anso77 [ 30 ]. We assembled and annotated their whole genomes and selected Anso77 as reference for identifying regions of interest (ROIs) for NAS. The performance of the method in capturing a set of NLR clusters in Anso77 and Doublon was assessed. Furthermore, we extended our assessment to an accession of a different subspecies (Chang-Bougi, ssp. agrestis ) for which open-access genomic data was available [ 32 ]. Materials and Methods BIOLOGICAL MATERIAL We selected Anso77, Doublon and Chang-Bougi melon cultivars to develop a proof of concept for the NLRome adaptive sampling experiment, with Anso77 serving as the reference cultivar. These cultivars originated from Spain, France and Korea, respectively. Anso77 and Doublon were chosen as ssp. melo lines belonging to the inodorus and cantalupensis botanical groups. Chang-Bougi, belonging to ssp. agrestis and specifically to the makuwa botanical group, was selected as a cultivar distantly related to Anso77 and Doublon. The aim of this choice was to validate the NAS procedure with cultivars differing markedly from the selected reference. Moreover, a draft genome assembly for Chang-Bougi constructed via Illumina HiSeq reads was readily available [ 32 ]. We obtained the seeds from the INRAE Centre for Vegetable Germplasm in Avignon, France [ 33 ], and grew them under greenhouse conditions at the INRAE GAFL research unit, Avignon, France. ANSO77 AND DOUBLON DE NOVO WHOLE GENOME SEQUENCING, ASSEMBLY AND ANNOTATION We produced whole de novo Anso77 and Doublon genome assemblies using long-read sequencing: ONT for Anso77 and ONT combined with PacBio (Pacific Biosciences, Menlo Park, CA, USA) for Doublon. Raw reads were already deposited in the NCBI database under the following Bioproject accession numbers: PRJNA662717 and PRJNA662721 [ 30 ]. BioNano optical maps (BioNano Genomics, San Diego, CA, USA), 10x Linked-Reads (Pleasanton, CA, USA) for Anso77, Illumina Novaseq short-read sequencing (Illumina, San Diego, CA, USA), and linkage map information were developed and used to construct the genome assemblies. We performed gene prediction on the assembled genomes using two independent procedures: A combination of ab initio , homology-based and transcriptome-based methods implemented with EuGene v. 4.3 [ 34 ]; and an ab initio approach using deep-learning implemented with Helixer [ 35 ]. Functional annotation of predicted genes was performed using the EggNOG-mapper v. 2.1.12 suite [ 36 ]. The fully detailed methods and parameters used for the assemblies and annotations are provided in Additional file 1: Supplementary Methods. NAS ENRICHMENT PANEL DEFINITION AND EXPERIMENTAL DESIGN We used Anso77 cultivar as reference for constructing the target regions for the NAS approach. We predicted the presence of NLR-related genes using NLGenomeSweeper [ 37 ] with default parameters. This tool approximates the presence of NLR genes via identification of the well-conserved NBS domain. We defined the regions of interest (ROIs) by grouping the predicted NBS domains separated by regions < 1 Mb. We ensured that there would be robust read depth coverage on the selected ROIs by adding a 20 kb buffer zone flanking the ROIs to constitute the initial target regions. We performed RE annotation in the initial target regions using the CENSOR tool available on the curated GIRI Repbase website [ 38 ]. Predicted REs > 200 bp were excluded from the initial target regions. Moreover, sequences < 500 bp located between them were also excluded, as adaptive sampling requires ≈ 500 bp to accept or reject the DNA strand. Figure 1 provides definitions of the ROIs, target regions and target regions without REs. We input these target regions without REs in bed format (Additional file 3) and the reference genome of Anso77 in fasta format in the MinKNOW software platform (ONT, Oxford, UK). These files were used for read acceptance or rejection. If the initial ~ 500 bp of the DNA strands matched the target regions without REs they underwent complete sequencing, otherwise they were ejected from the pore. DNA EXTRACTION, ADAPTIVE SAMPLING SEQUENCING AND BASE CALLING Plant leaves were harvested and immediately frozen in liquid nitrogen for subsequent DNA extraction. Genomic DNA was extracted using the NucleoSpin Plant II kit (Macherey-Nagel, Germany) according to the manufacturer’s protocol. DNA quantity and quality assessments were conducted using the Qubit4® 1x dsDNA BR Assay Kit (Invitrogen, Carlsbad, CA, USA) and the Agilent 2200 TapeStation system (Agilent Technologies, Santa Clara, CA, USA). We multiplexed and sequenced Anso77 and Doublon DNA on a single PromethION R10 .4.1 flowcell (ONT, Oxford, UK). Half the channels were used as control channels in which no adaptive sampling was performed. Moreover, we sequenced Chang-Bougi DNA using one tenth of a PromethION R10 .4.1 flowcell to assess the NAS flexibility and scalability. We prepared the sequencing libraries using the Native Barcoding Kit 24 V14 (SQK-NBD114.24) (ONT, Oxford, UK) according to the ONT guidelines with some modifications. One microgram of genomic DNA from each sample was repaired and end-prepped with 20 min incubation at 20°C followed by a heat-inactivation of the enzymes at 65°C for an additional 20 min. DNA was purified and barcodes were individually ligated to each of the purified DNA samples. NB01 and NB02 barcodes were used for Anso77 and Doublon, and NB22 was used for Chang-Bougi. Barcoded samples were purified using AMPure XP beads (Beckman Coulter Inc., Brea, CA, USA) at a ratio of 0.4:1 beads-to-barcoding mix, while keeping each barcoded sample in an independent Eppendorf tube. Finally, purified barcoded DNA samples were pooled at equimolar concentration to obtain a total volume of 30 µl. Adapters were ligated to the pooled samples. After purification, DNA sizes and concentrations in the barcoded pools were quantified using the Agilent 2200 TapeStation system and Qubit4® 1x dsDNA HS Assay Kit (Invitrogen, Carlsbad, CA, USA), respectively. Final libraries were adjusted to a volume of 32 µl containing 10–20 fmol of DNA. All incubations < 10 min were extended to 10 min. We supplemented the libraries by adding the Sequencing Buffer (ONT, Oxford, UK) and Library Loading Beads (ONT, Oxford, UK), and subsequently loaded them into R10.4.1 PromethION flowcells for 96 h runs for Anso77 and Doublon, or 120 h runs for Chang-Bougi. Library reloading (washing flush) was performed in all of the experiments when the percentage of sequencing pores dropped to 10–15%. NAS was performed using PromethION flowcell channels 1-1500, while the remaining channels served as control. The sequencing speed was set at 260 bp (accuracy mode) for Anso77 and Doublon, and the quality score threshold was set at 10. For Chang-Bougi, NAS was performed on the whole flowcell and the sequencing speed was modified to 400 bp (default mode) because the 260 bp option has been deprecated from MinKNOW version 23.04. Raw ONT FAST5 files were live base-called during the PromethION run with Guppy (ONT, London, UK) v. 6.3.9 for Anso77 and Doublon and Guppy v. 6.5.7 for Chang-Bougi in “super accurate base-calling” mode. Barcodes were automatically trimmed using the “trim barcodes” option of the MinKNOW software v. 22.10.7 for Anso77 and Doublon, and v. 23.04.5 for Chang-Bougi. For each run, the automatically generated “sequencing_summary.txt” file and the FASTQ files of the samples were retained for further processing. NAS DATA PROCESSING AND ENRICHMENT CALCULATION For Anso77 and Doublon, we split the reads by channel, thereby generating two FASTQ files per sample: one with the reads sequenced on channels 1-1500 for NAS, and another with the reads generated on channels 1501–3000 for WGS. No splitting by channel was performed for Chang-Bougi as all of the channels were used for NAS. Reads with a “PASS” flag, i.e. their quality score was > 10, were retained for downstream analysis. We identified the NAS-rejected reads based on their “end reason” in the sequencing summary file. This file included the generated read classifications based on their “end reason”. In this way, reads rejected by the adaptive sampling were labeled as “Data Service Unblock Mux Change”. Reads labeled as “Unblock Mux Change”, “Mux Change” and “Signal Negative” were also filtered out. Thereafter we filtered the generated FASTQ files by size, keeping reads > 1 kb. We assessed statistics on these FASTQ files using seqkit stats v. 2.4.0 [ 39 ]. We computed sequence depth statistics for Anso77 and Doublon by aligning the reads to their reference whole genome assemblies with minimap2 v. 2.24-r1122 [ 40 ] and using mosdepth v. 0.3.3 [ 41 ], with a bed file containing the coordinates of the 15 target regions. For Chang-Bougi, sequence depth statistics were calculated by aligning the reads to their assembled target regions. The split flowcell setup allowed us to calculate the extent of enrichment in Anso77 and Doublon by comparing the read depths generated in NAS and WGS. We assessed the NAS efficiency using the two following enrichment measurements. Enrichment by yield, i.e. the ratio of the on-target sequence depth (NLR cluster + 20 kb flanking) with NAS to that with WGS, was assessed as follows: $$\:Enrichment\:by\:yield=\:\frac{{depth}_{region\_NAS}}{{depth}_{region\_WGS}}$$ 1 where depth region_NAS and depth region_WGS represent the on-target sequence depth in the NAS and WGS experiments, respectively. Enrichment by selection, i.e. the ratio of the relative selection of the target regions between NAS and WGS, measures the extent to which NAS can alter the abundance of the given target regions within a complete genome, while considering the sequencing behavior of each ROI. This was calculated as follows: $$\:Enrichment\:by\:selection=\:\frac{\raisebox{1ex}{${depth}_{region\_NAS}$}\!\left/\:\!\raisebox{-1ex}{${depth}_{chr\_NAS}$}\right.}{\raisebox{1ex}{${depth}_{region\_WGS}$}\!\left/\:\!\raisebox{-1ex}{${depth}_{chr\_WGS}$}\right.}$$ 2 where depth region_NAS and depth chr_NAS represent, respectively, the sequence depth on-target (NLR cluster + 20 kb flanking) and on the rest of the chromosome in the adaptive sampling approach, while depth region_WGS and depth chr_WGS represent the depth coverage on-target (NLR cluster + 20 kb flanking) and on the rest of the chromosome in the WGS approach. The relative selection with WGS should be equal to one if there is no bias that could cause the target regions to be differentially enriched compared to the rest of the genome. We calculated the average enrichment by yield between all target regions as the ratio of the average sequence depth on-target in NAS and WGS. Similarly, we assessed the average enrichment by selection between all target regions as the ratio of the average relative frequency of target regions in NAS and WGS. The average relative frequencies of target regions were calculated as the ratio of the average sequence depth on-target and off-target. Only chromosomes containing target regions were considered when calculating the off-target average sequence depth. TARGET REGION ASSEMBLY, NLR ANNOTATION AND QUALITY CONTROL We tested a set of assemblers tailored for ONT sequencing data, including Canu [ 42 ], Flye [ 43 ], Shasta [ 44 ], Necat [ 45 ], Raven [ 46 ] and SMARTdenovo [ 47 ], for assembling the NAS reads (data not shown). SMARTdenovo was primarily selected due to its superior target region assembly metrics (contiguity and assembly errors), achieved within a short time and with low memory usage. For Chang-Bougi, one target region was selected from the Canu assembly, as SMARTdenovo failed to collapse a repeat region generating two contigs instead of a single one. We used default parameters and added the “generate consensus” option for SMARTdenovo v. 2018.2.19. Canu v. 2.2 was executed with the “genomesize = 7m –corrected –trimmed –nanopore” options, and using reads > 8 kb. For each assembly, we filtered the contigs and only kept those that included at least one predicted NBS domain or matched more than 15 kb with at least 45% identity to any of the 15 target regions of Anso77. We assessed NBS domain prediction using NLGenomeSweeper. We used the nucmer and delta-filter commands of MUMmer v. 4.0.0rc1 [ 48 ] to select contigs matching the Anso77 target regions. Nucmer was used with the –l 100 option, keeping the rest of the parameters as default. Hits reported by nucmer were filtered with delta-filter with the -r -q -l 15000 -i 45 options. We ran QUAST v. 5.0.2 [ 49 ] to assess the basic statistics of the generated filtered assemblies. Assembly errors were analysed by focusing on the well-studied Vat region. We performed manual annotations of the Vat regions as described by Chovelon et al. [ 30 ]. The accuracy of the Vat homologs was analysed using two PCR markers: Z649 FR, which indicates the number of R65aa motifs in Vat homologs [ 30 ], and Z1431 FR which is specific to a Vat homolog with four R65aa motifs [ 31 ]. Figure 2 . Workflow diagram summarizing the different steps involved in data processing and target regions assembly. For Chang-Bougi, only the NAS way was followed, and the split by channel step was omitted. Results ANSO77 AND DOUBLON DE NOVO GENOME ASSEMBLIES AND ANNOTATION The metrics of the different genome assembly steps are detailed in Additional file 1: Supplementary Data. Key metrics of the final assemblies are summarized in Table 1 . In summary, initial contig assemblies provided 159 and 186 contigs for Anso77 and Doublon, with long N50 values of 8.9 and 15.2 Mb. Both genomes were organized in 12 chromosomes and presented a total size of 369.47 and 362.63 Mb. From this size, 12.84 and 12.92 Mb corresponded to unscaffolded contigs or unoriented scaffolds. BUSCO results showed that 98.5 and 94.4% of the single-copy orthologs were completely present in the Anso77 and Doublon assemblies. In addition, Anso77 and Doublon assemblies presented Merqury QVs of 31.21 and 42.52, proving their great quality and completeness. We predicted 32,714 and 33,404 genes in the Anso77 and Doublon assemblies using the EuGene annotation software. Using Helixer, we predicted 21,692 genes for Anso77 and 21,125 for Doublon. As the EuGene results are more consistent with those previously observed in the literature in terms of number of predicted genes (Additional file 2: Table S1 ), we kept this annotation as a basis. However, we noticed that Helixer works better in predicting NLR genes structure, since it annotated more precisely the intron-exon structure of the previously validated NLR genes ( Vat homologs, Fom-1 and Fom-2 ). For this reason, we selected the Helixer annotation for those genome regions where NLGenomeSweeper indicated the presence of NBS domains. In addition, we removed those ncRNA genes that did not include Rfam information. Therefore, our final annotation contained 31,152 genes for Anso77, and 31,865 genes for Doublon. From them, 29,714 and 30,198 were protein-coding genes. The average gene length was 3,398 bp for both accessions, with 4.46 and 4.29 exons/gene on average in Anso77 and Doublon, respectively. 28,627 genes were functionally annotated for Anso77, and 29,144 for Doublon. The gene models captured 95.2 and 91.6% of the BUSCOs for Anso77 and Doublon. Only 3.3 and 3% of the genes were found fragmented, and 1.5 and 5.4% were missing. Table 1 Summary metrics of the Anso77 and Doublon hybrid genome assemblies and annotations. Anso77 Doublon Chromosome assembly Number of chromosomes 12 12 Number of scaffolds 24 22 Min. scaffold length (Mb) 3.4 4.6 Max. scaffold length (Mb) 29.0 29.0 Total size of scaffolds (Mb) 356.6 349.7 Unplaced Number of scaffolds 6 6 Number of contigs (> 15 Kb) 57 60 Size of unplaced scaffolds 9.1 9.4 Size of unplaced contigs 3.7 3.4 Whole genome GC (%) 34 34 Merqury QV 31.2 42.5 BUSCO Complete genes 1,591 (98.5%) 1,524 (94.4%) BUSCO Duplicated genes 20 (1.2%) 16 (1.0%) BUSCO Fragmented genes 11 (0.7%) 12 (0.7%) BUSCO Missing genes 12 (0.8%) 78 (4.9%) Number of predicted genes 31,152 31,865 Number of functionally annotated genes 28,627 29,144 Number of predicted NBS domains 84 76 Number of predicted CC-NBS-LRRs 18 15 Number of predicted TIR-NBS-LRRs 24 23 Number of predicted RPW8-NBS-LRRs 1 1 Number of predicted NBS-LRRs 31 30 Number of predicted TIR-NBSs 4 1 Number of predicted NBSs 6 5 We predicted 84 and 76 NBS domains in the Anso77 and Doublon genomes, respectively, which was within the range of values previously reported for melon genomes (Additional file 2: Table S1 ). Based on InterProScan [ 51 ] domain identification in the 10 kb flanking sequence on both sides of the NBS domain, potential genes containing the predicted NBS domains were classified in different categories (Table 1 ). The accuracy of the NLR gene assemblies was assessed on the basis of the accuracy of the Vat homologs, whose cDNA sequence was previously obtained by Sanger sequencing [ 30 ]. For Anso77, the AN-Vat2 , AN-Vat3 and AN-Vat5 homologs fully matched the assembly generated here, while AN-Vat1 and AN-Vat4 contained one SNP each. For Doublon, all three Vat homologs fully matched our assembled sequence. NAS TARGET REGION CONSTRUCTION We arranged the 84 NLR predicted domains on Anso77 into 15 groups encompassing nine ROIs with 2–28 NBS domains and six ROIs with isolated NBS domains. We also found 15 groups and similar physical positions of the NLR genes on Doublon and on the previously published melon genomes. After adding the 20 kb flanking zones, the sizes of the 15 target regions ranged from ~ 41 to ~ 1,378 kb, representing a total length of ~ 6.16 Mb of the ~ 370 Mb Anso77 genome (~ 1.68%) (Table 2 ). After masking the REs, the final file input in the PromethION sequencer included 935 target regions, ranging from 502 bp to 67.609 kb in size (Additional file 2: Supplementary Data). They accounted for a total of ~ 5.23 Mb of the ~ 370 Mb Anso77 genome (~ 1.41%). Table 2 Detailed information about the 15 target regions of Anso77. Target region Chr Start position End position Size Predicted NBS domains Region 01 01 32,471,836 32,684,765 212,929 14 Region 02 02 979,011 1,026,074 47,063 2 Region 03 02 15,072,686 15,113,435 40,749 1 Region 04 04 5,452,467 5,493,242 40,775 1 Region 05 04 26,516,353 27,894,199 1,377,846 7 Region 06 05 14,002,942 14,043,763 40,821 1 Region 07 05 17,364,466 18,340,871 976,405 2 Region 08 05 25,108,836 26,146,869 1,038,033 28 Region 09 06 5,837,171 5,877,998 40,827 1 Region 10 07 2,550,415 2,591,165 40,750 1 Region 11 07 24,326,063 24,411,978 85,915 4 Region 12 08 3,465,204 3,505,984 40,780 1 Region 13 09 645,676 815,779 170,103 10 Region 14 09 6,558,754 7,597,791 1,039,037 4 Region 15 11 6,635,285 7,601,705 966,420 7 The Vat region is included within region 08. NAS TARGET REGION VALIDATION: EFFECTIVE ENRICHMENT OF NLR CLUSTERS IN MELON Over 8.62 million reads and 19.86 Gb (42.71%) were assigned to Anso77, and over 11.97 million reads and 26.63 Gb (57.27%) were assigned to Doublon after barcode trimming. We compared NAS to WGS of Anso77 and Doublon in terms of their general metrics. For Anso77, further read splitting by channel and filtering of this data by quality, “end reason” and length resulted in 110.06 K reads and 1.14 Gb derived from the NAS half-flowcell. The other half-flowcell (WGS) yielded 1.12 million reads, with a cumulative size of 11.93 Gb for Anso77. Using the same processing for Doublon, 163.84 K reads generating 1.56 Gb from were assigned to the NAS half-flowcell, while 15.81 Gb from 1.70 million reads were assigned to the WGS part. For both Anso77 and Doublon, the N50 value from the filtered NAS reads was very similar to that of the filtered WGS reads. All information on the generated datasets is presented in Table 3 . Table 3 Anso77, Doublon and Chang-Bougi sequencing metrics. Dataset Cultivar Sequences number Size (bp) Mean length (bp) Max. length (bp) N50 Q20 (%) Q30 (%) Total dataset Anso77 8,628,783 19,860,863,026 2,302 174,519 13,263 82.17 72.04 Doublon 11,976,866 26,639,849,880 2,224 193,630 11,968 83.65 73.58 Chang-Bougi 3,320,000 3,454,320,986 1,041 467,827 883 76.23 58.39 Total WGS “pass” Anso77 1,423,908 13,579,603,804 9,536 143,366 16,713 87.55 77.18 Doublon 2,033,986 18,285,705,305 8,990 137,509 15,261 87.55 77.33 Total NAS “pass” Anso77 6,634,798 4,807,120,593 725 126,590 617 87.89 77.41 Doublon 9,407,579 6,779,872,639 721 112,867 614 88.01 77.78 Chang-Bougi 3,056,000 3,115,590,337 1,020 232,918 878 82.81 64.01 Filtered WGS Anso77 1,122,605 11,939,444,337 10,636 143,366 16,772 88.00 77.75 Doublon 1,704,828 15,807,284,512 9,272 137,509 15,120 88.08 78.00 Filtered NAS Anso77 110,061 1,147,896,611 10,430 123,373 16,911 88.17 78.03 Doublon 163,843 1,561,078,862 9,528 112,867 15,182 88.09 77.99 Chang-Bougi 96,626 803,580,422 8,316 122,918 13,749 81.40 66.60 Anso77 and Doublon were sequenced using both NAS and WGS, while Chang-Bougi was only sequenced using NAS. Total WGS “pass” and total NAS “pass” represent the reads having a “PASS” flag (quality over 10) and assigned to the WGS and NAS half-flowcell, respectively. Filtered WGS and filtered NAS represent the two sets of reads just mentioned after filters of “end reason” and length. The length distribution of NAS-generated reads peaked at around 500 bp, corresponding to reads rejected by adaptive sampling (91.20% and 92.20% of the total “pass” reads for Anso77 and Doublon) (Fig. 3 A). When rejected reads were out-filtered, the length distribution profile was similar for reads obtained for both cultivars in WGS and in NAS (Fig. 3 B, C). When focusing on Anso77, we evaluated the read depth on the target regions and on the rest of the chromosome in the NAS approach. The sequence depth on the target regions at the end of the experiment was much higher than on the rest of the chromosome (Fig. 4 ), with an average frequency of target regions of 63.37. The sequence depth remained stable throughout the entire ROIs, even more so when the ROIs had a smaller size (Fig. 4 A-C). The standard deviation of the sequence depth in the ROIs ranged from 1.38 for region 03 to 20.19 for region 07, with values of 10.29, 16.15 and 1.80 obtained for regions 01, 08 and 12, respectively. The increase in sequence depth was gradual on the 20 kb flanking regions, with the highest depth obtained in the ROIs. The increase had a similar pattern regardless of the ROIs size (Fig. 4 D). The half-flowcell design allowed us to calculate the enrichment obtained in NAS compared to the WGS approach. We obtained enrichment by yield for Anso77 that varied among the target regions, i.e. ranging from 2.45 to 5.18 at the end of the run (Fig. 5 A). Otherwise, we obtained increased enrichment by selection ranging from 45.56 to 102.91 (Fig. 5 B). On average, we obtained enrichment by yield and by selection of 3.96 and 78.38, respectively (Table 4 ). Target regions were sequenced at a lower rate than the rest of the genome in WGS, with a relative frequency of 0.81 (Table 4 ). We also noted that both enrichment by yield and by selection followed very similar temporal patterns. This enrichment peaked at the beginning of the run for most of the regions when most of the flowcell channels were actively sequencing, and then it decreased over time. This trend implies that channel inactivation occurred faster on the NAS half-flowcell. Region 10 exhibited a very particular behavior, i.e. it was extremely enriched at the beginning of the run (Fig. 5 A, B). As shown in Additional file 1: Figure S1 A, this high enrichment during the first hours of the run corresponded to poor sequencing in the WGS approach. Furthermore, within a period of just 10 h, the NAS approach provided a sequence depth comparable to that achieved in the entire WGS run (Additional file 1: Figure S1 A). In addition, the washing flush contributed to an increase in pore activity in both the NAS and WGS approaches (Additional file 1: Figure S1 A). We sought to confirm the applicability of NAS in targeting the entire spectrum of NLR clusters in melon by extending its use to Doublon, i.e. a cultivar of the same subspecies as Anso77 but belonging to a distinct botanical group. Similar to Anso77, the NAS sequence depth on the target regions at the end of the run was always 2.25 to 4.65-fold greater than that obtained with the WGS approach (Fig. 5 C, D). The least (region 06) and most (region 10) enriched regions were the same for both cultivars. Notably, the enrichment by yield presented a Kendall’s coefficient of concordance (W) of 0.87 when comparing Anso77 and Doublon (p 0.04), suggesting region-specific patterns rather than cultivar-based differences. Target regions of Doublon were sequenced and mapped with a ratio (0.98) identical to that of the rest of the genome in WGS (Table 4 ). Regarding enrichment by selection in Doublon, we obtained values ranging from 43.52- to 83.89-fold, representing a significant correlation with the values previously obtained for Anso77 (W = 0.89; p 0.04). Overall, we demonstrated an average enrichment by yield of 3.73 for all the target regions and an average enrichment by selection of 69.92 (Table 4 ). In terms of the temporal enrichment patterns, the results were consistent with those obtained for Anso77 regarding both enrichment by yield and by selection (Fig. 5 C, D). Table 4 Summary statistics of the NAS and WGS runs for Anso77 and Doublon. Anso77 Doublon WGS NAS WGS NAS Average on-target depth 22.79 90.18 31.74 118.28 Average off-target depth 28.19 1.42 32.52 1.73 Relative frequency of target regions 0.81 63.37 0.98 68.21 Average enrichment by yield 3.96 3.73 Average enrichment by selection 78.38 69.92 Only the chromosomes including target regions were considered for calculating the average off-target depth. NANOPORE ADAPTIVE SAMPLING FACILITATES THE CORRECT ASSEMBLY OF NLR CLUSTERS IN ANSO77 AND DOUBLON GENOMES NAS-enriched reads provided very contiguous and accurate assemblies of the target regions in both Anso77 and Doublon. Each cultivar presented a single contig per target region. The NAS assembly metrics are outlined in Table 5 . Notably, the size of all contigs was over that of their corresponding target regions. Table 5 Anso77 and Doublon NAS assembly metrics. NAS reference file Anso77 Doublon Chang-Bougi Number of sequences 15 15 15 16 Assembly total size 6,158,453 7,022,836 7,137,869 6,684,108 %GC 33 33 33 33 Region 01 212,929 267,987 288,544 283,266 Region 02 47,063 111,515 145,599 103,652 Region 03 40,749 92,325 109,144 62,015 Region 04 40,775 88,396 91,470 65,369 Region 05 1,377,846 1,442,885 1,469,924 1,422,416 Region 06 40,821 80,095 94,491 87,574 Region 07 976,405 1,030,491 1,002,692 877,386 Region 08 1,038,033 1,109,994 1,047,373 1,011,255 Region 09 40,827 93,035 116,040 76,550 Region 10 40,750 95,015 127,865 97,965 Region 11 85,915 132,117 154,578 146,542 Region 12 40,780 127,510 110,582 93,626 Region 13 170,103 218,950 239,877 233,624 Region 14 1,039,037 1,106,869 1,072,236 1,052,862 Region 15 966,420 1,025,652 1,067,454 82,354 & 987,652 Regions of the NAS reference file represent the size (bp) of the target regions provided to the sequencer. Regions of Anso77, Doublon and Chang-Bougi represent the size (bp) of the contigs matching those regions. We generated dot plots for Anso77 and Doublon comparing each assembled contig with the corresponding region in the whole genome assemblies we produced (Additional file 1: Figure S2 ). There was perfect collinearity in the dot plot of each target region, thereby confirming the accuracy of the NAS assemblies with regard to the reference genomes. Notably, the same number of NBS domains at the same positions were predicted in the NAS assembly compared to the reference genome for both cultivars. We focused on the well-known Vat region to further assess the accuracy of the NAS assemblies Dot plots representing the Vat regions are shown in Fig. 6 . The dot plots highlighted the complexity of this area with numerous duplicated sequences, but a perfect diagonal was noted between the reference and NAS-assembled sequences. Moreover, we checked the sequence of the Vat homologs previously sequenced by Sanger sequencing (cDNA sequencing). Among the five Anso77 homologs, AN-Vat1, AN-Vat2 , AN-Vat3 and AN-Vat5 fully matched the assembly generated from the NAS library, while AN-Vat4 contained one SNP in the first exon (G/T on position 402710). This overcomes the reference assembly presented here, which contained one SNP in AN-Vat4 as well as one SNP in AN-Vat1. For Doublon, the three Vat homologous presented 100% DNA sequence similarity with the NAS assembly. Overall, we demonstrated that NAS produced very contiguous and accurate assemblies in highly complex resistance gene clusters. TOWARDS A BENCHMARK PROCEDURE: ENRICHMENT AND ASSEMBLY OF NLR CLUSTERS FROM A DISTANT CULTIVAR PROVIDE VERY VALUABLE STRUCTURAL INFORMATION We obtained 3.32 million reads and 3.45 Gb for Chang-Bougi in a 1/10 flowcell. After eliminating the reads rejected by adaptive sampling and filtering by quality and 1 kb length, 96.62 K target reads and 0.80 Gb were available for further processing. These reads exhibited an N50 of 13.75 kb, i.e. comparable to that obtained for Anso77 and Doublon. Rejected reads had an average length of ~ 790.52 bp, which was longer than that obtained for Anso77 and Doublon due to the updated sequencing speed (400 bp/s) in the last MinKNOW version. The sequence depth mapped on the assembled contigs of the target regions averaged 41.82X. As illustrated in Fig. 7 , this depth varied between regions, with the highest depth obtained in region 05 (50.37X) and the lowest in region 03 (21.08X). However, the sequence depth obtained between regions kept a significant concordance with that obtained for Anso77 and Doublon (Additional file 1: Figure S1 -left) (W = 0.78; p = 0.003). For Chang-Bougi, the NAS-enriched read assemblies obtained with SMARTdenovo resulted in 17 contigs. Notably, we observed an inversion of ≈ 100 kb in region 01 compared to Anso77 (Fig. 8 A). Region 13 and region 15 were fragmented into two contigs. Among the tested assemblers, Canu generated a contiguous assembly of region 13 in a single contig (Additional file 1: Figure S3 ). Consequently, we retained region 13 from the Canu assembly. No assembler succeeded in reconstructing region 15 into a single contig. We then investigated why region 15 was fragmented into two contigs, but we were unable to conclude after contig alignment to the published Illumina-based assembly due to its high degree of fragmentation. As Chang-Bougi belongs to the makuwa botanical group, we mapped the two contigs to the genomes in this group for which open-access data was available: Early Silver Line, Ohgon and Sakata's Sweet [ 52 ]. We identified a very large and size-conserved insertion (ranging from 862 to 871 kb) at the breakpoint between the two contigs obtained for Chang-Bougi (Fig. 8 A). No NBS domain was predicted in this insertion on the Early Silver Line, Ohgon and Sakata's Sweet genomes. We could not recover this insertion in Chang-Bougi as it was not present in the provided reference. The total assembly size was 6.68 Mb. Table 4 shows the detailed NAS assembly metrics. In the Chang-Bougi draft genome generated by Shin et al. [ 32 ], we identified 81 NBS domains spanning 18 contigs. These contigs matched the previously identified 15 ROIs of the NAS assembly with no extra clusters (Fig. 8 B). We identified 83 NBS domains in the NAS assembly. The two extra NBS domains predicted in the NAS assembly were located in region 08, one in the Vat region and the other outside. We manually annotated the Vat region of both Chang-Bougi assemblies (NAS and published) and some discrepancies were detected in the complex and repetitive area between Vat1 and VatRev (Fig. 9 A). In the NAS assembly, we identified the extra NBS domain within the Vat region as being a Vat homolog with four R65aa motifs (Fig. 9 A). We confirmed the presence and structure of this Vat gene with four R65aa motifs, as well as the presence of Vat genes with three R65aa through PCR using the published Z649FR and Z1431FR primers (Fig. 9 B). Finally, the presence of long reads encompassing the Vat1 : Vat2 and Vat2 : Vat3 gene pairs confirmed the NAS-assembled structure. Discussion The findings of the present study highlighted that NAS is a promising approach for studying polymorphism in complex genomic ROIs. We used melon as a model to select highly diverse ROIs in terms of size and NLR gene content. We generated two de novo assemblies and used one of them as reference for the adaptive sampling experiment. We selected the two accessions based on their different responses to the most studied pathogens at INRAE GAFL. Both de novo assemblies presented great quality and completeness, comparable to the best published melon assemblies to date [ 53 ]. Similarly, predicted gene content values were in the range of previously melon published assemblies (Additional file 2: Table S1 ). Several factors can influence the NAS efficiency, two of which we took into account prior to launching the NAS experiments. First, we hypothesized that there is a direct relationship between the ideal size of sequenced fragments and the ROI size (Fig. 10 ). Given that here the ROI sizes ranged from ≈ 41 to ≈ 1378 kb, we used standard DNA extraction (10–30 kb), which we expected would lead to a more stable sequencing depth in ROIs than ultra-long reads (100–300 kb) for the same yield. We felt that this approach would reduce off-target sequencing and avoid channel blockage when rejecting very long reads, as outlined in the ONT recommendations [ 29 ]. Second, REs span a major portion of the melon genome [ 54 ], and are especially common in NLR gene clusters [ 30 ]. To avoid sequencing off-target REs with high sequence similarity to those within the initial target regions, we assumed that masking repetitive elements within the provided target regions would reduce the quantity of off-target data, thereby enhancing enrichment. We masked 0.93 Mb of repetitive sequences over the entire 6.16 Mb target length. Masking REs in genomes prior to NAS has been previously suggested [ 55 ]. We assessed the efficacy of NAS compared to WGS. One key factor that may have a major effect on the final yield in ONT sequencing runs is the number of active channels at the beginning of the run and their lifespan, which may differ markedly between flowcells. Therefore we implemented a half-flowcell design to compare NAS and WGS with respect to eliminating biases that may arise when using two different flowcells, as previously revealed in many studies [ 15 , 22 ]. The results showed that for both Anso77 and Doublon, NAS produced about fourfold more on-target data than WGS (Table 4 ), while generating about tenfold less total data (see filtered NAS and WGS in Table 3 ). Yet the sequence depth between target regions was more variable in NAS than in WGS, but this did not compromise accurate ROI assembly in all the cultivars. We found no correlation between target size and sequence depth (Additional file 1: Figure S4 A) but there was a moderate correlation between the percentage of masking and the sequence depth, as expected (Additional file 1: Figure S4 B). We proposed two enrichment measurements adapted from previous studies in which NAS was tested on metagenomics samples or panels of many small genomic regions [ 56 , 15 ]: enrichment by yield, a simple widespread metric; and enrichment by selection, a metric that is not biased by the sequencing behavior of each target region. These enrichment measures made sense in our study because our goal was to increase the coverage in complex ROIs to generate more accurate assemblies. We achieved up to 3.7-fold average enrichment by yield and up to 69-fold average enrichment by selection, even when the reference was genetically distant from the sequenced accession. These findings were comparable to the best results obtained in previous studies involving the enrichment of individuals in metagenomics samples [ 15 , 22 ], and they were better than the enrichment values previously obtained with loci panels [ 56 ]. Our successful enrichment with NAS could have been linked to the percentage of the genome targeted here (~ 1.41%), the target sizes or the DNA fragment sizes, which have been demonstrated to be key factors in determining the enrichment rate [ 15 , 29 ]. In addition, the fact that we performed a late nuclease flush when the percentage of sequencing pores was around 10% rather than at a fixed time might have contributed to the good performance [ 57 , 15 , 58 ]. The temporal enrichment patterns of the different target regions were higher and more variable at the beginning of the run but then generally stabilized after 70 h (Fig. 5 ). Actually, channel inactivation occurred faster on the NAS half-flowcell, which could have been due to the repetitive potential flipping to reject off-target sequences or simply because the likelihood of channel clogging is statistically related to the number of sequenced molecules [ 12 , 15 ]. In previous studies, NAS has been used to enrich specific species in metagenomics samples [ 17 , 15 , 59 ] and relatively small sequences within an organism, e.g. panels of exons or of key variant loci [ 56 , 60 , 58 ]. Here we demonstrated the power of NAS as a tool for enriching ROIs representing isolated NLR genes or complex NLR gene clusters in a plant crop species. The correct assembly of these complex regions typically requires long reads, such as those generated by ONT sequencing, or dedicated laborious approaches such as the resistance gene enrichment sequencing (RenSeq) method [ 5 , 61 , 62 , 63 , 64 ]. In fact, NLR genes may sometimes be miss-predicted, especially when short-read sequencing technologies are used. Two to four functional NLR genes and some pseudo NRLs were found when deciphering the Vat region in the DHL92 genome [ 65 , 30 ]. When screening for these NLRs in the early released DHL92 genome, they were found to be misassembled. It was only when a high-quality genome (with long reads, optical maps or HiC) was released [ 54 ], that the Vat gene assemblies were finally in line with those obtained via Sanger sequencing using long-range PCR [ 30 ]. Moreover, we compared the Vat cluster in the Chang-Bougi cultivar derived from short WGS [ 32 ] and long NAS reads. We showed that the WGS assembly was erroneous in terms of the homologous gene numbers and sequences (Fig. 9 ) and that the NAS assembly accurately reconstructed the region. NLR genes are located in the dispensable portion of the genome [ 25 , 66 ], and therefore the NLR reference used for NAS should be carefully selected when targeting the NLRome of a species. Our predictions of the number of NBS domains in Anso77 and Doublon, alongside all previously published melon genome assemblies, consistently yielded similar values (Additional file 2: Table S1 ). In all cases, we did not detect more than 15 groups of NLR genes regardless of the subspecies assessed, i.e. melo or agrestis . These findings indicated a well-conserved number and location of NLR genes in melon. We chose Anso77—a Spanish cultivar belonging to ssp. melo and the inodorus botanical group—as reference for the NAS approach because it contained the highest number of Vat homologs within the Vat region used for benchmarking [ 30 ]. Our results obtained with Doublon and Chang-Bougi suggested that our strategy was suitable. Doublon is a French melon line belonging to ssp. melo , and the cantalupensis botanical group. When NAS was used without any short-read polishing, we obtained a Vat cluster with CDSs identical to those derived from an assembly using HW-DNA, PacBio and ONT long sequences, Illumina short sequences and optical maps. Chang-Bougi is a Korean melon line belonging to ssp. agrestis and the makuwa botanical group. The Vat cluster we obtained using NAS was highly consistent with that of PI 161375 [ 30 ], a Korean line belonging to ssp. agrestis . However, a limitation was noted regarding very large SVs not present in the reference, i.e. that identified in Chang-Bougi chromosome 11 which turned out to belong to the oriental melon clade. Using different high-quality reference genomes or even combining them in an “artificial” reference genome could address this shortcoming. Existing software packages such as BOSS-RUNS [ 14 ] already enable dynamic updating of decision strategies during a run, thereby enhancing the good balance of the target regions or multiplexed sample sequencing depth. Yet it is unclear whether NAS would be able to discover extra NLR gene clusters if they were to exist. This should be possible if the additional NBS domains are sufficiently conserved to match the provided reference. Overall, the findings of our study provide a blueprint for the selective capture of NLRome in melon and it could be extended to other key crop species. NLR gene numbers are generally low in the Cucurbitaceae family [ 67 , 25 ], and they represent an ideal percentage of the genome to be targeted using NAS. However, this ideal situation does not correspond to reality in other species, as the number of NLR genes is highly variable between plant species independently of their genome size [ 25 ]. To adapt the NAS procedure implemented here for NLR-rich plant species, certain adjustments should be made to align with the ideal targeted percentage of the genome. First, a reduction in the length of flanking regions surrounding ROIs is recommended. Subsequently, a more rigorous definition of NLR clusters would help reduce the targeted percentage of the genome. Finally, strict NAS targeting of clustered NLRs could be done, recovering the easiest-to-assemble isolated NLRs with the low-pass reads rejected by NAS. Conclusion NAS offered flexible real-time enrichment of selected NLR gene clusters while reducing costs as compared to the WGS approach. This target enrichment did not require any laborious or expensive library preparation, nor probe design and synthesis, unlike previously developed target sequencing methods. This is particularly advantageous for researchers who may not have access to special molecular biology techniques or who seek to conduct in-field experiments. NAS only requires an ONT sequencing device (e.g. the low-cost MinION device), a reference genome, and one or several ROIs. In addition, the fast enrichment obtained here may be of marked interest when time is a critical factor. Moreover, we highlighted the ability of NAS to reduce the high off-target data volume generated by WGS, addressing the growing challenges of data management and storage in the field of bioinformatics and genomics. This method, which we validated here on three melon cultivars, shows promise for application when dealing with a large number of accessions. This is particularly relevant for breeding applications as it opens avenues for creating multi-resistant varieties by tapping into the NLRome diversity. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials The datasets generated during the current study are available in the following repositories: The sequencing data and final consensus sequences of the WGS of Anso77 and Doublon are available at the NCBI database under BioProjects PRJNA662717 and PRJNA662721. Anso77: BioSample SAMN16093315, Accessions SRX9241647-SRX9241650, SRX24764327 for ONT and Illumina datasets, SUPF_0000005611 for BioNano Maps, and JBEGDE000000000 for genome assembly. Doublon: BioSample SAMN16093377, Accessions SRX9347576, SRX9235348 and SRX24764320 for PacBio, ONT and Illumina datasets, SUPF_0000005610 for BioNano Maps, and JBDXSX000000000 for genome assembly. The sequencing data generated during NAS experiments are available at the NCBI databases under BioProject PRJNA1127998. Anso77: BioSample SAMN16093315, Accession SRX25061202 for ONT dataset (including reads from the half targeted and half WGS flowcell configuration). Doublon: BioSample SAMN42022058, Accession SRX25061203 for ONT dataset (including reads from the half targeted and half WGS flowcell configuration). Chang-Bougi: BioSample SAMN42022059, Accession SRX25061204 for ONT dataset (targeted sequencing). The targeted sequencing assemblies for the three accessions are available at the Recherche Data Gouv database (https://entrepot.recherche.data.gouv.fr/) under DOI https://doi.org/10.57745/ZALVPU. The functional annotations of Anso77 and Doublon de novo whole genome assemblies, together with the scripts used for data analysis and genome assemblies, are available at the GitLab page indicated in the scripts_availability.txt file included in the Recherche Data Gouv database, DOI https://doi.org/10.57745/ZALVPU. The large sequencing_summary.txt files used for read filtering by end reason prior assembly are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This research was partly funded by the French Ministère de l’agriculture et de la souveraineté alimentaire ( Vat &Co project - CASDAR − 2017-2021) and the French National Research Institute for Agriculture, Food and Environment (INRAE). The doctoral position of Javier Belinchon-Moreno is co-funded by the INRAE BAP Department and the EUR Implanteus of Avignon University, France. Authors’ contributions J.B.M. performed the NAS sequencing experiments, the bioinformatics and statistical analyses, and NAS assemblies. P.F.R., N.B. and D.H. conceived the study. J.L., V.C., A.C. designed and identified the ROIs for NAS. A.B. and I.L. generated the ONT, Illumina and 10x genomic data for the whole genome assemblies. W.M. generated BioNano data for the whole genome assemblies, and participated in the hybrid scaffolding. J.L. and R.F.L. performed the whole genome assemblies. S.E. provided expertise and bioinformatics support. C.C. provided expertise and experimental support. V.R.R. manually annotated the Vat cluster and conducted the PCR experiments. J.B.M., P.F.R., N.B. and D.H. wrote the manuscript. J.B.M. and A.C. and J.B.M did the data submission. All authors read and approved the final manuscript. 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Shang L, Li X, He H, Yuan Q, Song Y, Wei Z, et al. A super pan-genomic landscape of rice. Cell Res. 2022;32(10):878-96. Baggs E, Dagdas G, Krasileva K. NLR diversity, helpers and integrated domains: Making sense of the NLR IDentity. Curr Opin Plant Biol. 2017;38:59–67. https://doi.org/10.1016/j.pbi.2017.04.012 Additional Declarations No competing interests reported. Supplementary Files Additionalfile1V1.docx Additionalfile2V2.xlsx Additionalfile3.bed Figuregel.png Cite Share Download PDF Status: Published Journal Publication published 10 Feb, 2025 Read the published version in BMC Genomics → Version 1 posted Editorial decision: Revision requested 09 Dec, 2024 Reviews received at journal 08 Dec, 2024 Reviewers agreed at journal 02 Dec, 2024 Reviews received at journal 12 Oct, 2024 Reviewers agreed at journal 09 Oct, 2024 Reviewers agreed at journal 01 Oct, 2024 Reviewers invited by journal 02 Aug, 2024 Editor invited by journal 02 Aug, 2024 Editor assigned by journal 01 Aug, 2024 Submission checks completed at journal 01 Aug, 2024 First submitted to journal 30 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4828883","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":345782318,"identity":"0c975720-58a5-438d-82e1-a06bda981b73","order_by":0,"name":"Javier Belinchon-Moreno","email":"","orcid":"","institution":"INRAE, Génétique et Amélioration des Fruits et Légumes","correspondingAuthor":false,"prefix":"","firstName":"Javier","middleName":"","lastName":"Belinchon-Moreno","suffix":""},{"id":345782319,"identity":"7ff08614-1295-4a56-84e1-fe411e623537","order_by":1,"name":"Aurelie Berard","email":"","orcid":"","institution":"Université Paris-Saclay, Centre INRAE Île-de-France Versailles-Saclay, EPGV","correspondingAuthor":false,"prefix":"","firstName":"Aurelie","middleName":"","lastName":"Berard","suffix":""},{"id":345782320,"identity":"d908c667-ed55-4f7e-8b3d-c181f615ef93","order_by":2,"name":"Aurelie Canaguier","email":"","orcid":"","institution":"Université Paris-Saclay, Centre INRAE Île-de-France Versailles-Saclay, EPGV","correspondingAuthor":false,"prefix":"","firstName":"Aurelie","middleName":"","lastName":"Canaguier","suffix":""},{"id":345782321,"identity":"3a2dbedd-aa5d-4e97-9617-de3e8f292996","order_by":3,"name":"Véronique Chovelon","email":"","orcid":"","institution":"INRAE, Génétique et Amélioration des Fruits et Légumes","correspondingAuthor":false,"prefix":"","firstName":"Véronique","middleName":"","lastName":"Chovelon","suffix":""},{"id":345782322,"identity":"c650f303-9cef-47e5-9263-f14063456cb2","order_by":4,"name":"Corinne Cruaud","email":"","orcid":"","institution":"Genoscope, Institut de Biologie François-Jacob, Université Paris-Saclay","correspondingAuthor":false,"prefix":"","firstName":"Corinne","middleName":"","lastName":"Cruaud","suffix":""},{"id":345782323,"identity":"04d8cb22-4852-477b-a91a-4765adb847df","order_by":5,"name":"Stéfan Engelen","email":"","orcid":"","institution":"Génomique Métabolique, Institut François Jacob, Commissariat à l’Energie Atomique (CEA), CNRS, Univ. Evry, Université Paris-Saclay","correspondingAuthor":false,"prefix":"","firstName":"Stéfan","middleName":"","lastName":"Engelen","suffix":""},{"id":345782324,"identity":"9a64ab91-1afd-4abf-89aa-2e2e28c49d80","order_by":6,"name":"Rafael Feriche-Linares","email":"","orcid":"","institution":"INRAE, Génétique et Amélioration des Fruits et Légumes","correspondingAuthor":false,"prefix":"","firstName":"Rafael","middleName":"","lastName":"Feriche-Linares","suffix":""},{"id":345782325,"identity":"7573ba92-7b95-4495-98aa-954dc847c43b","order_by":7,"name":"Isabelle Le-Clainche","email":"","orcid":"","institution":"Université Paris-Saclay, Centre INRAE Île-de-France Versailles-Saclay, EPGV","correspondingAuthor":false,"prefix":"","firstName":"Isabelle","middleName":"","lastName":"Le-Clainche","suffix":""},{"id":345782326,"identity":"6028429d-7969-4ac2-b806-805059b9d2f7","order_by":8,"name":"William Marande","email":"","orcid":"","institution":"INRAE, Centre National de Ressources Génomiques Végétales","correspondingAuthor":false,"prefix":"","firstName":"William","middleName":"","lastName":"Marande","suffix":""},{"id":345782327,"identity":"94ea4d69-3e18-4522-91d9-ae567055eac5","order_by":9,"name":"Vincent Rittener-Ruff","email":"","orcid":"","institution":"INRAE, Génétique et Amélioration des Fruits et Légumes","correspondingAuthor":false,"prefix":"","firstName":"Vincent","middleName":"","lastName":"Rittener-Ruff","suffix":""},{"id":345782328,"identity":"716cdfab-b12a-4da1-9f98-3a89e3e890a6","order_by":10,"name":"Jacques Lagnel","email":"","orcid":"","institution":"INRAE, Génétique et Amélioration des Fruits et Légumes","correspondingAuthor":false,"prefix":"","firstName":"Jacques","middleName":"","lastName":"Lagnel","suffix":""},{"id":345782329,"identity":"07442079-c157-4a56-8b84-6f47c11ecd1b","order_by":11,"name":"Damien Hinsinger","email":"","orcid":"","institution":"Université Paris-Saclay, Centre INRAE Île-de-France Versailles-Saclay, EPGV","correspondingAuthor":false,"prefix":"","firstName":"Damien","middleName":"","lastName":"Hinsinger","suffix":""},{"id":345782330,"identity":"c84667b4-c54a-430d-b6ca-24f56ea889c8","order_by":12,"name":"Nathalie Boissot","email":"","orcid":"","institution":"INRAE, Génétique et Amélioration des Fruits et Légumes","correspondingAuthor":false,"prefix":"","firstName":"Nathalie","middleName":"","lastName":"Boissot","suffix":""},{"id":345782331,"identity":"6a3af494-554d-4f34-b40b-1339f0d0a368","order_by":13,"name":"Patricia Faivre Rampant","email":"data:image/png;base64,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","orcid":"","institution":"Université Paris-Saclay, Centre INRAE Île-de-France Versailles-Saclay, EPGV","correspondingAuthor":true,"prefix":"","firstName":"Patricia","middleName":"Faivre","lastName":"Rampant","suffix":""}],"badges":[],"createdAt":"2024-07-30 12:44:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4828883/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4828883/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12864-025-11295-5","type":"published","date":"2025-02-10T15:57:54+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":63483637,"identity":"d14fe593-7f71-4136-a822-dff8c2f49e52","added_by":"auto","created_at":"2024-08-28 15:30:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":74415,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic representation of the definition of ROIs, target regions and target regions without REs. Target regions without REs were provided to the MinKNOW software. Regions were defined in the reference genome for NAS, Anso77.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4828883/v1/035358481cb17a05f20657ff.png"},{"id":63483638,"identity":"fcb93f55-e234-4bd8-87de-2b23ec2b475e","added_by":"auto","created_at":"2024-08-28 15:30:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":94903,"visible":true,"origin":"","legend":"\u003cp\u003eWorkflow diagram summarizing the different steps involved in data processing and target regions assembly. For Chang-Bougi, only the NAS way was followed, and the split by channel step was omitted.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4828883/v1/6ecea4531861652f9cb109ac.png"},{"id":63485617,"identity":"848c8dc1-7e9f-44b9-9b36-1e2d8ad27353","added_by":"auto","created_at":"2024-08-28 15:54:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":77223,"visible":true,"origin":"","legend":"\u003cp\u003eRead length distribution of the different generated datasets. A) Length distribution of “PASS”-tagged NAS reads. The number of reads was log-transformed. B) Length distribution of WGS reads after filtering by “end reason”, quality and length. C) Length distribution of NAS reads after filtering by “end reason”, quality and length.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4828883/v1/15c1ae655a3feb50afd4e00d.png"},{"id":63483650,"identity":"77cb91e6-f0b7-436c-ae63-4c8d1cfd18c1","added_by":"auto","created_at":"2024-08-28 15:30:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":150407,"visible":true,"origin":"","legend":"\u003cp\u003eNAS sequencing depth on three representative target regions of different sizes. A) NAS sequencing depth on the three target regions compared to the rest of the chromosome. Target regions (ROI + 20kb buffer) are represented between black dotted bars, while ROIs are collapsed and represented between black solid bars. B) NAS sequencing depth on the ROI of region 01 of chromosome 1 (≈173 kb). C) NAS sequencing depth on the ROI of region 08 of chromosome 5 (≈998 kb) D) NAS sequencing depth on the ROI of region 12 of chromosome 8 (≈1 kb). Region 08 contains the well-studied \u003cem\u003eVat \u003c/em\u003ecluster. For B, C and D, vertical colored bars represent the enriched regions, while vertical white bars represent masked repetitive elements.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4828883/v1/079566c491bd1520ec24510c.png"},{"id":63484345,"identity":"3d945438-49f3-4284-8e5a-1cb3ece3c4a0","added_by":"auto","created_at":"2024-08-28 15:38:11","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":247262,"visible":true,"origin":"","legend":"\u003cp\u003eEnrichment by yield (A, C) and enrichment by selection (B, D) of the 15 target regions from Anso77 (A, B) and Doublon (C, D). Vertical red-dotted bars denote the flowcell washing flush time.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4828883/v1/31b8158b49f3d1c463fef49e.png"},{"id":63484348,"identity":"7a502302-4851-4f1d-b1e1-7e6f2c9a5494","added_by":"auto","created_at":"2024-08-28 15:38:11","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":206597,"visible":true,"origin":"","legend":"\u003cp\u003eDot plots representing the \u003cem\u003eVat \u003c/em\u003eregion for Anso77 (A) and Doublon (B). Reference \u003cem\u003eVat \u003c/em\u003eregion is represented on the x-axis, while the NAS-reconstructed \u003cem\u003eVat \u003c/em\u003eregion is represented on the y-axis.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4828883/v1/48e33dd1ddba2c117ac66386.png"},{"id":63483643,"identity":"ce38332b-0363-4d58-be9a-9c5063e4f047","added_by":"auto","created_at":"2024-08-28 15:30:11","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":59638,"visible":true,"origin":"","legend":"\u003cp\u003eSequence depth by time in the NAS experience of the 15 target regions from Chang-Bougi.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4828883/v1/aa277a37d37b67f86317b173.png"},{"id":63485020,"identity":"63b70344-d3fa-4258-8ee9-11c820d0ad46","added_by":"auto","created_at":"2024-08-28 15:46:11","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":235936,"visible":true,"origin":"","legend":"\u003cp\u003eDot plots representing the NAS filtered assembly of Chang-Bougi (y-axis) against two sequences. A) NAS filtered assembly of Chang-Bougi (y-axis) against the 15 target regions from Anso77 (x-axis). B) NAS filtered assembly of Chang-Bougi (y-axis) against the 18 NLR clusters identified in the Chang-Bougi assembly published by Shin \u003cem\u003eet al\u003c/em\u003e. [32].\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4828883/v1/bf69402dd710d0af84b5489f.png"},{"id":63483645,"identity":"50bf2e3f-1f84-4643-b527-592d41449354","added_by":"auto","created_at":"2024-08-28 15:30:11","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":188228,"visible":true,"origin":"","legend":"\u003cp\u003eManual annotation and validation of the \u003cem\u003eVat \u003c/em\u003eregion of Chang-Bougi\u003cem\u003e. \u003c/em\u003eA) Genes identified after manual annotation within the \u003cem\u003eVat \u003c/em\u003eregions of Chang-Bougi. The sequence above was obtained from the NAS assembly, while the sequence below was recovered from the publicly available genome assembly [32].\u003cem\u003e \u003c/em\u003eB) Agarose gel electrophoresis of PCR products obtained using primers Z649FR and Z1431FR. Lanes M1 and M2 are the two 1 kb DNA ladders (Promega, Madison, WI, USA). PI161375 was used as a control having a \u003cem\u003eVat1\u003c/em\u003e with four R65aa motifs and a \u003cem\u003eVat2\u003c/em\u003e with three R65aa motifs. Bands pointed with arrows represent an amplicon of four R65aa motifs (1), an amplicon of three R65aa motifs (2), and a specific amplicon of four R65aa motifs (3).\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-4828883/v1/14762c2e8be0f65c4bd46a7b.png"},{"id":63483640,"identity":"196c4cf1-f711-4b1d-8fce-aa2983218167","added_by":"auto","created_at":"2024-08-28 15:30:11","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":109690,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the use of standard and ultra-long reads in the enrichment of the \u003cem\u003eVat\u003c/em\u003e cluster. The diagram illustrates the difference in coverage and extent of the area outside the region to be enriched for standard size fragments (10-30kb) and for ultra-high molecular weight fragments (100-300kb) on the \u003cem\u003eVat\u003c/em\u003e (melon) cluster. For the same yield (illustrated here by an arbitrary overall depth of 5X), standard fragments make it possible to achieve a depth more concentrated on the area to be enriched and to sequence less outside this area. For convenience, only reads oriented from 5' to 3' are represented.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-4828883/v1/c5335e584d95395c6a5e71a5.png"},{"id":76487641,"identity":"2582e445-4440-4d7a-8eca-00d416de2817","added_by":"auto","created_at":"2025-02-17 16:10:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2689230,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4828883/v1/5660c726-93d9-4904-9ad7-74449cadf751.pdf"},{"id":63483649,"identity":"8e6261a7-fa81-4894-bb52-ed72473d8327","added_by":"auto","created_at":"2024-08-28 15:30:11","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1972365,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1V1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4828883/v1/43cb64dbf67fca142a07ce1e.docx"},{"id":63483639,"identity":"1bbd72ab-1ccc-436b-b525-d5e6c987892c","added_by":"auto","created_at":"2024-08-28 15:30:11","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":190564,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile2V2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4828883/v1/c98d4a21986b4913e7af6c1f.xlsx"},{"id":63486186,"identity":"40b2a9cc-eaad-4a83-9772-defd1c128246","added_by":"auto","created_at":"2024-08-28 16:02:11","extension":"bed","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":22458,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile3.bed","url":"https://assets-eu.researchsquare.com/files/rs-4828883/v1/ef4f3728b32bfff1d8d3d7e9.bed"},{"id":63483647,"identity":"8c919b6d-7961-4d9b-9a6c-eba314e87673","added_by":"auto","created_at":"2024-08-28 15:30:11","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":240769,"visible":true,"origin":"","legend":"","description":"","filename":"Figuregel.png","url":"https://assets-eu.researchsquare.com/files/rs-4828883/v1/dabe4f0914c63dcf9299d837.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Nanopore adaptive sampling to identify the NLR gene family in melon (Cucumis melo L.)","fulltext":[{"header":"Background","content":"\u003cp\u003eThe correct assembly of complex and highly repeated genome regions, which are especially prevalent in plants, remains a challenge. Short-read sequencing is a cost-effective method that is widely used in whole genome sequencing (WGS) approaches, yet it is ineffective in these regions as the read length is not sufficient for proper assessment of repeated elements, copy-number variations and duplication events [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Long-read technologies, such as those developed by Oxford Nanopore Technologies (ONT, Oxford, UK), have demonstrated their potential in accurately resolving complex regions as they are able to span long repetitive elements or areas with tandemly repeated genes [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Whole genome long-read sequencing is nevertheless still too costly for most studies, particularly when numerous genotypes have to be sequenced to highlight a few specific regions of interest.\u003c/p\u003e \u003cp\u003eTargeted sequencing approaches offer a valuable alternative for characterizing specific genomic regions while reducing sequencing and data storage costs as compared to WGS [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Current targeted sequencing protocols have been adapted for long-read sequencing and mainly involve hybridization capture [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], PCR amplification [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], Cas9-assisted targeting [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] or microfluidic-based droplet sorting [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, these approaches require substantial experimental and design efforts, along with substantial prior knowledge of the sequence to be enriched and its genotypic diversity [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. PCR-based techniques are particularly prone to introducing bias in the enriched sequences, and long amplicons are hard to consistently amplify [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Hybridization-based methods require the construction of complex RNA libraries alongside very specific hybridization and capture conditions [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Cas9-based methods, e.g. Nanopore Cas9-targeted sequencing (nCATS) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] or Cas9-Assisted Targeting of CHromosome segments (CATCH) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], require the design of multiple guide-RNAs, which may be a challenging task when dealing with complex and repetitive genome regions. Finally, microfluidic-based methods like Xdrop [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] are highly complex and require specialized microfluidic equipment [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe Nanopore adaptive sampling (NAS) approach recently developed by ONT overcomes these limitations. NAS was first suggested in 2016 [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and has been implemented with different algorithms since late 2019 [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This strategy takes advantage of the ability of the pores to control the directional flow of the DNA strand being sequenced through alterations in the applied current polarity. By combining live calling of sequenced bases with real-time mapping to a set of DNA sequences provided by the user for enrichment, the DNA strand may be dynamically discarded or fully sequenced based on the similarity of its initial first few hundred bases to the provided reference [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. NAS just requires standard library preparation, while overcoming the need for DNA amplification, laborious and expensive experimental design or probe synthesis, and it offers real-time selective enrichment [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. NAS has been used in clinical settings and for metagenomic sample enrichment [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. NAS is therefore a promising approach for studying target regions, especially those that are highly complex, such as disease-associated repeat loci in humans [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn plants, immunity is encoded by resistance genes (R genes), frequently organized in complex regions [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Among R genes, Nucleotide-binding site leucine-rich repeat resistance genes (NLRs) form the largest family [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. These genes encode intracellular receptors that play a central role in the so-called effector-triggered immunity (ETI) against pathogens. NLR genes exhibit a highly conserved structure with three main domains [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]: the N-terminal domain, the central domain, and the C-terminal domain. The N-terminal domain can be a Toll/Interleukin-1 receptor (TIR), a Coiled-coil (CC), or a resistance to Powdery Mildew 8-like (RPW8) domain. The central domain, the most conserved one, is a nucleotide-binding adaptor (NB-ARC), also named as NBS (nucleotide-binding site) domain. This domain plays a crucial role in signal transduction. Finally, the C-terminal domain is often composed of leucine-rich repeats (LRR) with ligand-binding functions. A clustered genomic arrangement is a common characteristic of NLR genes [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. These clusters often result from unequal crossing overs, tandem duplications, or intra-cluster rearrangements [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In this context, NAS, combining long-read sequencing and target enrichment, should allow the accurate characterization of NLR clusters in plants.\u003c/p\u003e \u003cp\u003eWe selected melon (\u003cem\u003eCucumis melo\u003c/em\u003e L.) as a model to investigate the ability of NAS to efficiently sequence a complete set of NLR clusters within a species (or NLRome). The melon genome features: i/ a small genome size; ii/ an NLR content estimated to account for \u0026asymp;\u0026thinsp;1% of the genome [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], which is in line with ONT target size recommendations [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]; and iii/ a finely characterized, highly variable complex NLR cluster, \u003cem\u003eVat\u003c/em\u003e [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], that is suitable for benchmarking. Among the accessions that are well characterized with regard to the \u003cem\u003eVat\u003c/em\u003e region, we chose Anso77 (ssp. \u003cem\u003emelo\u003c/em\u003e) because it features the highest number of functional \u003cem\u003eVat\u003c/em\u003e genes [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. We also selected Doublon (ssp. \u003cem\u003emelo\u003c/em\u003e) as an accession whose \u003cem\u003eVat\u003c/em\u003e region structure contrasts with that of Anso77 [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. We assembled and annotated their whole genomes and selected Anso77 as reference for identifying regions of interest (ROIs) for NAS. The performance of the method in capturing a set of NLR clusters in Anso77 and Doublon was assessed. Furthermore, we extended our assessment to an accession of a different subspecies (Chang-Bougi, ssp. \u003cem\u003eagrestis\u003c/em\u003e) for which open-access genomic data was available [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eBIOLOGICAL MATERIAL\u003c/h2\u003e \u003cp\u003eWe selected Anso77, Doublon and Chang-Bougi melon cultivars to develop a proof of concept for the NLRome adaptive sampling experiment, with Anso77 serving as the reference cultivar. These cultivars originated from Spain, France and Korea, respectively. Anso77 and Doublon were chosen as ssp. \u003cem\u003emelo\u003c/em\u003e lines belonging to the \u003cem\u003einodorus\u003c/em\u003e and \u003cem\u003ecantalupensis\u003c/em\u003e botanical groups. Chang-Bougi, belonging to ssp. \u003cem\u003eagrestis\u003c/em\u003e and specifically to the \u003cem\u003emakuwa\u003c/em\u003e botanical group, was selected as a cultivar distantly related to Anso77 and Doublon. The aim of this choice was to validate the NAS procedure with cultivars differing markedly from the selected reference. Moreover, a draft genome assembly for Chang-Bougi constructed via Illumina HiSeq reads was readily available [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe obtained the seeds from the INRAE Centre for Vegetable Germplasm in Avignon, France [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], and grew them under greenhouse conditions at the INRAE GAFL research unit, Avignon, France.\u003c/p\u003e \u003cp\u003e \u003cb\u003eANSO77 AND DOUBLON\u003c/b\u003e \u003cb\u003eDE NOVO\u003c/b\u003e \u003cb\u003eWHOLE GENOME SEQUENCING, ASSEMBLY AND ANNOTATION\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe produced whole \u003cem\u003ede novo\u003c/em\u003e Anso77 and Doublon genome assemblies using long-read sequencing: ONT for Anso77 and ONT combined with PacBio (Pacific Biosciences, Menlo Park, CA, USA) for Doublon. Raw reads were already deposited in the NCBI database under the following Bioproject accession numbers: PRJNA662717 and PRJNA662721 [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. BioNano optical maps (BioNano Genomics, San Diego, CA, USA), 10x Linked-Reads (Pleasanton, CA, USA) for Anso77, Illumina Novaseq short-read sequencing (Illumina, San Diego, CA, USA), and linkage map information were developed and used to construct the genome assemblies. We performed gene prediction on the assembled genomes using two independent procedures: A combination of \u003cem\u003eab initio\u003c/em\u003e, homology-based and transcriptome-based methods implemented with EuGene v. 4.3 [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]; and an \u003cem\u003eab initio\u003c/em\u003e approach using deep-learning implemented with Helixer [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Functional annotation of predicted genes was performed using the EggNOG-mapper v. 2.1.12 suite [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The fully detailed methods and parameters used for the assemblies and annotations are provided in Additional file 1: Supplementary Methods.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eNAS ENRICHMENT PANEL DEFINITION AND EXPERIMENTAL DESIGN\u003c/h2\u003e \u003cp\u003eWe used Anso77 cultivar as reference for constructing the target regions for the NAS approach. We predicted the presence of NLR-related genes using NLGenomeSweeper [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] with default parameters. This tool approximates the presence of NLR genes via identification of the well-conserved NBS domain. We defined the regions of interest (ROIs) by grouping the predicted NBS domains separated by regions\u0026thinsp;\u0026lt;\u0026thinsp;1 Mb. We ensured that there would be robust read depth coverage on the selected ROIs by adding a 20 kb buffer zone flanking the ROIs to constitute the initial target regions.\u003c/p\u003e \u003cp\u003eWe performed RE annotation in the initial target regions using the CENSOR tool available on the curated GIRI Repbase website [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Predicted REs\u0026thinsp;\u0026gt;\u0026thinsp;200 bp were excluded from the initial target regions. Moreover, sequences\u0026thinsp;\u0026lt;\u0026thinsp;500 bp located between them were also excluded, as adaptive sampling requires\u0026thinsp;\u0026asymp;\u0026thinsp;500 bp to accept or reject the DNA strand. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides definitions of the ROIs, target regions and target regions without REs.\u003c/p\u003e \u003cp\u003eWe input these target regions without REs in bed format (Additional file 3) and the reference genome of Anso77 in fasta format in the MinKNOW software platform (ONT, Oxford, UK). These files were used for read acceptance or rejection. If the initial\u0026thinsp;~\u0026thinsp;500 bp of the DNA strands matched the target regions without REs they underwent complete sequencing, otherwise they were ejected from the pore.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eDNA EXTRACTION, ADAPTIVE SAMPLING SEQUENCING AND BASE CALLING\u003c/h2\u003e \u003cp\u003ePlant leaves were harvested and immediately frozen in liquid nitrogen for subsequent DNA extraction. Genomic DNA was extracted using the NucleoSpin Plant II kit (Macherey-Nagel, Germany) according to the manufacturer\u0026rsquo;s protocol. DNA quantity and quality assessments were conducted using the Qubit4\u0026reg; 1x dsDNA BR Assay Kit (Invitrogen, Carlsbad, CA, USA) and the Agilent 2200 TapeStation system (Agilent Technologies, Santa Clara, CA, USA).\u003c/p\u003e \u003cp\u003eWe multiplexed and sequenced Anso77 and Doublon DNA on a single PromethION \u003cem\u003eR10\u003c/em\u003e.4.1 flowcell (ONT, Oxford, UK). Half the channels were used as control channels in which no adaptive sampling was performed. Moreover, we sequenced Chang-Bougi DNA using one tenth of a PromethION \u003cem\u003eR10\u003c/em\u003e.4.1 flowcell to assess the NAS flexibility and scalability. We prepared the sequencing libraries using the Native Barcoding Kit 24 V14 (SQK-NBD114.24) (ONT, Oxford, UK) according to the ONT guidelines with some modifications. One microgram of genomic DNA from each sample was repaired and end-prepped with 20 min incubation at 20\u0026deg;C followed by a heat-inactivation of the enzymes at 65\u0026deg;C for an additional 20 min. DNA was purified and barcodes were individually ligated to each of the purified DNA samples. NB01 and NB02 barcodes were used for Anso77 and Doublon, and NB22 was used for Chang-Bougi. Barcoded samples were purified using AMPure XP beads (Beckman Coulter Inc., Brea, CA, USA) at a ratio of 0.4:1 beads-to-barcoding mix, while keeping each barcoded sample in an independent Eppendorf tube. Finally, purified barcoded DNA samples were pooled at equimolar concentration to obtain a total volume of 30 \u0026micro;l. Adapters were ligated to the pooled samples. After purification, DNA sizes and concentrations in the barcoded pools were quantified using the Agilent 2200 TapeStation system and Qubit4\u0026reg; 1x dsDNA HS Assay Kit (Invitrogen, Carlsbad, CA, USA), respectively. Final libraries were adjusted to a volume of 32 \u0026micro;l containing 10\u0026ndash;20 fmol of DNA. All incubations\u0026thinsp;\u0026lt;\u0026thinsp;10 min were extended to 10 min.\u003c/p\u003e \u003cp\u003eWe supplemented the libraries by adding the Sequencing Buffer (ONT, Oxford, UK) and Library Loading Beads (ONT, Oxford, UK), and subsequently loaded them into R10.4.1 PromethION flowcells for 96 h runs for Anso77 and Doublon, or 120 h runs for Chang-Bougi. Library reloading (washing flush) was performed in all of the experiments when the percentage of sequencing pores dropped to 10\u0026ndash;15%. NAS was performed using PromethION flowcell channels 1-1500, while the remaining channels served as control. The sequencing speed was set at 260 bp (accuracy mode) for Anso77 and Doublon, and the quality score threshold was set at 10. For Chang-Bougi, NAS was performed on the whole flowcell and the sequencing speed was modified to 400 bp (default mode) because the 260 bp option has been deprecated from MinKNOW version 23.04.\u003c/p\u003e \u003cp\u003eRaw ONT FAST5 files were live base-called during the PromethION run with Guppy (ONT, London, UK) v. 6.3.9 for Anso77 and Doublon and Guppy v. 6.5.7 for Chang-Bougi in \u0026ldquo;super accurate base-calling\u0026rdquo; mode. Barcodes were automatically trimmed using the \u0026ldquo;trim barcodes\u0026rdquo; option of the MinKNOW software v. 22.10.7 for Anso77 and Doublon, and v. 23.04.5 for Chang-Bougi. For each run, the automatically generated \u0026ldquo;sequencing_summary.txt\u0026rdquo; file and the FASTQ files of the samples were retained for further processing.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eNAS DATA PROCESSING AND ENRICHMENT CALCULATION\u003c/h2\u003e \u003cp\u003eFor Anso77 and Doublon, we split the reads by channel, thereby generating two FASTQ files per sample: one with the reads sequenced on channels 1-1500 for NAS, and another with the reads generated on channels 1501\u0026ndash;3000 for WGS. No splitting by channel was performed for Chang-Bougi as all of the channels were used for NAS. Reads with a \u0026ldquo;PASS\u0026rdquo; flag, i.e. their quality score was \u0026gt;\u0026thinsp;10, were retained for downstream analysis. We identified the NAS-rejected reads based on their \u0026ldquo;end reason\u0026rdquo; in the sequencing summary file. This file included the generated read classifications based on their \u0026ldquo;end reason\u0026rdquo;. In this way, reads rejected by the adaptive sampling were labeled as \u0026ldquo;Data Service Unblock Mux Change\u0026rdquo;. Reads labeled as \u0026ldquo;Unblock Mux Change\u0026rdquo;, \u0026ldquo;Mux Change\u0026rdquo; and \u0026ldquo;Signal Negative\u0026rdquo; were also filtered out. Thereafter we filtered the generated FASTQ files by size, keeping reads\u0026thinsp;\u0026gt;\u0026thinsp;1 kb. We assessed statistics on these FASTQ files using seqkit stats v. 2.4.0 [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe computed sequence depth statistics for Anso77 and Doublon by aligning the reads to their reference whole genome assemblies with minimap2 v. 2.24-r1122 [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] and using mosdepth v. 0.3.3 [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], with a bed file containing the coordinates of the 15 target regions. For Chang-Bougi, sequence depth statistics were calculated by aligning the reads to their assembled target regions. The split flowcell setup allowed us to calculate the extent of enrichment in Anso77 and Doublon by comparing the read depths generated in NAS and WGS. We assessed the NAS efficiency using the two following enrichment measurements.\u003c/p\u003e \u003cp\u003eEnrichment by yield, i.e. the ratio of the on-target sequence depth (NLR cluster\u0026thinsp;+\u0026thinsp;20 kb flanking) with NAS to that with WGS, was assessed as follows:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:Enrichment\\:by\\:yield=\\:\\frac{{depth}_{region\\_NAS}}{{depth}_{region\\_WGS}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere depth\u003csub\u003eregion_NAS\u003c/sub\u003e and depth\u003csub\u003eregion_WGS\u003c/sub\u003e represent the on-target sequence depth in the NAS and WGS experiments, respectively.\u003c/p\u003e \u003cp\u003eEnrichment by selection, i.e. the ratio of the relative selection of the target regions between NAS and WGS, measures the extent to which NAS can alter the abundance of the given target regions within a complete genome, while considering the sequencing behavior of each ROI. This was calculated as follows:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:Enrichment\\:by\\:selection=\\:\\frac{\\raisebox{1ex}{${depth}_{region\\_NAS}$}\\!\\left/\\:\\!\\raisebox{-1ex}{${depth}_{chr\\_NAS}$}\\right.}{\\raisebox{1ex}{${depth}_{region\\_WGS}$}\\!\\left/\\:\\!\\raisebox{-1ex}{${depth}_{chr\\_WGS}$}\\right.}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere depth\u003csub\u003eregion_NAS\u003c/sub\u003e and depth\u003csub\u003echr_NAS\u003c/sub\u003e represent, respectively, the sequence depth on-target (NLR cluster\u0026thinsp;+\u0026thinsp;20 kb flanking) and on the rest of the chromosome in the adaptive sampling approach, while depth\u003csub\u003eregion_WGS\u003c/sub\u003e and depth\u003csub\u003echr_WGS\u003c/sub\u003e represent the depth coverage on-target (NLR cluster\u0026thinsp;+\u0026thinsp;20 kb flanking) and on the rest of the chromosome in the WGS approach. The relative selection with WGS should be equal to one if there is no bias that could cause the target regions to be differentially enriched compared to the rest of the genome.\u003c/p\u003e \u003cp\u003eWe calculated the average enrichment by yield between all target regions as the ratio of the average sequence depth on-target in NAS and WGS. Similarly, we assessed the average enrichment by selection between all target regions as the ratio of the average relative frequency of target regions in NAS and WGS. The average relative frequencies of target regions were calculated as the ratio of the average sequence depth on-target and off-target. Only chromosomes containing target regions were considered when calculating the off-target average sequence depth.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eTARGET REGION ASSEMBLY, NLR ANNOTATION AND QUALITY CONTROL\u003c/h2\u003e \u003cp\u003eWe tested a set of assemblers tailored for ONT sequencing data, including Canu [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], Flye [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], Shasta [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], Necat [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], Raven [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] and SMARTdenovo [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], for assembling the NAS reads (data not shown). SMARTdenovo was primarily selected due to its superior target region assembly metrics (contiguity and assembly errors), achieved within a short time and with low memory usage. For Chang-Bougi, one target region was selected from the Canu assembly, as SMARTdenovo failed to collapse a repeat region generating two contigs instead of a single one. We used default parameters and added the \u0026ldquo;generate consensus\u0026rdquo; option for SMARTdenovo v. 2018.2.19. Canu v. 2.2 was executed with the \u0026ldquo;genomesize\u0026thinsp;=\u0026thinsp;7m \u0026ndash;corrected \u0026ndash;trimmed \u0026ndash;nanopore\u0026rdquo; options, and using reads\u0026thinsp;\u0026gt;\u0026thinsp;8 kb.\u003c/p\u003e \u003cp\u003eFor each assembly, we filtered the contigs and only kept those that included at least one predicted NBS domain or matched more than 15 kb with at least 45% identity to any of the 15 target regions of Anso77. We assessed NBS domain prediction using NLGenomeSweeper. We used the nucmer and delta-filter commands of MUMmer v. 4.0.0rc1 [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] to select contigs matching the Anso77 target regions. Nucmer was used with the \u0026ndash;l 100 option, keeping the rest of the parameters as default. Hits reported by nucmer were filtered with delta-filter with the -r -q -l 15000 -i 45 options. We ran QUAST v. 5.0.2 [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] to assess the basic statistics of the generated filtered assemblies.\u003c/p\u003e \u003cp\u003eAssembly errors were analysed by focusing on the well-studied \u003cem\u003eVat\u003c/em\u003e region. We performed manual annotations of the \u003cem\u003eVat\u003c/em\u003e regions as described by Chovelon et al. [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The accuracy of the \u003cem\u003eVat\u003c/em\u003e homologs was analysed using two PCR markers: Z649 FR, which indicates the number of R65aa motifs in \u003cem\u003eVat\u003c/em\u003e homologs [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], and Z1431 FR which is specific to a \u003cem\u003eVat\u003c/em\u003e homolog with four R65aa motifs [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Workflow diagram summarizing the different steps involved in data processing and target regions assembly. For Chang-Bougi, only the NAS way was followed, and the split by channel step was omitted.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eANSO77 AND DOUBLON\u003c/b\u003e \u003cb\u003eDE NOVO\u003c/b\u003e \u003cb\u003eGENOME ASSEMBLIES AND ANNOTATION\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe metrics of the different genome assembly steps are detailed in Additional file 1: Supplementary Data. Key metrics of the final assemblies are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In summary, initial contig assemblies provided 159 and 186 contigs for Anso77 and Doublon, with long N50 values of 8.9 and 15.2 Mb. Both genomes were organized in 12 chromosomes and presented a total size of 369.47 and 362.63 Mb. From this size, 12.84 and 12.92 Mb corresponded to unscaffolded contigs or unoriented scaffolds. BUSCO results showed that 98.5 and 94.4% of the single-copy orthologs were completely present in the Anso77 and Doublon assemblies. In addition, Anso77 and Doublon assemblies presented Merqury QVs of 31.21 and 42.52, proving their great quality and completeness.\u003c/p\u003e \u003cp\u003eWe predicted 32,714 and 33,404 genes in the Anso77 and Doublon assemblies using the EuGene annotation software. Using Helixer, we predicted 21,692 genes for Anso77 and 21,125 for Doublon. As the EuGene results are more consistent with those previously observed in the literature in terms of number of predicted genes (Additional file 2: Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), we kept this annotation as a basis. However, we noticed that Helixer works better in predicting NLR genes structure, since it annotated more precisely the intron-exon structure of the previously validated NLR genes (\u003cem\u003eVat\u003c/em\u003e homologs, \u003cem\u003eFom-1\u003c/em\u003e and \u003cem\u003eFom-2\u003c/em\u003e). For this reason, we selected the Helixer annotation for those genome regions where NLGenomeSweeper indicated the presence of NBS domains. In addition, we removed those ncRNA genes that did not include Rfam information. Therefore, our final annotation contained 31,152 genes for Anso77, and 31,865 genes for Doublon. From them, 29,714 and 30,198 were protein-coding genes. The average gene length was 3,398 bp for both accessions, with 4.46 and 4.29 exons/gene on average in Anso77 and Doublon, respectively. 28,627 genes were functionally annotated for Anso77, and 29,144 for Doublon. The gene models captured 95.2 and 91.6% of the BUSCOs for Anso77 and Doublon. Only 3.3 and 3% of the genes were found fragmented, and 1.5 and 5.4% were missing.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary metrics of the Anso77 and Doublon hybrid genome assemblies and annotations.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnso77\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDoublon\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eChromosome assembly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of chromosomes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of scaffolds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMin. scaffold length (Mb)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMax. scaffold length (Mb)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal size of scaffolds (Mb)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e356.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e349.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eUnplaced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of scaffolds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of contigs (\u0026gt;\u0026thinsp;15 Kb)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSize of unplaced scaffolds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSize of unplaced contigs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"14\" rowspan=\"15\"\u003e \u003cp\u003eWhole genome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGC (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMerqury QV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBUSCO Complete genes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,591 (98.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,524 (94.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBUSCO Duplicated genes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (1.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBUSCO Fragmented genes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBUSCO Missing genes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (0.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78 (4.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of predicted genes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31,152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31,865\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of functionally annotated genes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28,627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29,144\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of predicted NBS domains\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of predicted CC-NBS-LRRs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of predicted TIR-NBS-LRRs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of predicted RPW8-NBS-LRRs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of predicted NBS-LRRs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of predicted TIR-NBSs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of predicted NBSs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe predicted 84 and 76 NBS domains in the Anso77 and Doublon genomes, respectively, which was within the range of values previously reported for melon genomes (Additional file 2: Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Based on InterProScan [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] domain identification in the 10 kb flanking sequence on both sides of the NBS domain, potential genes containing the predicted NBS domains were classified in different categories (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe accuracy of the NLR gene assemblies was assessed on the basis of the accuracy of the \u003cem\u003eVat\u003c/em\u003e homologs, whose cDNA sequence was previously obtained by Sanger sequencing [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. For Anso77, the \u003cem\u003eAN-Vat2\u003c/em\u003e, \u003cem\u003eAN-Vat3\u003c/em\u003e and \u003cem\u003eAN-Vat5\u003c/em\u003e homologs fully matched the assembly generated here, while \u003cem\u003eAN-Vat1\u003c/em\u003e and \u003cem\u003eAN-Vat4\u003c/em\u003e contained one SNP each. For Doublon, all three \u003cem\u003eVat\u003c/em\u003e homologs fully matched our assembled sequence.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eNAS TARGET REGION CONSTRUCTION\u003c/h2\u003e \u003cp\u003eWe arranged the 84 NLR predicted domains on Anso77 into 15 groups encompassing nine ROIs with 2\u0026ndash;28 NBS domains and six ROIs with isolated NBS domains. We also found 15 groups and similar physical positions of the NLR genes on Doublon and on the previously published melon genomes. After adding the 20 kb flanking zones, the sizes of the 15 target regions ranged from ~\u0026thinsp;41 to ~\u0026thinsp;1,378 kb, representing a total length of ~\u0026thinsp;6.16 Mb of the ~\u0026thinsp;370 Mb Anso77 genome (~\u0026thinsp;1.68%) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAfter masking the REs, the final file input in the PromethION sequencer included 935 target regions, ranging from 502 bp to 67.609 kb in size (Additional file 2: Supplementary Data). They accounted for a total of ~\u0026thinsp;5.23 Mb of the ~\u0026thinsp;370 Mb Anso77 genome (~\u0026thinsp;1.41%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDetailed information about the 15 target regions of Anso77.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTarget region\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChr\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStart position\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEnd position\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSize\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePredicted NBS domains\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion 01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32,471,836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32,684,765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e212,929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion 02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e979,011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,026,074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e47,063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion 03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15,072,686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15,113,435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40,749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion 04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5,452,467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5,493,242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40,775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion 05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26,516,353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27,894,199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,377,846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion 06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14,002,942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14,043,763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40,821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion 07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17,364,466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18,340,871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e976,405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion 08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25,108,836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26,146,869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,038,033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion 09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5,837,171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5,877,998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40,827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,550,415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,591,165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40,750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion 11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24,326,063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24,411,978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e85,915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion 12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,465,204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3,505,984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40,780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion 13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e645,676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e815,779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e170,103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion 14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6,558,754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7,597,791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,039,037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion 15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6,635,285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7,601,705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e966,420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe \u003cem\u003eVat\u003c/em\u003e region is included within region 08.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eNAS TARGET REGION VALIDATION: EFFECTIVE ENRICHMENT OF NLR CLUSTERS IN MELON\u003c/h3\u003e\n\u003cp\u003eOver 8.62\u0026nbsp;million reads and 19.86 Gb (42.71%) were assigned to Anso77, and over 11.97\u0026nbsp;million reads and 26.63 Gb (57.27%) were assigned to Doublon after barcode trimming. We compared NAS to WGS of Anso77 and Doublon in terms of their general metrics. For Anso77, further read splitting by channel and filtering of this data by quality, \u0026ldquo;end reason\u0026rdquo; and length resulted in 110.06 K reads and 1.14 Gb derived from the NAS half-flowcell. The other half-flowcell (WGS) yielded 1.12\u0026nbsp;million reads, with a cumulative size of 11.93 Gb for Anso77. Using the same processing for Doublon, 163.84 K reads generating 1.56 Gb from were assigned to the NAS half-flowcell, while 15.81 Gb from 1.70\u0026nbsp;million reads were assigned to the WGS part. For both Anso77 and Doublon, the N50 value from the filtered NAS reads was very similar to that of the filtered WGS reads. All information on the generated datasets is presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnso77, Doublon and Chang-Bougi sequencing metrics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDataset\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCultivar\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSequences number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSize (bp)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean length (bp)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMax. length (bp)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN50\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eQ20 (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eQ30 (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTotal dataset\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAnso77\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8,628,783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19,860,863,026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e174,519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13,263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e82.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e72.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eDoublon\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11,976,866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26,639,849,880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e193,630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11,968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e83.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e73.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eChang-Bougi\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,320,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3,454,320,986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e467,827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e76.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e58.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal WGS \u0026ldquo;pass\u0026rdquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAnso77\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,423,908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13,579,603,804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9,536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e143,366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16,713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e87.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e77.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eDoublon\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,033,986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18,285,705,305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8,990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e137,509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15,261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e87.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e77.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTotal NAS \u0026ldquo;pass\u0026rdquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAnso77\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6,634,798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4,807,120,593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e126,590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e87.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e77.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eDoublon\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9,407,579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6,779,872,639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e112,867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e88.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e77.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eChang-Bougi\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,056,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3,115,590,337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e232,918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e82.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e64.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFiltered WGS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAnso77\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,122,605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11,939,444,337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10,636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e143,366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16,772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e88.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e77.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eDoublon\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,704,828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15,807,284,512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9,272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e137,509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15,120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e88.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e78.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eFiltered NAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAnso77\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e110,061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,147,896,611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10,430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e123,373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16,911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e88.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e78.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eDoublon\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e163,843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,561,078,862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9,528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e112,867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15,182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e88.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e77.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eChang-Bougi\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96,626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e803,580,422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8,316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e122,918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13,749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e81.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e66.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAnso77 and Doublon were sequenced using both NAS and WGS, while Chang-Bougi was only sequenced using NAS. Total WGS \u0026ldquo;pass\u0026rdquo; and total NAS \u0026ldquo;pass\u0026rdquo; represent the reads having a \u0026ldquo;PASS\u0026rdquo; flag (quality over 10) and assigned to the WGS and NAS half-flowcell, respectively. Filtered WGS and filtered NAS represent the two sets of reads just mentioned after filters of \u0026ldquo;end reason\u0026rdquo; and length.\u003c/p\u003e \u003cp\u003eThe length distribution of NAS-generated reads peaked at around 500 bp, corresponding to reads rejected by adaptive sampling (91.20% and 92.20% of the total \u0026ldquo;pass\u0026rdquo; reads for Anso77 and Doublon) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). When rejected reads were out-filtered, the length distribution profile was similar for reads obtained for both cultivars in WGS and in NAS (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, C).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWhen focusing on Anso77, we evaluated the read depth on the target regions and on the rest of the chromosome in the NAS approach. The sequence depth on the target regions at the end of the experiment was much higher than on the rest of the chromosome (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), with an average frequency of target regions of 63.37. The sequence depth remained stable throughout the entire ROIs, even more so when the ROIs had a smaller size (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-C). The standard deviation of the sequence depth in the ROIs ranged from 1.38 for region 03 to 20.19 for region 07, with values of 10.29, 16.15 and 1.80 obtained for regions 01, 08 and 12, respectively. The increase in sequence depth was gradual on the 20 kb flanking regions, with the highest depth obtained in the ROIs. The increase had a similar pattern regardless of the ROIs size (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe half-flowcell design allowed us to calculate the enrichment obtained in NAS compared to the WGS approach. We obtained enrichment by yield for Anso77 that varied among the target regions, i.e. ranging from 2.45 to 5.18 at the end of the run (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Otherwise, we obtained increased enrichment by selection ranging from 45.56 to 102.91 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). On average, we obtained enrichment by yield and by selection of 3.96 and 78.38, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Target regions were sequenced at a lower rate than the rest of the genome in WGS, with a relative frequency of 0.81 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). We also noted that both enrichment by yield and by selection followed very similar temporal patterns. This enrichment peaked at the beginning of the run for most of the regions when most of the flowcell channels were actively sequencing, and then it decreased over time. This trend implies that channel inactivation occurred faster on the NAS half-flowcell.\u003c/p\u003e \u003cp\u003eRegion 10 exhibited a very particular behavior, i.e. it was extremely enriched at the beginning of the run (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, B). As shown in Additional file 1: Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA, this high enrichment during the first hours of the run corresponded to poor sequencing in the WGS approach. Furthermore, within a period of just 10 h, the NAS approach provided a sequence depth comparable to that achieved in the entire WGS run (Additional file 1: Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA). In addition, the washing flush contributed to an increase in pore activity in both the NAS and WGS approaches (Additional file 1: Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eWe sought to confirm the applicability of NAS in targeting the entire spectrum of NLR clusters in melon by extending its use to Doublon, i.e. a cultivar of the same subspecies as Anso77 but belonging to a distinct botanical group. Similar to Anso77, the NAS sequence depth on the target regions at the end of the run was always 2.25 to 4.65-fold greater than that obtained with the WGS approach (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC, D). The least (region 06) and most (region 10) enriched regions were the same for both cultivars. Notably, the enrichment by yield presented a Kendall\u0026rsquo;s coefficient of concordance (W) of 0.87 when comparing Anso77 and Doublon (p 0.04), suggesting region-specific patterns rather than cultivar-based differences.\u003c/p\u003e \u003cp\u003eTarget regions of Doublon were sequenced and mapped with a ratio (0.98) identical to that of the rest of the genome in WGS (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Regarding enrichment by selection in Doublon, we obtained values ranging from 43.52- to 83.89-fold, representing a significant correlation with the values previously obtained for Anso77 (W\u0026thinsp;=\u0026thinsp;0.89; p 0.04). Overall, we demonstrated an average enrichment by yield of 3.73 for all the target regions and an average enrichment by selection of 69.92 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In terms of the temporal enrichment patterns, the results were consistent with those obtained for Anso77 regarding both enrichment by yield and by selection (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC, D).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary statistics of the NAS and WGS runs for Anso77 and Doublon.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAnso77\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eDoublon\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWGS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWGS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNAS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage on-target depth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e118.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage off-target depth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelative frequency of target regions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage enrichment by yield\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage enrichment by selection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOnly the chromosomes including target regions were considered for calculating the average off-target depth.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eNANOPORE ADAPTIVE SAMPLING FACILITATES THE CORRECT ASSEMBLY OF NLR CLUSTERS IN ANSO77 AND DOUBLON GENOMES\u003c/h2\u003e \u003cp\u003eNAS-enriched reads provided very contiguous and accurate assemblies of the target regions in both Anso77 and Doublon. Each cultivar presented a single contig per target region. The NAS assembly metrics are outlined in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Notably, the size of all contigs was over that of their corresponding target regions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnso77 and Doublon NAS assembly metrics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNAS reference file\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnso77\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDoublon\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChang-Bougi\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of sequences\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAssembly total size\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6,158,453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7,022,836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7,137,869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6,684,108\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e%GC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegion 01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e212,929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e267,987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e288,544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e283,266\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegion 02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47,063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e111,515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e145,599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e103,652\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegion 03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40,749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e92,325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e109,144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62,015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegion 04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40,775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88,396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e91,470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e65,369\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegion 05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,377,846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,442,885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,469,924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,422,416\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegion 06\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40,821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80,095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e94,491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e87,574\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegion 07\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e976,405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,030,491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,002,692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e877,386\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegion 08\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,038,033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,109,994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,047,373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,011,255\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegion 09\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40,827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93,035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e116,040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76,550\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegion 10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40,750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e95,015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e127,865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e97,965\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegion 11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e85,915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e132,117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e154,578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e146,542\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegion 12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40,780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e127,510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e110,582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e93,626\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegion 13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e170,103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e218,950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e239,877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e233,624\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegion 14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,039,037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,106,869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,072,236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,052,862\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegion 15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e966,420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,025,652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,067,454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82,354 \u0026amp; 987,652\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRegions of the NAS reference file represent the size (bp) of the target regions provided to the sequencer. Regions of Anso77, Doublon and Chang-Bougi represent the size (bp) of the contigs matching those regions.\u003c/p\u003e \u003cp\u003eWe generated dot plots for Anso77 and Doublon comparing each assembled contig with the corresponding region in the whole genome assemblies we produced (Additional file 1: Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). There was perfect collinearity in the dot plot of each target region, thereby confirming the accuracy of the NAS assemblies with regard to the reference genomes. Notably, the same number of NBS domains at the same positions were predicted in the NAS assembly compared to the reference genome for both cultivars.\u003c/p\u003e \u003cp\u003eWe focused on the well-known \u003cem\u003eVat\u003c/em\u003e region to further assess the accuracy of the NAS assemblies Dot plots representing the \u003cem\u003eVat\u003c/em\u003e regions are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The dot plots highlighted the complexity of this area with numerous duplicated sequences, but a perfect diagonal was noted between the reference and NAS-assembled sequences. Moreover, we checked the sequence of the \u003cem\u003eVat\u003c/em\u003e homologs previously sequenced by Sanger sequencing (cDNA sequencing). Among the five Anso77 homologs, \u003cem\u003eAN-Vat1, AN-Vat2\u003c/em\u003e, \u003cem\u003eAN-Vat3\u003c/em\u003e and \u003cem\u003eAN-Vat5\u003c/em\u003e fully matched the assembly generated from the NAS library, while \u003cem\u003eAN-Vat4\u003c/em\u003e contained one SNP in the first exon (G/T on position 402710). This overcomes the reference assembly presented here, which contained one SNP in \u003cem\u003eAN-Vat4\u003c/em\u003e as well as one SNP in \u003cem\u003eAN-Vat1.\u003c/em\u003e For Doublon, the three \u003cem\u003eVat\u003c/em\u003e homologous presented 100% DNA sequence similarity with the NAS assembly. Overall, we demonstrated that NAS produced very contiguous and accurate assemblies in highly complex resistance gene clusters.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eTOWARDS A BENCHMARK PROCEDURE: ENRICHMENT AND ASSEMBLY OF NLR CLUSTERS FROM A DISTANT CULTIVAR PROVIDE VERY VALUABLE STRUCTURAL INFORMATION\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe obtained 3.32\u0026nbsp;million reads and 3.45 Gb for Chang-Bougi in a 1/10 flowcell. After eliminating the reads rejected by adaptive sampling and filtering by quality and 1 kb length, 96.62 K target reads and 0.80 Gb were available for further processing. These reads exhibited an N50 of 13.75 kb, i.e. comparable to that obtained for Anso77 and Doublon. Rejected reads had an average length of ~\u0026thinsp;790.52 bp, which was longer than that obtained for Anso77 and Doublon due to the updated sequencing speed (400 bp/s) in the last MinKNOW version.\u003c/p\u003e \u003cp\u003eThe sequence depth mapped on the assembled contigs of the target regions averaged 41.82X. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, this depth varied between regions, with the highest depth obtained in region 05 (50.37X) and the lowest in region 03 (21.08X). However, the sequence depth obtained between regions kept a significant concordance with that obtained for Anso77 and Doublon (Additional file 1: Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e-left) (W\u0026thinsp;=\u0026thinsp;0.78; p\u0026thinsp;=\u0026thinsp;0.003).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor Chang-Bougi, the NAS-enriched read assemblies obtained with SMARTdenovo resulted in 17 contigs. Notably, we observed an inversion of \u0026asymp;\u0026thinsp;100 kb in region 01 compared to Anso77 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). Region 13 and region 15 were fragmented into two contigs. Among the tested assemblers, Canu generated a contiguous assembly of region 13 in a single contig (Additional file 1: Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). Consequently, we retained region 13 from the Canu assembly. No assembler succeeded in reconstructing region 15 into a single contig. We then investigated why region 15 was fragmented into two contigs, but we were unable to conclude after contig alignment to the published Illumina-based assembly due to its high degree of fragmentation. As Chang-Bougi belongs to the \u003cem\u003emakuwa\u003c/em\u003e botanical group, we mapped the two contigs to the genomes in this group for which open-access data was available: Early Silver Line, Ohgon and Sakata's Sweet [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. We identified a very large and size-conserved insertion (ranging from 862 to 871 kb) at the breakpoint between the two contigs obtained for Chang-Bougi (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). No NBS domain was predicted in this insertion on the Early Silver Line, Ohgon and Sakata's Sweet genomes. We could not recover this insertion in Chang-Bougi as it was not present in the provided reference. The total assembly size was 6.68 Mb. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the detailed NAS assembly metrics.\u003c/p\u003e \u003cp\u003eIn the Chang-Bougi draft genome generated by Shin et al. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], we identified 81 NBS domains spanning 18 contigs. These contigs matched the previously identified 15 ROIs of the NAS assembly with no extra clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). We identified 83 NBS domains in the NAS assembly. The two extra NBS domains predicted in the NAS assembly were located in region 08, one in the \u003cem\u003eVat\u003c/em\u003e region and the other outside. We manually annotated the \u003cem\u003eVat\u003c/em\u003e region of both Chang-Bougi assemblies (NAS and published) and some discrepancies were detected in the complex and repetitive area between \u003cem\u003eVat1\u003c/em\u003e and \u003cem\u003eVatRev\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA). In the NAS assembly, we identified the extra NBS domain within the \u003cem\u003eVat\u003c/em\u003e region as being a \u003cem\u003eVat\u003c/em\u003e homolog with four R65aa motifs (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA). We confirmed the presence and structure of this \u003cem\u003eVat\u003c/em\u003e gene with four R65aa motifs, as well as the presence of \u003cem\u003eVat\u003c/em\u003e genes with three R65aa through PCR using the published Z649FR and Z1431FR primers (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB). Finally, the presence of long reads encompassing the \u003cem\u003eVat1\u003c/em\u003e:\u003cem\u003eVat2\u003c/em\u003e and \u003cem\u003eVat2\u003c/em\u003e:\u003cem\u003eVat3\u003c/em\u003e gene pairs confirmed the NAS-assembled structure.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe findings of the present study highlighted that NAS is a promising approach for studying polymorphism in complex genomic ROIs. We used melon as a model to select highly diverse ROIs in terms of size and NLR gene content. We generated two \u003cem\u003ede novo\u003c/em\u003e assemblies and used one of them as reference for the adaptive sampling experiment. We selected the two accessions based on their different responses to the most studied pathogens at INRAE GAFL. Both \u003cem\u003ede novo\u003c/em\u003e assemblies presented great quality and completeness, comparable to the best published melon assemblies to date [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Similarly, predicted gene content values were in the range of previously melon published assemblies (Additional file 2: Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral factors can influence the NAS efficiency, two of which we took into account prior to launching the NAS experiments. First, we hypothesized that there is a direct relationship between the ideal size of sequenced fragments and the ROI size (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). Given that here the ROI sizes ranged from \u0026asymp;\u0026thinsp;41 to \u0026asymp;\u0026thinsp;1378 kb, we used standard DNA extraction (10\u0026ndash;30 kb), which we expected would lead to a more stable sequencing depth in ROIs than ultra-long reads (100\u0026ndash;300 kb) for the same yield. We felt that this approach would reduce off-target sequencing and avoid channel blockage when rejecting very long reads, as outlined in the ONT recommendations [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Second, REs span a major portion of the melon genome [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], and are especially common in NLR gene clusters [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. To avoid sequencing off-target REs with high sequence similarity to those within the initial target regions, we assumed that masking repetitive elements within the provided target regions would reduce the quantity of off-target data, thereby enhancing enrichment. We masked 0.93 Mb of repetitive sequences over the entire 6.16 Mb target length. Masking REs in genomes prior to NAS has been previously suggested [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe assessed the efficacy of NAS compared to WGS. One key factor that may have a major effect on the final yield in ONT sequencing runs is the number of active channels at the beginning of the run and their lifespan, which may differ markedly between flowcells. Therefore we implemented a half-flowcell design to compare NAS and WGS with respect to eliminating biases that may arise when using two different flowcells, as previously revealed in many studies [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The results showed that for both Anso77 and Doublon, NAS produced about fourfold more on-target data than WGS (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), while generating about tenfold less total data (see filtered NAS and WGS in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Yet the sequence depth between target regions was more variable in NAS than in WGS, but this did not compromise accurate ROI assembly in all the cultivars. We found no correlation between target size and sequence depth (Additional file 1: Figure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003eA) but there was a moderate correlation between the percentage of masking and the sequence depth, as expected (Additional file 1: Figure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eWe proposed two enrichment measurements adapted from previous studies in which NAS was tested on metagenomics samples or panels of many small genomic regions [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]: enrichment by yield, a simple widespread metric; and enrichment by selection, a metric that is not biased by the sequencing behavior of each target region. These enrichment measures made sense in our study because our goal was to increase the coverage in complex ROIs to generate more accurate assemblies. We achieved up to 3.7-fold average enrichment by yield and up to 69-fold average enrichment by selection, even when the reference was genetically distant from the sequenced accession. These findings were comparable to the best results obtained in previous studies involving the enrichment of individuals in metagenomics samples [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], and they were better than the enrichment values previously obtained with loci panels [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Our successful enrichment with NAS could have been linked to the percentage of the genome targeted here (~\u0026thinsp;1.41%), the target sizes or the DNA fragment sizes, which have been demonstrated to be key factors in determining the enrichment rate [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In addition, the fact that we performed a late nuclease flush when the percentage of sequencing pores was around 10% rather than at a fixed time might have contributed to the good performance [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. The temporal enrichment patterns of the different target regions were higher and more variable at the beginning of the run but then generally stabilized after 70 h (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Actually, channel inactivation occurred faster on the NAS half-flowcell, which could have been due to the repetitive potential flipping to reject off-target sequences or simply because the likelihood of channel clogging is statistically related to the number of sequenced molecules [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn previous studies, NAS has been used to enrich specific species in metagenomics samples [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e] and relatively small sequences within an organism, e.g. panels of exons or of key variant loci [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Here we demonstrated the power of NAS as a tool for enriching ROIs representing isolated NLR genes or complex NLR gene clusters in a plant crop species. The correct assembly of these complex regions typically requires long reads, such as those generated by ONT sequencing, or dedicated laborious approaches such as the resistance gene enrichment sequencing (RenSeq) method [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. In fact, NLR genes may sometimes be miss-predicted, especially when short-read sequencing technologies are used. Two to four functional NLR genes and some pseudo NRLs were found when deciphering the \u003cem\u003eVat\u003c/em\u003e region in the DHL92 genome [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. When screening for these NLRs in the early released DHL92 genome, they were found to be misassembled. It was only when a high-quality genome (with long reads, optical maps or HiC) was released [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], that the \u003cem\u003eVat\u003c/em\u003e gene assemblies were finally in line with those obtained via Sanger sequencing using long-range PCR [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Moreover, we compared the \u003cem\u003eVat\u003c/em\u003e cluster in the Chang-Bougi cultivar derived from short WGS [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] and long NAS reads. We showed that the WGS assembly was erroneous in terms of the homologous gene numbers and sequences (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e) and that the NAS assembly accurately reconstructed the region.\u003c/p\u003e \u003cp\u003eNLR genes are located in the dispensable portion of the genome [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e], and therefore the NLR reference used for NAS should be carefully selected when targeting the NLRome of a species. Our predictions of the number of NBS domains in Anso77 and Doublon, alongside all previously published melon genome assemblies, consistently yielded similar values (Additional file 2: Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). In all cases, we did not detect more than 15 groups of NLR genes regardless of the subspecies assessed, i.e. \u003cem\u003emelo\u003c/em\u003e or \u003cem\u003eagrestis\u003c/em\u003e. These findings indicated a well-conserved number and location of NLR genes in melon. We chose Anso77\u0026mdash;a Spanish cultivar belonging to ssp. \u003cem\u003emelo\u003c/em\u003e and the \u003cem\u003einodorus\u003c/em\u003e botanical group\u0026mdash;as reference for the NAS approach because it contained the highest number of \u003cem\u003eVat\u003c/em\u003e homologs within the \u003cem\u003eVat\u003c/em\u003e region used for benchmarking [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Our results obtained with Doublon and Chang-Bougi suggested that our strategy was suitable. Doublon is a French melon line belonging to ssp. \u003cem\u003emelo\u003c/em\u003e, and the \u003cem\u003ecantalupensis\u003c/em\u003e botanical group. When NAS was used without any short-read polishing, we obtained a \u003cem\u003eVat\u003c/em\u003e cluster with CDSs identical to those derived from an assembly using HW-DNA, PacBio and ONT long sequences, Illumina short sequences and optical maps. Chang-Bougi is a Korean melon line belonging to ssp. \u003cem\u003eagrestis\u003c/em\u003e and the \u003cem\u003emakuwa\u003c/em\u003e botanical group. The \u003cem\u003eVat\u003c/em\u003e cluster we obtained using NAS was highly consistent with that of PI 161375 [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], a Korean line belonging to ssp. \u003cem\u003eagrestis\u003c/em\u003e. However, a limitation was noted regarding very large SVs not present in the reference, i.e. that identified in Chang-Bougi chromosome 11 which turned out to belong to the oriental melon clade. Using different high-quality reference genomes or even combining them in an \u0026ldquo;artificial\u0026rdquo; reference genome could address this shortcoming. Existing software packages such as BOSS-RUNS [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] already enable dynamic updating of decision strategies during a run, thereby enhancing the good balance of the target regions or multiplexed sample sequencing depth. Yet it is unclear whether NAS would be able to discover extra NLR gene clusters if they were to exist. This should be possible if the additional NBS domains are sufficiently conserved to match the provided reference.\u003c/p\u003e \u003cp\u003eOverall, the findings of our study provide a blueprint for the selective capture of NLRome in melon and it could be extended to other key crop species. NLR gene numbers are generally low in the Cucurbitaceae family [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], and they represent an ideal percentage of the genome to be targeted using NAS. However, this ideal situation does not correspond to reality in other species, as the number of NLR genes is highly variable between plant species independently of their genome size [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. To adapt the NAS procedure implemented here for NLR-rich plant species, certain adjustments should be made to align with the ideal targeted percentage of the genome. First, a reduction in the length of flanking regions surrounding ROIs is recommended. Subsequently, a more rigorous definition of NLR clusters would help reduce the targeted percentage of the genome. Finally, strict NAS targeting of clustered NLRs could be done, recovering the easiest-to-assemble isolated NLRs with the low-pass reads rejected by NAS.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eNAS offered flexible real-time enrichment of selected NLR gene clusters while reducing costs as compared to the WGS approach. This target enrichment did not require any laborious or expensive library preparation, nor probe design and synthesis, unlike previously developed target sequencing methods. This is particularly advantageous for researchers who may not have access to special molecular biology techniques or who seek to conduct in-field experiments. NAS only requires an ONT sequencing device (e.g. the low-cost MinION device), a reference genome, and one or several ROIs. In addition, the fast enrichment obtained here may be of marked interest when time is a critical factor. Moreover, we highlighted the ability of NAS to reduce the high off-target data volume generated by WGS, addressing the growing challenges of data management and storage in the field of bioinformatics and genomics. This method, which we validated here on three melon cultivars, shows promise for application when dealing with a large number of accessions. This is particularly relevant for breeding applications as it opens avenues for creating multi-resistant varieties by tapping into the NLRome diversity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during the current study are available in the following repositories:\u003c/p\u003e\n\u003cp\u003eThe sequencing data and final consensus sequences of the WGS of Anso77 and Doublon are available at the NCBI database under BioProjects PRJNA662717 and PRJNA662721. Anso77: BioSample SAMN16093315, Accessions SRX9241647-SRX9241650, SRX24764327 for ONT and Illumina datasets, SUPF_0000005611 for BioNano Maps, and JBEGDE000000000 for genome assembly. Doublon: BioSample SAMN16093377, Accessions SRX9347576, SRX9235348 and SRX24764320 for PacBio, ONT and Illumina datasets, SUPF_0000005610 for BioNano Maps, and JBDXSX000000000 for genome assembly.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe sequencing data generated during NAS experiments are available at the NCBI databases under BioProject PRJNA1127998. Anso77: BioSample SAMN16093315, Accession SRX25061202 for ONT dataset (including reads from the half targeted and half WGS flowcell configuration). Doublon: BioSample SAMN42022058, Accession SRX25061203 for ONT dataset (including reads from the half targeted and half WGS flowcell configuration). Chang-Bougi: BioSample SAMN42022059, Accession SRX25061204 for ONT dataset (targeted sequencing). The targeted sequencing assemblies for the three accessions are available at the Recherche Data Gouv database (https://entrepot.recherche.data.gouv.fr/) under DOI https://doi.org/10.57745/ZALVPU.\u003c/p\u003e\n\u003cp\u003eThe functional annotations of Anso77 and Doublon \u003cem\u003ede novo\u0026nbsp;\u003c/em\u003ewhole genome assemblies, together with the scripts used for data analysis and genome assemblies, are available at the GitLab page indicated in the scripts_availability.txt file included in the Recherche Data Gouv database, DOI https://doi.org/10.57745/ZALVPU.\u003c/p\u003e\n\u003cp\u003eThe large sequencing_summary.txt files used for read filtering by end reason prior assembly are available from the corresponding author on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was partly funded by the French \u003cem\u003eMinist\u0026egrave;re de l\u0026rsquo;agriculture et de la souverainet\u0026eacute; alimentaire\u0026nbsp;\u003c/em\u003e(\u003cem\u003eVat\u003c/em\u003e\u0026amp;Co project - CASDAR \u0026minus; 2017-2021) and the French National Research Institute for Agriculture, Food and Environment (INRAE). The doctoral position of Javier Belinchon-Moreno is co-funded by the INRAE BAP Department and the EUR Implanteus of Avignon University, France.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ.B.M. performed the NAS sequencing experiments, the bioinformatics and statistical analyses, and NAS assemblies. P.F.R., N.B. and D.H. conceived the study. J.L., V.C., A.C. designed and identified the ROIs for NAS. A.B. and I.L. generated the ONT, Illumina and 10x genomic data for the whole genome assemblies. W.M. generated BioNano data for the whole genome assemblies, and participated in the hybrid scaffolding. J.L. and R.F.L. performed the whole genome assemblies. S.E. provided expertise and bioinformatics support. C.C. provided expertise and experimental support. V.R.R. manually annotated the \u003cem\u003eVat\u003c/em\u003e cluster and conducted the PCR experiments. J.B.M., P.F.R., N.B. and D.H. wrote the manuscript. J.B.M. and A.C. and J.B.M did the data submission. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Isabelle Dufau (INRAE-CNRGV) for supplying high-quality HMW DNA and optical maps for Anso77 and Doublon. Moreover, we are grateful to Valerie Barbe for advice concerning the manuscript.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLee RRQ, Chae E. Variation Patterns of NLR Clusters in Arabidopsis thaliana Genomes. Plant Commun. 2020;1(4):100089.\u003c/li\u003e\n\u003cli\u003eMohamed M, Dang NTM, Ogyama Y, Burlet N, Mugat B, Boulesteix M, et al. A transposon story: From TE content to TE dynamic invasion of Drosophila genomes using the single-molecule sequencing technology from Oxford Nanopore. 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Cell. 2019;178(5):1260\u0026ndash;72.\u003c/li\u003e\n\u003cli\u003eHuang Z, Qiao F, Yang B, Liu J, Liu Y, Wulff BBH, et al. Genome-wide identification of the NLR gene family in Haynaldia villosa by SMRT-RenSeq. BMC Genomics. 2022;23(1):118. \u003c/li\u003e\n\u003cli\u003eVendelbo NM, Mahmood K, Steuernagel B, Wulff BB, Sarup P, Hovm\u0026oslash;ller MS, et al. Discovery of resistance genes in rye by targeted long-read sequencing and association genetics. Cells. 2022;11(8):1273. \u003c/li\u003e\n\u003cli\u003eAdams TM, Smith M, Wang Y, Brown LH, Bayer MM, Hein I. HISS: Snakemake-based workflows for performing SMRT-RenSeq assembly, AgRenSeq and dRenSeq for the discovery of novel plant disease resistance genes. BMC Bioinformatics. 2023;24(1):204.\u003c/li\u003e\n\u003cli\u003eGarcia-Mas J, Benjak A, Sanseverino W, Bourgeois M, Mir G, Gonz\u0026aacute;lez VM, et al. The genome of melon (Cucumis melo L.). Proc Natl Acad Sci U S A. 2012;109(29):11872\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eShang L, Li X, He H, Yuan Q, Song Y, Wei Z, et al. A super pan-genomic landscape of rice. Cell Res. 2022;32(10):878-96.\u003c/li\u003e\n\u003cli\u003eBaggs E, Dagdas G, Krasileva K. NLR diversity, helpers and integrated domains: Making sense of the NLR IDentity. Curr Opin Plant Biol. 2017;38:59\u0026ndash;67. https://doi.org/10.1016/j.pbi.2017.04.012\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Melon, Nanopore adaptive sampling, targeted sequencing, resistance genes, NLR, genome assembly","lastPublishedDoi":"10.21203/rs.3.rs-4828883/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4828883/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eNanopore adaptive sampling (NAS) offers a promising approach for assessing genetic diversity in targeted genomic regions. Here we designed and validated an experiment to enrich a set of resistance genes in several melon cultivars as a proof of concept.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe showed that, using a single reference, each of the 15 regions we identified in two newly assembled melon genomes (ssp. \u003cem\u003emelo\u003c/em\u003e) was also successfully and accurately reconstructed in a third ssp. \u003cem\u003eagrestis\u003c/em\u003e cultivar. We obtained fourfold enrichment regardless of the tested samples, but with some variations according to the enriched regions. The accuracy of our assembly was further confirmed by PCR in the \u003cem\u003eagrestis\u003c/em\u003e cultivar. We discussed parameters that could influence the enrichment and accuracy of NAS generated assemblies.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eOverall, we demonstrated that NAS is a simple and efficient approach for exploring complex genomic regions. This approach facilitates resistance gene characterization in a large number of individuals, as required when breeding new cultivars suitable for the agroecological transition.\u003c/p\u003e","manuscriptTitle":"Nanopore adaptive sampling to identify the NLR gene family in melon (Cucumis melo L.)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-28 15:30:06","doi":"10.21203/rs.3.rs-4828883/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-12-09T10:54:59+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-08T09:45:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"180587170332839588641944006782516036976","date":"2024-12-02T16:58:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-12T09:50:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"52667104285259304527003777345218974932","date":"2024-10-09T15:22:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"250112762813267791818199920345417519578","date":"2024-10-01T20:58:59+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-02T12:43:41+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-08-02T06:23:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-02T01:56:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-02T01:55:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Genomics","date":"2024-07-30T12:42:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ad4895f2-e468-4446-a64e-22c3e3039f49","owner":[],"postedDate":"August 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-02-17T16:04:10+00:00","versionOfRecord":{"articleIdentity":"rs-4828883","link":"https://doi.org/10.1186/s12864-025-11295-5","journal":{"identity":"bmc-genomics","isVorOnly":false,"title":"BMC Genomics"},"publishedOn":"2025-02-10 15:57:54","publishedOnDateReadable":"February 10th, 2025"},"versionCreatedAt":"2024-08-28 15:30:06","video":"","vorDoi":"10.1186/s12864-025-11295-5","vorDoiUrl":"https://doi.org/10.1186/s12864-025-11295-5","workflowStages":[]},"version":"v1","identity":"rs-4828883","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4828883","identity":"rs-4828883","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}