NLRome assemblies of Cucumis melo derived from Nanopore adaptive sampling sequencing

[Lien vers la fiche source] The dataset contains genome assembly and annotation data of the complete set of NLR clusters (NLRome) from several melon accessions. This data was generated during the PhD project of Javier BELINCHON-MORENO at INRAE-EPGV and INRAE-GAFL. NLR clusters of Cucumis melo were obtained using the long-read targeted sequencing method "adaptive sampling (NAS)" from Oxford Nanopore Technologies. We identified 15 NLR clusters using NLGenomeSweeper v. 1.2.1 in the recently assembled whole-genome sequence (WGS) of ANSO-77 (NCBI BioProject PRJNA662717), which served as the reference for the adaptive sampling approach. Targeted assemblies were performed using SMARTdenovo v. 2018.2.19, and if a contiguous contig for each cluster was not achieved, we attempted assembly with Canu v. 2.2, Flye v. 2.9.1, Shasta v. 0.11.1, or NextDenovo v. 2.5.0. Later, using NLR annotation tools like NLRAnnotator v. 2.1b, and analysing the NLR set of previously released Cucumis melo assemblies, we discovered that six isolated NLR genes were missed in the reference used for adaptive sampling. To recover these genes, we used the rejected NAS reads along with previously available WGS short reads for each accession, performing reference-guided assemblies with the software Geneious and using the ANSO-77 assembly as reference. Each FASTA file corresponds to the assembly of the complete set of NLR clusters of one accession. In the FASTA file, sequence headers present the following structure: accesionnumber#haplotype#contigname_clusterposition, where accesionnumber is the code of the accession; haplotype is the haplotype strand -1 or 2- (normally just 1 appears as sequenced accessions are diploid and mainly homozygous); contigname is the name of the assembled contig (contigs starting by "utg", "tig", "ctg", "contig", number, and "guided" were assembled with SMARTdenovo, Canu, NextDenovo, Flye, Shasta, and Geneious, respectively); and clusterposition is the position of the assembled cluster with respect to the reference genome, ANSO-77 (as established in the file "clusters_positions_ANSO-77.txt"). Cluster gene annotations were performed with Helixer v. 0.3.3, and this annotation was complemented with an expert manual annotation of Vat genes between the M5 and M4 markers (Chovelon et al., 2021). Final annotations (GFF files) are a combination of manual annotation between M5 and M4 markers, and Helixer annotation for the rest of NLR zones. GENERAL METADATA Project description - Target sequencing and assembly of NLR clusters Organism - Cucumis melo Sequencing platform - PromethION (Oxford Nanopore) for long-reads, Illumina NovaSeq 6000 or DNBSEQ-G400RS for short-reads PromethION flowcell version - R10.4.1 DETAILED METADATA - Sample-related metadata is available in the attached file: metadata_samples.txt - Sequencing-related metadata is available in the attached file: metadata_sequencing.txt - Assembly-related metadata is available in the attached file: metadata_assembly.txt - Detailed explanation about the sequencing and assembly of each accession is available in the attached file : README.txt ABSTRACT Understanding and characterizing the defense mechanisms of plants against pathogens represent a major challenge for sustainable agriculture and food production. It facilitates the creation of varieties with a very broad spectrum of resistance by maximizing the use of the known defense mechanisms. In this sense, the structural and functional characterization of the complete set of Nucleotide-binding-site-Leucine-rich-Repeat (NLR) disease resistance genes in a species (or NLRome) becomes especially interesting. NLR genes encode the most diverse family of plant resistance proteins. They play a central role in the so-called effector triggered immunity of plants by recognizing specific pathogen effectors. In melon (Cucumis melo L.), a highly important and widely cultivated vegetable crop belonging to the Cucurbitaceae family, the specific role of each NLR gene remains largely unknown, especially in relation to quantitative resistances. This lack of information is a consequence of a poor sequencing, assembly and annotation of these genes using short-reads sequencing, due to their complex structure and organization. NLR genes are often arranged in clusters that include a large number of repetitive elements. This fact, together with the repetitive intra-structure of NLR genes (domain leucine-rich-repeat) makes them prone to major evolutionary structural changes like duplication or transposition. For these reasons, NLR clusters commonly present a high level of presence-absence (PAV) polymorphisms. Short reads are usually ineffective to characterize them, but long reads sequencing methods may provide a very valuable information. However, they can still result expensive at wetlab, bioinformatics and data storage level when a large number of samples need to be evaluated. Nanopore Adaptive Sampling (NAS) is presumed to be a good approach here, since it can reduce the quantity of information to manage while increasing the coverage of the target regions compared to a whole genome sequencing (WGS). In addition, it is a cost and labor-effective solution compared to other complex and time-consuming targeted sequencing methods. NAS offers a promising approach for assessing genetic diversity in targeted genomic regions. We designed and validated an experiment to enrich a set of resistance genes in several melon cultivars as a proof of concept. We showed that each of the 15 regions we identified in two newly assembled melon genomes -ANSO-77 and DOUBLON- (subspecies melo) were successfully and accurately reconstructed as well as in a third cultivar from the agrestis subspecies (CHANGBOUGI). We obtained a fourfold enrichment, independently from the samples, but with some variations according to the enriched regions. In the agrestis cultivar, we further confirmed our assembly by PCR. By extending the use of NAS to other melon varieties, we demonstrated it as a simple and efficient approach to explore complex genomic regions. This approach finally unlocks the characterization of resistance genes for a large number of individuals, as required for breeding new cultivars responding to the agroecological transition.