Whole-genome sequencing of wild and ancestral Dura provides insight into genetic shifting during the domestication of oil palm (Elaeis guineensis Jacq.)

Whole-genome sequencing of wild and ancestral Dura provides insight into genetic shifting during the domestication of oil palm (Elaeis guineensis Jacq.) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Whole-genome sequencing of wild and ancestral Dura provides insight into genetic shifting during the domestication of oil palm ( Elaeis guineensis Jacq.) Redi Aditama, Heri Adriwan Siregar, Zulfikar Achmad Tanjung, Diny Dinarti, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6543597/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Nov, 2025 Read the published version in DNA Research → Version 1 posted You are reading this latest preprint version Abstract Oil palm ( Elaeis guineensis Jacq. ) is a vital global crop, and its genetic improvement benefits from comprehensive genome sequencing. This study presents the whole-genome sequencing and annotation of two key genetic resources: the wild (Eg-DCM) and ancestral (Eg-DBG) Dura accessions. Comparative analysis with the advanced breeding Dura (Eg-D1) provides insights into genome structure, gene content, and evolutionary shifts during domestication. Retroelements dominated both genomes, constituting 46.10% in Eg-DBG and 43.85% in Eg-DCM. Gene prediction found 61,256 and 53,985 genes, refined into high-confidence sets of 39,263 (Eg-DBG) and 35,298 (Eg-DCM). A decline in resistance genes (R-genes) was observed in Eg-D1, suggesting that selection pressures influenced their retention. Codon usage analysis revealed significant shifts, showing increased mutational bias, with a stronger correlation between GC content at the third (GC3) and the first and second (GC12) codon positions in Eg-D1. Moreover, the relationship between GC3 and the effective number of codons (ENC) suggests stronger selection pressure driving codon optimization, a trend commonly observed in domesticated crops. These findings highlight how artificial selection has shaped genome composition, resistance mechanisms, and translational efficiency. The assembled genomes provide essential resources for understanding oil palm evolution and optimizing breeding strategies for enhanced productivity and sustainability. Plant Molecular Biology and Genetics Comparative genomics Codon usage adaptation Domestication-driven selection Resistance gene evolution Transposable elements dynamics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction African Oil palm ( Elaeis guineensis Jacq.), also known as the "king of oil crops" 1 , is an essential global vegetable oil producer, contributing 38.7% of vegetable oil demand 2 . However, the oil palm plantations only cover 6.6% of the global oil cropland, making it the most efficient oil-producing crop 3 . Originating from Africa, oil palm has played a significant role in several Southeast Asian countries’ economy 4 . Oil palm is the Arecaceae family member produces triglycerides in its fruit mesocarp. Oil palm is classified into Dura (D), Pisifera (P), and Tenera (T) types based on their fruit shell thickness. The Dura type has a thick shell (2–8 mm), a low mesocarp percentage, and produces a little oil. The Pisifera type lacks the shell and thick mesocarp, but the fruit size is small and does not develop well. The Tenera type has a thin shell (0.5-4 mm) and a thicker mesocarp, resulting in a higher oil content 5 . The Dutch colonial government introduced the first oil palm in Southeast Asia in the mid-19th century. Four seedlings assumed to be the Dura type were transported from Amsterdam and Mauritius to Indonesia and planted at the Bogor Botanical Gardens in 1848 6 . The seeds produced by those palms were distributed to other areas, including the Deli Plantation Company in North Sumatra, for use as ornamental plants. The growing understanding of the commercial potential of oil palm led to the establishment of the first trial plantation in Deli in 1878, covering an area of 0.4 Ha, using seeds produced by those ornamental plants 7 . The trial findings indicated higher fruit production than in its indigenous habitat in Africa, encouraging numerous investors to establish new commercial plantations. In the first trial, plants were subjected to mass selection to produce seeds for the newly established plantations, and as a result, the Deli Dura population emerged. Until now, Deli Dura remains a key genetic source for creating superior oil palm varieties in Indonesia and Malaysia 8 . In the current era of molecular biology, genetic knowledge on a molecular scale is essential for understanding the mechanisms underlying oil palm traits and aiding breeders in swiftly and precisely selecting planting materials. Whole-genome sequencing has been carried out on modern Pisifera 9 , 10 and Dura 11 palms, benefiting the breeding process through the implementation of genomic selection 12 , 13 and marker-assisted selection approaches 14 , 15 . The whole genome sequence of Deli Dura's ancestor and wild-type Dura will be useful in understanding the genetic shift occurring in the oil palm selection process over the past century. In this study, we performed whole-genome sequencing on Eg-DBG ( Dura Botanical Garden), the first descendant of the four oil palms first planted in Bogor Botanical Garden, which is genetically equivalent to the Deli Dura ancestor, and Eg-DCM ( Dura Cameroon), a wild Dura palm originating from the Bamenda Region, Cameroon. We performed a genomic comparison of Eg-DBG, Eg-DCM, and an advanced breeding Dura (Eg-D1) 11 to investigate genetic shifts during oil palm domestication in Indonesia and Malaysia. To show evidence of genetic shifting, we compare several genomic elements, including gene content, functional classification, resistance genes (R-genes), and codon usage preferences, which may reveal the molecular impact of domestication and artificial selection on the oil palm genome. The findings of this study provide valuable genomic resources for oil palm breeding programs by offering insight into how domestication has shaped the genetic architecture of this important crop. Materials and Methods Plant material and DNA extraction This study used Eg-DBG, the first descendant of the progenitor tree V.L.2, one of the first four oil palms cultivated in the Bogor Botanical Gardens. Genealogically, Eg-DBG is equivalent to the oil palm accession planted at the Deli Plantation Company in North Sumatra in the 1870s. This study also used Eg-DCM, a wild oil palm with seeds collected from a wild grove of Bamenda Region, Cameroon. Fresh leaves were collected for DNA extraction using CTAB buffer. Subsequently, initial quantity and purity were observed using NanoDrop™ (Thermo Scientific) combined with DNA visualization using agarose gel electrophoresis. Accurate quantifications were performed using the Qubit dsDNA HS Assay Kit (Thermo Scientific). DNA quality checks were performed using the 4150 TapeStation (Agilent Technologies). Library preparation and whole-genome sequencing Total genomic DNA (gDNA) was used as the input for library preparations in both long- and short-read sequencing. For the long-read sequencing, gDNA was repaired using an end-repaired end prep enzyme mix, generating DNA with 5'-phosphorylated and 3'-dA-tailed ends. The repaired DNA was ligated with ONT-compatible adapters and quantified using a Qubit Fluorometer before being loaded onto the flow cell. Sequencing was performed using GridION (Oxford Nanopore Technologies) employing MinKNOW v23.07.12. Base calling was performed using Guppy v7.1.4 with high accuracy mode. For the short-read sequencing, gDNA was fragmented using an enzymatic method and ligated using an MGI-compatible adapter. Library preparations were conducted using the MGIEasy FS DNA Library Prep Set. Sequencing was conducted for 612 cycles (PE300) using the MGI DNBSEQ-G400. De novo assemblies and contig polishing Long-read quality control was performed using NanoPlot v1.33.0, and low-quality reads were trimmed using NanoFilt v2.8.0 16 . De novo assemblies were performed on the long reads employing Flye v2.9.3 with the default parameters 17 . Quality control, adapter removal, and low-quality short-read trimming were performed using fastp v0.23.4 using the default parameters 18 . Clean short reads were aligned to the assembled contigs using the BWA-MEM algorithm from BWA software v0.7.18 19 . Aligned reads were used to polish assembled contigs employing Pilon v1.24 20 . Proximity ligation and homology-based scaffolding Proximity ligation scaffolding was conducted using the Hi-C protocol and performed by Dovetail Genomics (California, USA). In short, formaldehyde was used to fix the chromatin in the nucleus of the libraries before it was extracted. DpnII was used to digest the fixed chromatin, biotinylated nucleotides were added to the 5' overhangs, and the free blunt ends were ligated. The DNA was separated from the protein by reversing the crosslinks. Biotin that was not intrinsic to the ligated segments was eliminated by treating the purified DNA. After the DNA was sheared to a mean fragment size of about 350 bp, NEBNext Ultra enzymes and Illumina-compatible adapters were used to create sequencing libraries. The sequencing was performed using the Illumina HiSeq X platform to produce Hi-C reads. Juicer v1.6 21 was used to map Hi-C reads to the contigs, identify the Hi-C interactions, and build the contact map. Identification of genomic structure and the scaffolding process was performed using 3D-DNA 22 . Assembled scaffolds were corrected and reassembled using a homology approach employing RagTag v2.1.0 23 with the EG11 genome sequence 10 as a reference. Evaluation of the completeness of the genome from contigs to chromosome-level assembly was assessed using the benchmarking universal single-copy orthologs (BUSCO) v4.1.5 24 against OrthoDB v10 datasets of Eukaryota (n = 255), Viridiplantae (n = 425), and Embryophyta (n = 1,614). Duplicated fragments in the genomes were analyzed through self-alignment of each assembly using NUCmer v3.1.0 25 . The GC profile for 1 Mb window interval was analyzed using BEDTools v2.31.1 26 . The SSR sequences from the two libraries were mapped to the assembled genomes to confirm the scaffolding result’s consistency using NCBI BLASTN v2.10.1 + 27 . The assembled genome’s chromosome structure, properties, and features were visualized using Circos v0.69-8 28 . The synteny of the assembled and the reference genomes was analyzed employing ntSynt v1.0.2 29 and visualized using ntSynt-viz v1.0.0 30 . Repeat annotation and gene prediction Repeat sequences in the assembled genomes were found using the combination of de novo identification, known repeat searching against databases, and tandem repeat search. RepeatModeler v2.0.6 31 was used to build models of repeat sequences. The models built and the Repbase 32 were mapped to assembled genomes using RepeatMasker v4.1.5 33 . In addition, the full-length LTR retrotransposons were analyzed by LTR_Finder v1.07 34 . Gene predictions were carried out utilizing the BRAKER pipeline v3.0.8 35–37 , which involves two primary tools: GeneMark 38 and Augustus 39 . Eight RNA-seq datasets were used as transcript evidence to predict the gene structure (Supplementary Table 1). In addition, putative oil palm protein sequences from the NCBI RefSeq assembly (ID: GCF_000442705.1) were used as evidence. The gene prediction results were filtered using a combination of three approaches to improve accuracy and minimize redundancy. First, sixteen RNA-seq data from the sequence read archives (SRA) database (Supplementary Table 2) were mapped to the predicted transcripts using HISAT2 v2.21 40 with the default options, followed by transcript quantifications employing StringTie v3.0.0 41 . Transcripts with FPKM values less than one were filtered out. Second, publicly available annotations of oil palm were downloaded from NCBI RefSeq with ID GCF_000442705.1 and mapped to the assembled genomes employing Liftoff v1.6.3 42 . Mapped annotations were compared with predicted genes employing gffread v0.12.7 43 . Any predicted gene not intersected by the mapped annotation was filtered out. Third, protein sequences of the predicted genes were aligned to the reviewed Swiss-Prot protein database ( https://uniprot.org ) employing NCBI BLASTP v2.10.1 + 27 . Any predicted genes with a bit score less than 50 and an e-value greater than 10 − 5 were filtered out. Functional analysis of predicted genes was performed using eggNOG-mapper v2 44 with eggNOG DB v5.0.2 45 as a reference. Annotation of resistance genes Resistance genes in three oil palm Dura genomes (Eg-DBG, Eg-DCM, and Eg-D1) were annotated using the Disease Resistance Analysis and Gene Orthology (DRAGO 3) pipeline 46 . LRR, Kinase, NBS, and TIR domains were detected from protein sequences using a 60 HMM database employing the HMMER v3 package ( https://hmmer.org ). In addition, coiled-coil (CC) and transmembrane (TM) domains were detected using the COILS v2.2 and the TMHMM v2.0C. Codon usage analysis Coding sequences (CDS) of annotated genes were extracted from assembled genomes using GFFRead v0.12.7 43 . Codon usage indices, including GC content, GC content at third position (GC3), GC content at first and second positions (GC12), effective number of codons (ENC), and correspondence analysis of codon usage were calculated using the CodonW v1.4.4 ( https://sourceforge.net/projects/codonw/ ). Visualization of codon usage matrices was conducted using the ggplot2 R package 47 . Results Genome assembly characteristics The draft genome sequences of Eg-DBG and Eg-DCM were constructed in this study using the combination of short reads from the MGI DNBSEQ-G400 and long reads from the ONT GridION platforms. De novo assemblies were performed on the long reads, followed by short-read polishing, resulting in contigs totalling 1.715 Gb and 1.486 Gb for Eg-DBG and Eg-DCM genomes, respectively (Table 1 ). In vivo proximity ligation data (Hi-C) were used to scaffold assembled contigs employing the pipeline involving Juicer and 3D-DNA. This process increased the N50 to 72.87 Mb for the Eg-DBG genome and 101.60 Mb for the Eg-DCM genome. The total genome length was increased for both genomes due to the addition of gap sequences in the scaffolding process. The assembly process then continued with an in-silico homology-based scaffolding employing the RagTag. The assembled scaffolds were initially corrected and reassembled using the EG11 genome sequence as a reference. The last process increases the N50 of the Eg-DBG genome from 72.87 to 92.50 Mb. Table 1 The statistics of the Elaeis guineensis Dura Bogor botanical Garden (Eg - DBG) and E. guineensis Dura Cameroon (Eg - DCM) genome assembly and scaffolding processes Genome Assembly Level Inputs Software Longest fragment (Mb) No. of fragment N50 (Mb) Genome length (Mb) Eg-DBG Contigs ONT + MGI Flye + Pilon 2.26 20,219 0.16 1,175 Scaffolds Contigs + Hi-C Juicer + 3D-DNA 121.83 21,920 72.87 1,724 Chromosome Scaffolds + EG11 RagTag 164.56 22,655 92.50 1,725 Eg-DCM Contigs ONT + MGI Flye + Pilon 3.29 16,128 0.21 1,486 Scaffolds Contigs + Hi-C Juicer + 3D-DNA 160.06 8,436 101.60 1,492 Chromosome Scaffolds + EG11 RagTag 155.64 8.305 96.77 1.492 On the other hand, the N50 of the Eg-DCM genome decreased from 101.60 to 96.77 Mb due to assembly correction. The quantitative assessment of genome assemblies' completeness was performed using the Benchmarking Universal Single-Copy Orthologs (BUSCO) v5.5.0 48 . The assessment showed that both assembled genomes had more than 99% completeness for the Eukaryote database and more than 97% for both Viridiplantae and Embryophyte databases (Fig. 1 ). A total of 591 simple sequence repeats (SSRs) from two previous studies 49 , 50 were mapped to ensure the consistency of genome assemblies. The distribution of SSRs in the assembled genomes is visualized in the second outermost circle of the Circos plot (Fig. 2 ). The mapped SSRs are colour-coded according to their original linkage group, where the same colour indicates that the SSRs originate from the same linkage group. The SSRs are distributed throughout all chromosomes, but certain regions exhibit higher accumulation. An interesting pattern in the SSR distribution is seen on chromosome 12, where a significant portion of SSRs shares the same linkage group as those on chromosome 14. The GC content distribution was analyzed using a 1 Mb sliding window and represented in the bar chart layer on the Circos plot (Fig. 2 ). Blue bars indicate regions with GC content higher than the genome-wide average, while red bars represent regions with lower GC content. The analysis reveals a heterogeneous distribution of GC content across chromosomes, with some areas showing significantly higher or lower GC proportions. The duplicated regions along the genome were analyzed through whole-genome self-assembly employing NUCmer v3.1.0 25 . The Circos plot highlights duplicated regions within assembled genomes through the synteny plot in the innermost layer. Notably, several duplicated regions coincide with gene-rich areas, implying that retained gene duplications may be functionally important. Additionally, some duplicated segments overlap with repeat-rich areas, suggesting that transposable elements might have helped the duplication events. Comparison to other assemblies Several publicly available oil palm genome sequences were downloaded, including EG5 ( Pisifera ) 9 , EG11 ( Pisifera ) 10 , and Eg-D1 ( Dura ) 11 , to perform a full genomic comparison. Since Eg-D1 was a draft assembly result, we used RagTag v2.1.0 51 to perform homology-based scaffolding on the sequence using EG11 as a reference. The characteristics of these five assembled genomes were compared based on various sequencing and assembly metrics (Table 2 ). The size of assembled genomes of Eg-DBG (1.73 Gb) and Eg-DCM (1.49 Gb) was lower than the 1.8 Gb measured by flow cytometry 9 . The lower number is due to DNA fragments in the genome that are not accessible to the sequencing methods used. In this metric, EG11 exhibits the most complete genome set with an assembled size of 1.84 Gb. The number and length of assembly gaps also influenced the size of assembled genomes. Gaps are introduced during the scaffolding process to keep the order and orientation of known fragments. The length of the ungapped sequence of EG11 is the highest (1,714,158,544 bp), but only slightly higher compared to Eg-DBG (1,712,233,623 bp). Compared to other assemblies, Eg-D1 has the least ungapped sequence, which is 553,149,332 bp. Table 2 Genome characteristics comparison between Elaeis guineensis Dura Bogor Botanical Garden (Eg-DBG), E. guineensis Dura Cameroon (Eg-DCM) and previously assembled oil palm reference E. guineensis Dura D1 (Eg-D1), E. guineensis Pisifera EG5 (EG5), and E. guineensis Pisifera EG11 (EG11). Characteristics Eg-DBG Eg-DCM Eg-D1 EG5 EG11 Genome size (bp) 1,725,021,623 1,492,598,792 1,702,041,784 1,535,178,229 1,841,922,575 Total un-gapped length (bp) 1,712,233,623 1,486,247,692 553,149,332 1,057,103,131 1,714,158,544 No. of gaps 29,862 15,729 337,489 166,490 37,524 N50 (bp) 92,499,198 96,770,559 74,098,475 1,268,079 128,314,321 No. of chromosome 16 16 16 16 16 No. of unplaced scaffolds 22,639 8,289 8,797 40,045 13 Max. seq. length (bp) 164,565,033 155,642,322 156,671,551 68,435,666 186,201,113 GC content 38.7 38.3 37.1 37.2 38.5 Sequencing coverage 48x 48x 95x 16x 60x N50 is a metric used to assess the contiguity of genome assembly. It stands for the length of the longest contig or scaffold where 50% of the total assembled genome length is contained. The N50 value of Eg-DBG (92,499,198 bp) and Eg-DCM (96,770,559 bp) were lower than that of EG11 (128,314,321 bp) but higher than that of EG5 (1,268,079 bp) and Eg-D1 (74,098,475 bp). The number of unplaced scaffolds influences the N50 value. The greater the number of unplaced scaffolds, the smaller the N50. EG11 has the smallest number of unplaced scaffolds, which is 13. Eg-DBG and Eg-DCM still have many unplaced scaffolds, 22,639 and 8,289, respectively. The GC content of Eg-DBG (38.7%) and Eg-DCM (38.3%) was consistent with that of EG11 (38.5%) and slightly higher compared to EG5 (37.2%) and Eg-D1 (37.1%). To investigate the structural variation and genomic synteny among different assemblies, we performed a whole-genome alignment analysis employing ntSynt v1.0.2 29 and visualization using ntSynt-viz v1.0.0 30 . The results, visualized in a ribbon plot (Fig. 3 ), reveal a high degree of genomic conservation among the assemblies, with extensive syntenic regions seen across all 16 chromosomes. The phylogenetic tree illustrates the genetic relationship among the four accessions. Eg-D1 appears to be the most distinct accession, while Eg-DBG and EG11 share a closer evolutionary relationship. Despite the high synteny, minor structural variations can be seen in certain chromosomal regions, particularly in chromosomes 1, 6, and 14, where slight disruptions suggest potential structural rearrangements, such as inversions or segmental duplications. Repeat annotation and gene prediction Repeated elements in the Eg-DBG and Eg-DCM genomes were analyzed using a combination of RepeatModeler 31 and RepeatMasker 33 , supplemented by the Repbase database. The results indicate that Retroelements are the dominant repeat class in both genomes, comprising 46.10% in Eg-DBG and 43.85% in Eg-DCM, with LTR elements being the most abundant subclass (Supplementary Table 3). DNA transposons account for a smaller proportion, around 2.71% in Eg-DBG and 2.83% in Eg-DCM. Interestingly, unclassified repeats also represent a substantial portion, suggesting the presence of novel or poorly characterized repeat sequences (Fig. 4 ). Additionally, simple repeats and low-complexity sequences contribute slightly more to Eg-DCM than Eg-DBG. Gene prediction was performed using the de novo method employing the BRAKER2 pipeline v3.0.8 35–37 . Publicly available oil palm transcriptomics data and protein sequences were used as evidence to support the prediction of the structure and splicing patterns of the genes. Initially, the prediction identified 61,256 genes in Eg-DBG and 53,985 genes in Eg-DCM (Table 3 ). Three filtering approaches were applied. The first was transcription-based filtering to refine these predictions and reduce potential false positives, which kept 24,456 genes in Eg-DBG and 23,779 genes in Eg-DCM. The second was reference-based filtering, which kept 25,650 genes in Eg-DBG and 21,504 genes in Eg-DCM. The third was protein filtering using the SwissPort database, which kept 38,361 genes in Eg-DBG and 35,454 genes in Eg-DCM. By integrating these filtering strategies, a final high-confidence gene set was obtained, consisting of 39,263 genes in Eg-DBG and 35,298 genes in Eg-DCM. On the other hand, the final mRNA counts from the annotations and filtering were 44,865 and 41,772 in Eg-DBG and Eg-DCM, respectively. Table 3 Gene and mRNA count after annotation and filtering in Elaeis guineensis Dura Bogor Botanical Garden (Eg-DBG) and E. guineensis Dura Cameroon (Eg-DCM) genomes Annotation Process Eg-DBG Eg-DCM Gene mRNA Gene mRNA BRAKER 61,256 67,303 53,985 59,981 Filtering: Transcriptome 24,456 28,148 23,779 27,602 Reference 25,650 30,744 21,504 30,014 Swissprot 38,361 33,401 35,454 30,435 Total filtered 39,263 44,865 35,298 41,772 The functional annotation of predicted genes was performed based on precomputed orthology assignments employing eggNOG-mapper and using eggNOG DB as a reference. The distribution of Cluster of Orthologous Groups (COG) categories reveals notable differences in gene distribution across categories (Fig. 5 ). Most annotated genes in both genomes fall under the function unknown category, showing many genes with uncharacterized functions. Among the well-defined categories, Replication , recombination , and repair showed the most significant difference, with Eg-DBG having a markedly higher proportion of genes than Eg-DCM. Other functional categories, such as translation, ribosomal structure, and biogenesis, exhibited relatively similar distributions between the two genomes. The Circos plot illustrates the distribution of genes and repetitive elements across Eg-DBG and Eg-DCM genomes (Fig. 2 ). The annotation results (blue line) and reference annotation (red line) reveal that genes are unevenly distributed along the chromosomes, with higher concentrations in euchromatic regions and lower densities in heterochromatic regions. Gene-rich regions often overlap with areas of higher GC content and show inverse correlations with Copia and Gypsy retrotransposon. On the other hand, a positive correlation between gene density and SINEs, LINEs, and simple repeats was observed. Resistance gene identification The resistance genes (R-genes) were found in three oil palm Dura accessions employing the DRAGO 3 pipeline. The analysis revealed a noticeable reduction in the total number and proportion of R-genes in Eg-D1 compared to the other two genomes (Table 4 ). Eg-DCM, the wild Dura from Cameroon, contained 1,684 R-genes, representing 4.03% of its total predicted proteins, while Eg-DBG, the parental Dura used in Indonesia, had 1,760 R-genes, comprising 3.93% of its total proteins. In contrast, Eg-D1, the modern Dura cultivar subjected to multiple selection cycles, exhibited a significant decrease, with only 1,575 R-genes making up just 2.60% of its total protein-coding genes. A more detailed examination of R gene classes revealed that certain categories experienced a sharp decline in Eg-D1. Notably, the CC-NBS (CN) and CC-NBS-LRR (CNL) classes associated with disease resistance were reduced. Eg-D1 held only 2 CN genes and 60 CNL genes, compared to 50 and 105 in Eg-DCM and 61 and 114 in Eg-DBG, respectively. The CC-Kinase (CK) and NBS-LRR (NL) classes also decreased Eg-D1, suggesting that selection may have influenced the retention of specific R-gene families. Interestingly, the CC-NBS-TIR (CNT) class displayed an opposing trend, with Eg-D1 showing a notable increase (29 genes) compared to its near absence in the other genomes. Table 4 Comparison of R-gene counts by class in Elaeis guineensis Dura Bogor Botanical Garden (Eg-DBG), E. guineensis Dura Cameroon (Eg-DCM) and previously assembled E. guineensis Dura D1 (Eg-D1) genomes R-Gene Major Class Domain Eg-DCM Eg-DBG Eg-D1 C CC 6 4 6 CK CC, Kinase 98 95 53 CL CC, LRR 13 12 17 CLECRK CC, Kinase, LECM, TM 3 4 6 CLK CC, LRR, Kinase 6 8 6 CN CC, NBS 50 61 2 CNL CC, NBS, LRR 105 114 60 CNT CC, NBS, TIR 1 0 29 CT CC, TIR 3 4 4 KIN Kinase 507 521 491 L LRR 42 46 89 LEC LECM, TM 42 32 47 LECRK LECM, Kinase, TM 136 143 86 LYK LYSM, Kinase, TM 11 8 6 LYP LYSM, TM 7 6 8 LYS LYSM 15 19 17 N NBS 105 121 126 NL NBS, LRR 54 57 45 RLK RLK 263 269 205 RLP RLP 176 185 227 T TIR 2 1 2 TRAN Transmembrane 39 50 43 No. of total R-genes 1,684 1,760 1,575 No. of total proteins 41,772 44,865 60,619 R-gene portions (%) 4.03 3.93 2.60 Abbreviations: CC: coiled-coil, LRR: leucine-rich repeat, TM: transmembrane, NBS: nucleotide binding site, TIR: Toll/interleukin-1 receptor, LYSM: lysin motif, RLK: receptor-like kinase, RLP: Receptor-like protein. Codon usage analysis The codon usage pattern of three Dura accessions was analyzed using CodonW v1.4.4 ( https://sourceforge.net/projects/codonw/ ), and the results are shown in Fig. 6 . The neutrality plots (Fig. 6 a) reveal that Eg-D1 exhibits a higher correlation between GC3 and GC12 than the other accessions, suggesting a greater influence of mutational pressure on codon usage. This pattern is often observed in domesticated plants, where reduced selective constraint can lead to a stronger reflection of underlying mutational biases. The relationship between GC3 and ENC (Fig. 6 b) follows a parabolic trend, showing the combined effects of mutational bias and selection, with Eg-D1 showing a tendency toward more optimized codon usage. Additionally, the correspondence analysis biplot (Fig. 6 c) highlights a distinct clustering pattern for Eg-D1, further supporting the idea that domestication has shaped its codon usage preferences. These findings suggest that artificial selection during domestication may have influenced codon usage in Eg-D1, leading to patterns that distinguish it from the wild or less domesticated accessions. Discussions Oil palm is the most efficient oil-producing crop and is crucial in meeting global vegetable oil demands 3 . Interestingly, most oil palms that exist today, particularly in Indonesia and Malaysia, originated from four parent trees planted in Bogor Botanical Gardens in 1848 6 . In this paper, we provide the results of high-quality whole-genome sequencing of Eg-DBG ( Dura Botanical Gardens), the first derivative of an oil palm mother tree from the Bogor Botanical Gardens, and Eg-DCM ( Dura Cameroon), a wild Dura palm obtained from a wild grove of Cameroon. The results of BUSCO analysis confirmed the quality of sequencing and assemblies, which showed high completeness for eukaryotes, Viridiplantae, and embryophyte taxa in both genomes. In addition, the total ungapped length of both assemblies shows a value close to 1.8 Gb, the measured size of the oil palm genome 9 . The mapping of SRR sequences on the assembled genome validates the quality of the chromosome structure, as most SSRs mapped on a chromosome belong to the same linkage group. The quality of the chromosome structure was also validated by whole-genome comparison, which showed a great amount of parallel synteny between the assembled genomes and the oil palm reference genome, EG11 10 . The comprehensive analysis of repetitive elements in the assembled genomes highlights the significant role of Retroelements as the dominant class of repeats in oil palm genomes. The predominance of LTR elements within this category aligns with previous findings in plant genomes, where these elements contribute to genome expansion and structural variation 52 , 53 . Although present at lower proportions (2.71% in Eg-DBG and 2.83% in Eg-DCM), DNA transposons may still play a role in genome plasticity and adaptation 54 . Notably, the large fraction of unclassified repeats shows the presence of novel or poorly characterized sequences, which may reflect lineage-specific expansion events or limitations in current repeat databases. The gene prediction analysis in the assembled genomes highlights the impact of different filtering approaches in refining high-confidence gene sets. The first de novo prediction identified a large number of genes (61,256 in Eg-DBG and 53,985 in Eg-DCM), but subsequent filtering steps significantly reduced the number of putative false positives, ultimately resulting in 39,263 genes in Eg-DBG and 35,298 genes in Eg-DCM. This refinement aligns with previous studies that emphasize the importance of transcriptomic and protein evidence in improving gene annotation accuracy 36 . The final mRNA counts, slightly higher than the total gene count, suggest alternative splicing events contributing to transcript diversity. The spatial distribution of genes across chromosomes, as revealed by the Circos plot, follows a typical pattern observed in plant genomes, where gene-rich regions are concentrated in euchromatic regions. In contrast, heterochromatic regions harbour fewer genes due to the dominance of repetitive sequences 55 . The observed inverse correlation between gene density and Copia/Gypsy retrotransposons further supports the role of transposable elements in genome organization and gene silencing 56 . Conversely, the positive correlation between gene-rich regions and SINEs, LINEs, and simple repeats may reflect their functional relevance in regulatory elements and genome stability 57 . The Cluster of Orthologous Groups (COG) category distribution is the functional classification of genes within a genome based on their evolutionary relationships and predicted biological roles. The difference in COG category distribution between Eg-DBG and Eg-DCM suggests potential genomic variations that may influence biological functions, particularly in DNA repair and genome maintenance. The higher proportion of genes associated with Replication, recombination, and repair in Eg-DBG may indicate plant resilience, as efficient DNA repair systems help maintain genomic integrity under biotic and abiotic stress 58 . Additionally, the similarity in gene distribution for Translation, ribosomal structure, and biogenesis , and Post-translational modification, protein turnover, and chaperones suggests that fundamental cellular processes are conserved between the two genomes, as these functions are essential for protein synthesis and stress response in plants 59 . On the other hand, the large proportion of genes categorized under Function unknown highlights the need for further functional characterization, particularly in non-model species like oil palm. R-genes are crucial to annotation in this genome sequencing effort, as they play a fundamental role in plant immunity and adaptation to biotic stress. The reduction in the number and proportion of R genes in Eg-D1 compared to Eg-DCM and Eg-DBG suggests a potential trade-off between genetic resistance and selective breeding for agronomic traits. The substantial decline in key R-gene classes, particularly CC-NBS (CN) and CC-NBS-LRR (CNL), which are commonly associated with pathogen recognition and immune signaling 60 , indicates that selection may have inadvertently reduced the diversity of resistance-related loci in the Eg-D1 cultivar. This pattern is consistent with previous studies showing that domestication and artificial selection can lead to the loss of genetic variation, especially in genes related to biotic stress responses 61 . The reduction in CK and NL classes further supports the hypothesis that selection pressure has influenced the retention of specific R-gene families. Domestication and artificial selection have profoundly influenced the genetic composition of oil palm, and their effects can be observed through codon usage patterns. Previous studies have demonstrated that domesticated plants exhibit distinct shifts in codon usage bias, often driven by selective pressures that optimize gene expression and translational efficiency 62 , 63 . The neutrality plot analysis in this study highlights a stronger correlation between GC3 and GC12 in Eg-D1, showing that mutational bias has a greater influence on this domesticated accession than its wild counterparts. Additionally, the relationship between GC3 and ENC reveals a trend toward codon optimization in Eg-D1, further supporting the role of selection in shaping codon usage. This pattern mirrors findings in domesticated rice, where selective breeding has led to significant shifts in codon usage preferences 63 . In conclusion, the whole-genome sequencing and comparative analysis of Eg-DBG and Eg-DCM give valuable insight into the genetic consequences of oil palm domestication. The observed differences in repetitive elements, gene content, functional classification, and R-genes distribution highlight the genomic shifts due to selection pressures. Notably, the reduction in R-gene diversity and the codon usage bias in Eg-D1 strongly suggest that domestication and artificial selection have influenced genetic variation, potentially impacting stress resistance and adaptation. These findings emphasize the importance of keeping genetic diversity in breeding programs to balance productivity and resilience. In particular, the characterization of R-genes from wild and ancestral accessions can support the development of Ganoderma -resistant oil palm varieties, as Ganoderma boninense remains a major constraint in Southeast Asian plantations 64 . Leveraging these genomic resources could enhance marker-assisted selection and accelerate breeding strategies aimed at mitigating basal stem rot. Further investigation is needed to comprehensively understand the genetic shifts that have shaped modern oil palm cultivars. Structural variation analysis through a pan-genome approach will give a more complete view of genome-wide diversity and its role in adaptation, disease resistance, and agronomic traits. By integrating multiple accessions, a pan-genome analysis could reveal presence/absence variations, copy number variations, and large-scale structural rearrangements not captured in single-genome studies. Such insights will be crucial for refining breeding strategies, optimizing desirable traits, and ensuring long-term genetic sustainability in oil palm cultivation. Future research should also focus on functional validation of key genomic elements and on using wild genetic resources to develop oil palm cultivars with enhanced disease resistance and environmental adaptability. Declarations Acknowledgments The Oil Palm Research Grant ( Grant Riset Sawit , No. GRS_20220227110350) from the Indonesian Palm Oil Fund Management Agency (BPDPKS) supported this research. We appreciate the management of Bogor Botanical Gardens and the National Research and Innovation Agency (BRIN) for giving access to the Eg-DBG sample. We also thank the Indonesian Oil Palm Research Institute (IOPRI) for giving the Eg-DCM samples. Furthermore, we acknowledge valuable support from the management team of PT SMART Tbk, whose contributions helped the successful completion of this study. Plant material permission All authors state proper permissions and/or licenses for collecting plant or seed specimens used in this study. Data availability The whole-genome sequence data of this study have been deposited at GenBank ( https://ncbi.nlm.nih.gov ) under BioProject Accession No. PRJNA1241826. References Xu W, Martin J, Li JJ, X., et al (2024) Unveiling the Secrets of Oil Palm Genetics: A Look into Omics Research. Int J Mol Sci 25:8625 Lieke S-D, Spiller A, Busch G (2024) Are consumers still barking up the wrong (palm) tree? Insights into perceptions towards palm oil-related labels and claims. 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Plant Dis 107:603–615 Additional Declarations The authors declare no competing interests. Supplementary Files DnarEgKRBsupptbl.docx Cite Share Download PDF Status: Published Journal Publication published 27 Nov, 2025 Read the published version in DNA Research → Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6543597","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":448800798,"identity":"097ef9df-3ba7-4747-9b91-edebc5a70099","order_by":0,"name":"Redi Aditama","email":"","orcid":"https://orcid.org/0000-0003-2216-8798","institution":"IPB University","correspondingAuthor":false,"prefix":"","firstName":"Redi","middleName":"","lastName":"Aditama","suffix":""},{"id":448805551,"identity":"c4863666-7981-4885-83f8-7aad171c7625","order_by":1,"name":"Heri Adriwan Siregar","email":"","orcid":"","institution":"IPB 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03:53:55","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6543597/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6543597/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1093/dnares/dsaf033","type":"published","date":"2025-11-28T00:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81632839,"identity":"8b512a4e-ce18-4fa9-8d32-c3be083a949a","added_by":"auto","created_at":"2025-04-29 11:51:19","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1465558,"visible":true,"origin":"","legend":"\u003cp\u003eBUSCO scores of Eukaryotes, Viridiplantae, and Embryophytes illustrate the assembly completeness of \u003cem\u003eElaeis guineensis Dura\u003c/em\u003e Bogor Botanical Garden (Eg-DBG) and \u003cem\u003eE. guineensis Dura\u003c/em\u003e Cameroon (Eg-DCM) genomes.\u003c/p\u003e","description":"","filename":"Figure1busco.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6543597/v1/b28e1043198c257c4d46ee2f.jpg"},{"id":81632307,"identity":"9e94f66f-7776-4e7b-8cc4-d04bb3ca4db5","added_by":"auto","created_at":"2025-04-29 11:43:20","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":15078209,"visible":true,"origin":"","legend":"\u003cp\u003eCircos plot depicting the structure, properties, and features of \u003cem\u003eElaeis guineensis Dura\u003c/em\u003e Bogor Botanical Garden (Eg-DBG) and\u003cem\u003e E. guineensis Dura\u003c/em\u003e Cameroon (Eg-DCM) assemblies. From outside to inside: 1) Ideogram of each chromosome colour-coded by chromosome number. 2) SSR was mapped to the genomes and colour-coded to correspond to the original linkage group. The same colour indicates that the SSR comes from the same linkage group. 3) Scatter plot indicating the location and size of gap sequences. 4) Bar chart of GC content of 1 Mb sliding windows. Blue indicates that the values are higher than the averages, while red indicates the opposite. 5) Line plot of gene density. The blue color is the annotation results, and the red color is the reference annotation. 6-9) Heatmaps indicating the density of SINE, LINE, Copia, Gypsy, and simple repeats, respectively. 10) Synteny plot indicating the location of duplicated regions among chromosomes.\u003c/p\u003e","description":"","filename":"Figure2circos.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6543597/v1/5bbb2159776100cae7f33949.jpg"},{"id":81632291,"identity":"112e2f63-dbd3-4d76-a978-48e6a14e1f02","added_by":"auto","created_at":"2025-04-29 11:43:19","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2219673,"visible":true,"origin":"","legend":"\u003cp\u003eComparative genome alignment and phylogenetic relationship of \u003cem\u003eElaeis guineensis Dura\u003c/em\u003e Bogor Botanical Garden (Eg-DBG) and \u003cem\u003eE. guineensis Dura \u003c/em\u003eCameroon (Eg-DCM) genome assemblies and previously assembled oil palm genome references of\u003cem\u003e E. guineensis Dura\u003c/em\u003e D1 (Eg-D1), \u003cem\u003eE. guineensis Pisifera\u003c/em\u003e EG5 (EG5), and \u003cem\u003eE. guineensis Pisifera\u003c/em\u003e EG11 (EG11).\u003c/p\u003e","description":"","filename":"Figure3synteny.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6543597/v1/c69b42328119186fb8c05425.jpg"},{"id":81632303,"identity":"30a9edb0-cb16-4575-b772-663c3d4d889a","added_by":"auto","created_at":"2025-04-29 11:43:19","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1316555,"visible":true,"origin":"","legend":"\u003cp\u003eKrona chart visualizing the repeat composition of the assembled genomes, \u003cem\u003eElaeis guineensis Dura\u003c/em\u003e Bogor Botanical Garden (Eg-DBG, left) and \u003cem\u003eE. guineensis Dura\u003c/em\u003e Cameroon (Eg-DCM, right).\u003c/p\u003e","description":"","filename":"Figure4krona.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6543597/v1/8b08f3f514944f195c7acecd.jpg"},{"id":81632294,"identity":"83de3362-5872-4e9b-9c21-dbe134729595","added_by":"auto","created_at":"2025-04-29 11:43:19","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":918140,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of Cluster of Orthologous Groups (COG) categories for the annotated genomes, Elaeis guineensis Dura Bogor Botanical Garden (Eg-DBG, red) and E. guineensis Dura Cameroon (Eg-DCM, blue).\u003c/p\u003e","description":"","filename":"Figure5barchart.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6543597/v1/89c258b60fd815fb1430d55a.jpg"},{"id":81633869,"identity":"a868b9f0-903c-44ea-a8ee-98c151a3fe7d","added_by":"auto","created_at":"2025-04-29 11:59:19","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2403368,"visible":true,"origin":"","legend":"\u003cp\u003eCodon usage patterns in three oil palm accessions, \u003cem\u003eElaeis guineensis Dura\u003c/em\u003e Bogor Botanical Garden (Eg-DBG), \u003cem\u003eE. guineensis Dura\u003c/em\u003e Cameroon (Eg-DCM), and \u003cem\u003eE. guineensis Dura\u003c/em\u003e D1 (Eg-D1), \u0026nbsp;illustrated through (a) a neutrality plot depicting the relationship between GC3 and GC12, (b) the correlation between GC3 and Effective Number of Codons (ENC), and (c) a biplot from correspondence analysis, representing the distribution of genes based on codon composition.\u003c/p\u003e","description":"","filename":"Figure6codon.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6543597/v1/3bf32c1948952463a1d736cd.jpg"},{"id":99173517,"identity":"28ce2be8-9b5d-4e8d-8615-b206569bb544","added_by":"auto","created_at":"2025-12-29 16:15:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":24441184,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6543597/v1/503d2530-3d02-498e-af8c-b1e593454ddc.pdf"},{"id":81632292,"identity":"4129296e-ed65-40cf-8238-0ed01f645083","added_by":"auto","created_at":"2025-04-29 11:43:19","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":32560,"visible":true,"origin":"","legend":"","description":"","filename":"DnarEgKRBsupptbl.docx","url":"https://assets-eu.researchsquare.com/files/rs-6543597/v1/223cd9733ac48f2db9e9a152.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eWhole-genome sequencing of wild and ancestral \u003cem\u003eDura \u003c/em\u003eprovides insight into genetic shifting during the domestication of oil palm (\u003cem\u003eElaeis guineensis \u003c/em\u003eJacq.)\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAfrican Oil palm (\u003cem\u003eElaeis guineensis\u003c/em\u003e Jacq.), also known as the \"king of oil crops\"\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, is an essential global vegetable oil producer, contributing 38.7% of vegetable oil demand\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. However, the oil palm plantations only cover 6.6% of the global oil cropland, making it the most efficient oil-producing crop\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Originating from Africa, oil palm has played a significant role in several Southeast Asian countries\u0026rsquo; economy\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Oil palm is the Arecaceae family member produces triglycerides in its fruit mesocarp. Oil palm is classified into \u003cem\u003eDura\u003c/em\u003e (D), \u003cem\u003ePisifera\u003c/em\u003e (P), and \u003cem\u003eTenera\u003c/em\u003e (T) types based on their fruit shell thickness. The \u003cem\u003eDura\u003c/em\u003e type has a thick shell (2\u0026ndash;8 mm), a low mesocarp percentage, and produces a little oil. The \u003cem\u003ePisifera\u003c/em\u003e type lacks the shell and thick mesocarp, but the fruit size is small and does not develop well. The \u003cem\u003eTenera\u003c/em\u003e type has a thin shell (0.5-4 mm) and a thicker mesocarp, resulting in a higher oil content\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe Dutch colonial government introduced the first oil palm in Southeast Asia in the mid-19th century. Four seedlings assumed to be the \u003cem\u003eDura\u003c/em\u003e type were transported from Amsterdam and Mauritius to Indonesia and planted at the Bogor Botanical Gardens in 1848\u003csup\u003e6\u003c/sup\u003e. The seeds produced by those palms were distributed to other areas, including the Deli Plantation Company in North Sumatra, for use as ornamental plants. The growing understanding of the commercial potential of oil palm led to the establishment of the first trial plantation in Deli in 1878, covering an area of 0.4 Ha, using seeds produced by those ornamental plants\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. The trial findings indicated higher fruit production than in its indigenous habitat in Africa, encouraging numerous investors to establish new commercial plantations. In the first trial, plants were subjected to mass selection to produce seeds for the newly established plantations, and as a result, the Deli \u003cem\u003eDura\u003c/em\u003e population emerged. Until now, Deli \u003cem\u003eDura\u003c/em\u003e remains a key genetic source for creating superior oil palm varieties in Indonesia and Malaysia\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn the current era of molecular biology, genetic knowledge on a molecular scale is essential for understanding the mechanisms underlying oil palm traits and aiding breeders in swiftly and precisely selecting planting materials. Whole-genome sequencing has been carried out on modern \u003cem\u003ePisifera\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e and \u003cem\u003eDura\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e palms, benefiting the breeding process through the implementation of genomic selection\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e and marker-assisted selection approaches\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. The whole genome sequence of Deli \u003cem\u003eDura's\u003c/em\u003e ancestor and wild-type \u003cem\u003eDura\u003c/em\u003e will be useful in understanding the genetic shift occurring in the oil palm selection process over the past century.\u003c/p\u003e \u003cp\u003eIn this study, we performed whole-genome sequencing on Eg-DBG (\u003cem\u003eDura\u003c/em\u003e Botanical Garden), the first descendant of the four oil palms first planted in Bogor Botanical Garden, which is genetically equivalent to the Deli \u003cem\u003eDura\u003c/em\u003e ancestor, and Eg-DCM (\u003cem\u003eDura\u003c/em\u003e Cameroon), a wild \u003cem\u003eDura\u003c/em\u003e palm originating from the Bamenda Region, Cameroon. We performed a genomic comparison of Eg-DBG, Eg-DCM, and an advanced breeding \u003cem\u003eDura\u003c/em\u003e (Eg-D1)\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e to investigate genetic shifts during oil palm domestication in Indonesia and Malaysia. To show evidence of genetic shifting, we compare several genomic elements, including gene content, functional classification, resistance genes (R-genes), and codon usage preferences, which may reveal the molecular impact of domestication and artificial selection on the oil palm genome. The findings of this study provide valuable genomic resources for oil palm breeding programs by offering insight into how domestication has shaped the genetic architecture of this important crop.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePlant material and DNA extraction\u003c/h2\u003e \u003cp\u003eThis study used Eg-DBG, the first descendant of the progenitor tree V.L.2, one of the first four oil palms cultivated in the Bogor Botanical Gardens. Genealogically, Eg-DBG is equivalent to the oil palm accession planted at the Deli Plantation Company in North Sumatra in the 1870s. This study also used Eg-DCM, a wild oil palm with seeds collected from a wild grove of Bamenda Region, Cameroon. Fresh leaves were collected for DNA extraction using CTAB buffer. Subsequently, initial quantity and purity were observed using NanoDrop\u0026trade; (Thermo Scientific) combined with DNA visualization using agarose gel electrophoresis. Accurate quantifications were performed using the Qubit dsDNA HS Assay Kit (Thermo Scientific). DNA quality checks were performed using the 4150 TapeStation (Agilent Technologies).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLibrary preparation and whole-genome sequencing\u003c/h3\u003e\n\u003cp\u003eTotal genomic DNA (gDNA) was used as the input for library preparations in both long- and short-read sequencing. For the long-read sequencing, gDNA was repaired using an end-repaired end prep enzyme mix, generating DNA with 5'-phosphorylated and 3'-dA-tailed ends. The repaired DNA was ligated with ONT-compatible adapters and quantified using a Qubit Fluorometer before being loaded onto the flow cell. Sequencing was performed using GridION (Oxford Nanopore Technologies) employing MinKNOW v23.07.12. Base calling was performed using Guppy v7.1.4 with high accuracy mode. For the short-read sequencing, gDNA was fragmented using an enzymatic method and ligated using an MGI-compatible adapter. Library preparations were conducted using the MGIEasy FS DNA Library Prep Set. Sequencing was conducted for 612 cycles (PE300) using the MGI DNBSEQ-G400.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDe novo\u003c/b\u003e \u003cb\u003eassemblies and contig polishing\u003c/b\u003e\u003c/p\u003e \u003cp\u003eLong-read quality control was performed using NanoPlot v1.33.0, and low-quality reads were trimmed using NanoFilt v2.8.0\u003csup\u003e16\u003c/sup\u003e. \u003cem\u003eDe novo\u003c/em\u003e assemblies were performed on the long reads employing Flye v2.9.3 with the default parameters\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Quality control, adapter removal, and low-quality short-read trimming were performed using fastp v0.23.4 using the default parameters\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Clean short reads were aligned to the assembled contigs using the BWA-MEM algorithm from BWA software v0.7.18\u003csup\u003e19\u003c/sup\u003e. Aligned reads were used to polish assembled contigs employing Pilon v1.24\u003csup\u003e20\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eProximity ligation and homology-based scaffolding\u003c/h3\u003e\n\u003cp\u003eProximity ligation scaffolding was conducted using the Hi-C protocol and performed by Dovetail Genomics (California, USA). In short, formaldehyde was used to fix the chromatin in the nucleus of the libraries before it was extracted. DpnII was used to digest the fixed chromatin, biotinylated nucleotides were added to the 5' overhangs, and the free blunt ends were ligated. The DNA was separated from the protein by reversing the crosslinks. Biotin that was not intrinsic to the ligated segments was eliminated by treating the purified DNA. After the DNA was sheared to a mean fragment size of about 350 bp, NEBNext Ultra enzymes and Illumina-compatible adapters were used to create sequencing libraries. The sequencing was performed using the Illumina HiSeq X platform to produce Hi-C reads. Juicer v1.6\u003csup\u003e21\u003c/sup\u003e was used to map Hi-C reads to the contigs, identify the Hi-C interactions, and build the contact map. Identification of genomic structure and the scaffolding process was performed using 3D-DNA\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAssembled scaffolds were corrected and reassembled using a homology approach employing RagTag v2.1.0\u003csup\u003e23\u003c/sup\u003e with the EG11 genome sequence\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e as a reference. Evaluation of the completeness of the genome from contigs to chromosome-level assembly was assessed using the benchmarking universal single-copy orthologs (BUSCO) v4.1.5\u003csup\u003e24\u003c/sup\u003e against OrthoDB v10 datasets of Eukaryota (n\u0026thinsp;=\u0026thinsp;255), Viridiplantae (n\u0026thinsp;=\u0026thinsp;425), and Embryophyta (n\u0026thinsp;=\u0026thinsp;1,614). Duplicated fragments in the genomes were analyzed through self-alignment of each assembly using NUCmer v3.1.0\u003csup\u003e25\u003c/sup\u003e. The GC profile for 1 Mb window interval was analyzed using BEDTools v2.31.1\u003csup\u003e26\u003c/sup\u003e. The SSR sequences from the two libraries were mapped to the assembled genomes to confirm the scaffolding result\u0026rsquo;s consistency using NCBI BLASTN v2.10.1\u0026thinsp;+\u0026thinsp;\u003csup\u003e27\u003c/sup\u003e. The assembled genome\u0026rsquo;s chromosome structure, properties, and features were visualized using Circos v0.69-8\u003csup\u003e28\u003c/sup\u003e. The synteny of the assembled and the reference genomes was analyzed employing ntSynt v1.0.2\u003csup\u003e29\u003c/sup\u003e and visualized using ntSynt-viz v1.0.0\u003csup\u003e30\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eRepeat annotation and gene prediction\u003c/h3\u003e\n\u003cp\u003eRepeat sequences in the assembled genomes were found using the combination of \u003cem\u003ede novo\u003c/em\u003e identification, known repeat searching against databases, and tandem repeat search. RepeatModeler v2.0.6\u003csup\u003e31\u003c/sup\u003e was used to build models of repeat sequences. The models built and the Repbase\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e were mapped to assembled genomes using RepeatMasker v4.1.5\u003csup\u003e33\u003c/sup\u003e. In addition, the full-length LTR retrotransposons were analyzed by LTR_Finder v1.07\u003csup\u003e34\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGene predictions were carried out utilizing the BRAKER pipeline v3.0.8\u003csup\u003e35\u0026ndash;37\u003c/sup\u003e, which involves two primary tools: GeneMark\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e and Augustus\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Eight RNA-seq datasets were used as transcript evidence to predict the gene structure (Supplementary Table\u0026nbsp;1). In addition, putative oil palm protein sequences from the NCBI RefSeq assembly (ID: GCF_000442705.1) were used as evidence. The gene prediction results were filtered using a combination of three approaches to improve accuracy and minimize redundancy. First, sixteen RNA-seq data from the sequence read archives (SRA) database (Supplementary Table\u0026nbsp;2) were mapped to the predicted transcripts using HISAT2 v2.21\u003csup\u003e40\u003c/sup\u003e with the default options, followed by transcript quantifications employing StringTie v3.0.0\u003csup\u003e41\u003c/sup\u003e. Transcripts with FPKM values less than one were filtered out. Second, publicly available annotations of oil palm were downloaded from NCBI RefSeq with ID GCF_000442705.1 and mapped to the assembled genomes employing Liftoff v1.6.3\u003csup\u003e42\u003c/sup\u003e. Mapped annotations were compared with predicted genes employing gffread v0.12.7\u003csup\u003e43\u003c/sup\u003e. Any predicted gene not intersected by the mapped annotation was filtered out. Third, protein sequences of the predicted genes were aligned to the reviewed Swiss-Prot protein database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://uniprot.org\u003c/span\u003e\u003cspan address=\"https://uniprot.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) employing NCBI BLASTP v2.10.1\u0026thinsp;+\u0026thinsp;\u003csup\u003e27\u003c/sup\u003e. Any predicted genes with a bit score less than 50 and an e-value greater than 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e were filtered out. Functional analysis of predicted genes was performed using eggNOG-mapper v2\u003csup\u003e44\u003c/sup\u003e with eggNOG DB v5.0.2\u003csup\u003e45\u003c/sup\u003e as a reference.\u003c/p\u003e\n\u003ch3\u003eAnnotation of resistance genes\u003c/h3\u003e\n\u003cp\u003eResistance genes in three oil palm \u003cem\u003eDura\u003c/em\u003e genomes (Eg-DBG, Eg-DCM, and Eg-D1) were annotated using the Disease Resistance Analysis and Gene Orthology (DRAGO 3) pipeline\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. LRR, Kinase, NBS, and TIR domains were detected from protein sequences using a 60 HMM database employing the HMMER v3 package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://hmmer.org\u003c/span\u003e\u003cspan address=\"https://hmmer.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). In addition, coiled-coil (CC) and transmembrane (TM) domains were detected using the COILS v2.2 and the TMHMM v2.0C.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCodon usage analysis\u003c/h2\u003e \u003cp\u003eCoding sequences (CDS) of annotated genes were extracted from assembled genomes using GFFRead v0.12.7\u003csup\u003e43\u003c/sup\u003e. Codon usage indices, including GC content, GC content at third position (GC3), GC content at first and second positions (GC12), effective number of codons (ENC), and correspondence analysis of codon usage were calculated using the CodonW v1.4.4 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://sourceforge.net/projects/codonw/\u003c/span\u003e\u003cspan address=\"https://sourceforge.net/projects/codonw/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Visualization of codon usage matrices was conducted using the ggplot2 R package\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eGenome assembly characteristics\u003c/h2\u003e \u003cp\u003eThe draft genome sequences of Eg-DBG and Eg-DCM were constructed in this study using the combination of short reads from the MGI DNBSEQ-G400 and long reads from the ONT GridION platforms. \u003cem\u003eDe novo\u003c/em\u003e assemblies were performed on the long reads, followed by short-read polishing, resulting in contigs totalling 1.715 Gb and 1.486 Gb for Eg-DBG and Eg-DCM genomes, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). \u003cem\u003eIn vivo\u003c/em\u003e proximity ligation data (Hi-C) were used to scaffold assembled contigs employing the pipeline involving Juicer and 3D-DNA. This process increased the N50 to 72.87 Mb for the Eg-DBG genome and 101.60 Mb for the Eg-DCM genome. The total genome length was increased for both genomes due to the addition of gap sequences in the scaffolding process. The assembly process then continued with an \u003cem\u003ein-silico\u003c/em\u003e homology-based scaffolding employing the RagTag. The assembled scaffolds were initially corrected and reassembled using the EG11 genome sequence as a reference. The last process increases the N50 of the Eg-DBG genome from 72.87 to 92.50 Mb.\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\u003eThe statistics of the \u003cem\u003eElaeis guineensis Dura\u003c/em\u003e Bogor botanical Garden (Eg\u003cem\u003e-\u003c/em\u003eDBG) and \u003cem\u003eE. guineensis Dura\u003c/em\u003e Cameroon (Eg\u003cem\u003e-\u003c/em\u003eDCM) genome assembly and scaffolding processes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssembly Level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInputs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSoftware\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLongest fragment (Mb)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo. of fragment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN50 (Mb)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGenome length (Mb)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEg-DBG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eContigs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eONT\u0026thinsp;+\u0026thinsp;MGI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFlye\u0026thinsp;+\u0026thinsp;Pilon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20,219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1,175\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScaffolds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContigs\u0026thinsp;+\u0026thinsp;Hi-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eJuicer\u0026thinsp;+\u0026thinsp;3D-DNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e121.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21,920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e72.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1,724\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChromosome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eScaffolds\u0026thinsp;+\u0026thinsp;EG11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRagTag\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e164.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22,655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e92.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1,725\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEg-DCM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eContigs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eONT\u0026thinsp;+\u0026thinsp;MGI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFlye\u0026thinsp;+\u0026thinsp;Pilon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16,128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1,486\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScaffolds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContigs\u0026thinsp;+\u0026thinsp;Hi-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eJuicer\u0026thinsp;+\u0026thinsp;3D-DNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e160.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8,436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e101.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1,492\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChromosome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eScaffolds\u0026thinsp;+\u0026thinsp;EG11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRagTag\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e155.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e96.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.492\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\u003eOn the other hand, the N50 of the Eg-DCM genome decreased from 101.60 to 96.77 Mb due to assembly correction. The quantitative assessment of genome assemblies' completeness was performed using the Benchmarking Universal Single-Copy Orthologs (BUSCO) v5.5.0\u003csup\u003e48\u003c/sup\u003e. The assessment showed that both assembled genomes had more than 99% completeness for the Eukaryote database and more than 97% for both Viridiplantae and Embryophyte databases (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA total of 591 simple sequence repeats (SSRs) from two previous studies\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e were mapped to ensure the consistency of genome assemblies. The distribution of SSRs in the assembled genomes is visualized in the second outermost circle of the Circos plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The mapped SSRs are colour-coded according to their original linkage group, where the same colour indicates that the SSRs originate from the same linkage group. The SSRs are distributed throughout all chromosomes, but certain regions exhibit higher accumulation. An interesting pattern in the SSR distribution is seen on chromosome 12, where a significant portion of SSRs shares the same linkage group as those on chromosome 14.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe GC content distribution was analyzed using a 1 Mb sliding window and represented in the bar chart layer on the Circos plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Blue bars indicate regions with GC content higher than the genome-wide average, while red bars represent regions with lower GC content. The analysis reveals a heterogeneous distribution of GC content across chromosomes, with some areas showing significantly higher or lower GC proportions.\u003c/p\u003e \u003cp\u003eThe duplicated regions along the genome were analyzed through whole-genome self-assembly employing NUCmer v3.1.0\u003csup\u003e25\u003c/sup\u003e. The Circos plot highlights duplicated regions within assembled genomes through the synteny plot in the innermost layer. Notably, several duplicated regions coincide with gene-rich areas, implying that retained gene duplications may be functionally important. Additionally, some duplicated segments overlap with repeat-rich areas, suggesting that transposable elements might have helped the duplication events.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eComparison to other assemblies\u003c/h2\u003e \u003cp\u003eSeveral publicly available oil palm genome sequences were downloaded, including EG5 (\u003cem\u003ePisifera\u003c/em\u003e)\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, EG11 (\u003cem\u003ePisifera\u003c/em\u003e)\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, and Eg-D1 (\u003cem\u003eDura\u003c/em\u003e)\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, to perform a full genomic comparison. Since Eg-D1 was a draft assembly result, we used RagTag v2.1.0\u003csup\u003e51\u003c/sup\u003e to perform homology-based scaffolding on the sequence using EG11 as a reference. The characteristics of these five assembled genomes were compared based on various sequencing and assembly metrics (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The size of assembled genomes of Eg-DBG (1.73 Gb) and Eg-DCM (1.49 Gb) was lower than the 1.8 Gb measured by flow cytometry\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. The lower number is due to DNA fragments in the genome that are not accessible to the sequencing methods used. In this metric, EG11 exhibits the most complete genome set with an assembled size of 1.84 Gb. The number and length of assembly gaps also influenced the size of assembled genomes. Gaps are introduced during the scaffolding process to keep the order and orientation of known fragments. The length of the ungapped sequence of EG11 is the highest (1,714,158,544 bp), but only slightly higher compared to Eg-DBG (1,712,233,623 bp). Compared to other assemblies, Eg-D1 has the least ungapped sequence, which is 553,149,332 bp.\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\u003eGenome characteristics comparison between \u003cem\u003eElaeis guineensis Dura\u003c/em\u003e Bogor Botanical Garden (Eg-DBG), \u003cem\u003eE. guineensis Dura\u003c/em\u003e Cameroon (Eg-DCM) and previously assembled oil palm reference \u003cem\u003eE. guineensis Dura\u003c/em\u003e D1 (Eg-D1), \u003cem\u003eE. guineensis Pisifera\u003c/em\u003e EG5 (EG5), and \u003cem\u003eE. guineensis Pisifera\u003c/em\u003e EG11 (EG11).\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=\"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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEg-DBG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEg-DCM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEg-D1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEG5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEG11\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenome size (bp)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,725,021,623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,492,598,792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,702,041,784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,535,178,229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,841,922,575\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal un-gapped length (bp)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,712,233,623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,486,247,692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e553,149,332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,057,103,131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,714,158,544\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of gaps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29,862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15,729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e337,489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e166,490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37,524\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN50 (bp)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92,499,198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96,770,559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74,098,475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,268,079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e128,314,321\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of chromosome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of unplaced scaffolds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22,639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8,289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8,797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40,045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMax. seq.\u0026nbsp;length (bp)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e164,565,033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e155,642,322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e156,671,551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68,435,666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e186,201,113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGC content\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSequencing coverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48x\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48x\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95x\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16x\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e60x\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\u003eN50 is a metric used to assess the contiguity of genome assembly. It stands for the length of the longest contig or scaffold where 50% of the total assembled genome length is contained. The N50 value of Eg-DBG (92,499,198 bp) and Eg-DCM (96,770,559 bp) were lower than that of EG11 (128,314,321 bp) but higher than that of EG5 (1,268,079 bp) and Eg-D1 (74,098,475 bp). The number of unplaced scaffolds influences the N50 value. The greater the number of unplaced scaffolds, the smaller the N50. EG11 has the smallest number of unplaced scaffolds, which is 13. Eg-DBG and Eg-DCM still have many unplaced scaffolds, 22,639 and 8,289, respectively. The GC content of Eg-DBG (38.7%) and Eg-DCM (38.3%) was consistent with that of EG11 (38.5%) and slightly higher compared to EG5 (37.2%) and Eg-D1 (37.1%).\u003c/p\u003e \u003cp\u003eTo investigate the structural variation and genomic synteny among different assemblies, we performed a whole-genome alignment analysis employing ntSynt v1.0.2\u003csup\u003e29\u003c/sup\u003e and visualization using ntSynt-viz v1.0.0\u003csup\u003e30\u003c/sup\u003e. The results, visualized in a ribbon plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), reveal a high degree of genomic conservation among the assemblies, with extensive syntenic regions seen across all 16 chromosomes. The phylogenetic tree illustrates the genetic relationship among the four accessions. Eg-D1 appears to be the most distinct accession, while Eg-DBG and EG11 share a closer evolutionary relationship. Despite the high synteny, minor structural variations can be seen in certain chromosomal regions, particularly in chromosomes 1, 6, and 14, where slight disruptions suggest potential structural rearrangements, such as inversions or segmental duplications.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eRepeat annotation and gene prediction\u003c/h2\u003e \u003cp\u003eRepeated elements in the Eg-DBG and Eg-DCM genomes were analyzed using a combination of RepeatModeler\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e and RepeatMasker\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, supplemented by the Repbase database. The results indicate that Retroelements are the dominant repeat class in both genomes, comprising 46.10% in Eg-DBG and 43.85% in Eg-DCM, with LTR elements being the most abundant subclass (Supplementary Table\u0026nbsp;3). DNA transposons account for a smaller proportion, around 2.71% in Eg-DBG and 2.83% in Eg-DCM. Interestingly, unclassified repeats also represent a substantial portion, suggesting the presence of novel or poorly characterized repeat sequences (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Additionally, simple repeats and low-complexity sequences contribute slightly more to Eg-DCM than Eg-DBG.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGene prediction was performed using the \u003cem\u003ede novo\u003c/em\u003e method employing the BRAKER2 pipeline v3.0.8\u003csup\u003e35\u0026ndash;37\u003c/sup\u003e. Publicly available oil palm transcriptomics data and protein sequences were used as evidence to support the prediction of the structure and splicing patterns of the genes. Initially, the prediction identified 61,256 genes in Eg-DBG and 53,985 genes in Eg-DCM (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Three filtering approaches were applied. The first was transcription-based filtering to refine these predictions and reduce potential false positives, which kept 24,456 genes in Eg-DBG and 23,779 genes in Eg-DCM. The second was reference-based filtering, which kept 25,650 genes in Eg-DBG and 21,504 genes in Eg-DCM. The third was protein filtering using the SwissPort database, which kept 38,361 genes in Eg-DBG and 35,454 genes in Eg-DCM. By integrating these filtering strategies, a final high-confidence gene set was obtained, consisting of 39,263 genes in Eg-DBG and 35,298 genes in Eg-DCM. On the other hand, the final mRNA counts from the annotations and filtering were 44,865 and 41,772 in Eg-DBG and Eg-DCM, respectively.\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\u003eGene and mRNA count after annotation and filtering in \u003cem\u003eElaeis guineensis Dura\u003c/em\u003e Bogor Botanical Garden (Eg-DBG) and \u003cem\u003eE. guineensis Dura\u003c/em\u003e Cameroon (Eg-DCM) genomes\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAnnotation Process\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eEg-DBG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eEg-DCM\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003emRNA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003emRNA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRAKER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61,256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67,303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53,985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e59,981\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFiltering:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTranscriptome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24,456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28,148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23,779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27,602\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25,650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30,744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21,504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30,014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSwissprot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38,361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33,401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35,454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30,435\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal filtered\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39,263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44,865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35,298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41,772\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 functional annotation of predicted genes was performed based on precomputed orthology assignments employing eggNOG-mapper and using eggNOG DB as a reference. The distribution of Cluster of Orthologous Groups (COG) categories reveals notable differences in gene distribution across categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Most annotated genes in both genomes fall under the function unknown category, showing many genes with uncharacterized functions. Among the well-defined categories, \u003cem\u003eReplication\u003c/em\u003e, \u003cem\u003erecombination\u003c/em\u003e, \u003cem\u003eand\u003c/em\u003e repair showed the most significant difference, with Eg-DBG having a markedly higher proportion of genes than Eg-DCM. Other functional categories, such as translation, ribosomal structure, and biogenesis, exhibited relatively similar distributions between the two genomes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Circos plot illustrates the distribution of genes and repetitive elements across Eg-DBG and Eg-DCM genomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The annotation results (blue line) and reference annotation (red line) reveal that genes are unevenly distributed along the chromosomes, with higher concentrations in euchromatic regions and lower densities in heterochromatic regions. Gene-rich regions often overlap with areas of higher GC content and show inverse correlations with Copia and Gypsy retrotransposon. On the other hand, a positive correlation between gene density and SINEs, LINEs, and simple repeats was observed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eResistance gene identification\u003c/h2\u003e \u003cp\u003eThe resistance genes (R-genes) were found in three oil palm \u003cem\u003eDura\u003c/em\u003e accessions employing the DRAGO 3 pipeline. The analysis revealed a noticeable reduction in the total number and proportion of R-genes in Eg-D1 compared to the other two genomes (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Eg-DCM, the wild \u003cem\u003eDura\u003c/em\u003e from Cameroon, contained 1,684 R-genes, representing 4.03% of its total predicted proteins, while Eg-DBG, the parental \u003cem\u003eDura\u003c/em\u003e used in Indonesia, had 1,760 R-genes, comprising 3.93% of its total proteins. In contrast, Eg-D1, the modern \u003cem\u003eDura\u003c/em\u003e cultivar subjected to multiple selection cycles, exhibited a significant decrease, with only 1,575 R-genes making up just 2.60% of its total protein-coding genes. A more detailed examination of R gene classes revealed that certain categories experienced a sharp decline in Eg-D1. Notably, the CC-NBS (CN) and CC-NBS-LRR (CNL) classes associated with disease resistance were reduced. Eg-D1 held only 2 CN genes and 60 CNL genes, compared to 50 and 105 in Eg-DCM and 61 and 114 in Eg-DBG, respectively. The CC-Kinase (CK) and NBS-LRR (NL) classes also decreased Eg-D1, suggesting that selection may have influenced the retention of specific R-gene families. Interestingly, the CC-NBS-TIR (CNT) class displayed an opposing trend, with Eg-D1 showing a notable increase (29 genes) compared to its near absence in the other genomes.\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\u003eComparison of R-gene counts by class in \u003cem\u003eElaeis guineensis Dura\u003c/em\u003e Bogor Botanical Garden (Eg-DBG), \u003cem\u003eE. guineensis Dura\u003c/em\u003e Cameroon (Eg-DCM) and previously assembled \u003cem\u003eE. guineensis Dura\u003c/em\u003e D1 (Eg-D1) genomes\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 \u003cp\u003eR-Gene Major Class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDomain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEg-DCM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEg-DBG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEg-D1\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCC\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\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCC, Kinase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCC, LRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCLECRK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCC, Kinase, LECM, TM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCLK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCC, LRR, Kinase\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\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCC, NBS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCNL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCC, NBS, LRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCNT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCC, NBS, TIR\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\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCC, TIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKIN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKinase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e491\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLECM, TM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLECRK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLECM, Kinase, TM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLYK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLYSM, Kinase, TM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLYP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLYSM, TM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLYS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLYSM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNBS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e126\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNBS, LRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRLK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRLK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e205\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRLP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRLP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e227\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTRAN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTransmembrane\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. of total R-genes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,575\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. of total proteins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41,772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44,865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60,619\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR-gene portions (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: CC: coiled-coil, LRR: leucine-rich repeat, TM: transmembrane, NBS: nucleotide binding site, TIR: Toll/interleukin-1 receptor, LYSM: lysin motif, RLK: receptor-like kinase, RLP: Receptor-like protein.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCodon usage analysis\u003c/h2\u003e \u003cp\u003eThe codon usage pattern of three \u003cem\u003eDura\u003c/em\u003e accessions was analyzed using CodonW v1.4.4 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://sourceforge.net/projects/codonw/\u003c/span\u003e\u003cspan address=\"https://sourceforge.net/projects/codonw/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and the results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The neutrality plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea) reveal that Eg-D1 exhibits a higher correlation between GC3 and GC12 than the other accessions, suggesting a greater influence of mutational pressure on codon usage. This pattern is often observed in domesticated plants, where reduced selective constraint can lead to a stronger reflection of underlying mutational biases. The relationship between GC3 and ENC (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb) follows a parabolic trend, showing the combined effects of mutational bias and selection, with Eg-D1 showing a tendency toward more optimized codon usage. Additionally, the correspondence analysis biplot (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec) highlights a distinct clustering pattern for Eg-D1, further supporting the idea that domestication has shaped its codon usage preferences. These findings suggest that artificial selection during domestication may have influenced codon usage in Eg-D1, leading to patterns that distinguish it from the wild or less domesticated accessions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussions","content":"\u003cp\u003eOil palm is the most efficient oil-producing crop and is crucial in meeting global vegetable oil demands\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Interestingly, most oil palms that exist today, particularly in Indonesia and Malaysia, originated from four parent trees planted in Bogor Botanical Gardens in 1848\u003csup\u003e6\u003c/sup\u003e. In this paper, we provide the results of high-quality whole-genome sequencing of Eg-DBG (\u003cem\u003eDura\u003c/em\u003e Botanical Gardens), the first derivative of an oil palm mother tree from the Bogor Botanical Gardens, and Eg-DCM (\u003cem\u003eDura\u003c/em\u003e Cameroon), a wild \u003cem\u003eDura\u003c/em\u003e palm obtained from a wild grove of Cameroon. The results of BUSCO analysis confirmed the quality of sequencing and assemblies, which showed high completeness for eukaryotes, Viridiplantae, and embryophyte taxa in both genomes. In addition, the total ungapped length of both assemblies shows a value close to 1.8 Gb, the measured size of the oil palm genome\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. The mapping of SRR sequences on the assembled genome validates the quality of the chromosome structure, as most SSRs mapped on a chromosome belong to the same linkage group. The quality of the chromosome structure was also validated by whole-genome comparison, which showed a great amount of parallel synteny between the assembled genomes and the oil palm reference genome, EG11\u003csup\u003e10\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe comprehensive analysis of repetitive elements in the assembled genomes highlights the significant role of Retroelements as the dominant class of repeats in oil palm genomes. The predominance of LTR elements within this category aligns with previous findings in plant genomes, where these elements contribute to genome expansion and structural variation\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Although present at lower proportions (2.71% in Eg-DBG and 2.83% in Eg-DCM), DNA transposons may still play a role in genome plasticity and adaptation\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Notably, the large fraction of unclassified repeats shows the presence of novel or poorly characterized sequences, which may reflect lineage-specific expansion events or limitations in current repeat databases.\u003c/p\u003e \u003cp\u003eThe gene prediction analysis in the assembled genomes highlights the impact of different filtering approaches in refining high-confidence gene sets. The first de novo prediction identified a large number of genes (61,256 in Eg-DBG and 53,985 in Eg-DCM), but subsequent filtering steps significantly reduced the number of putative false positives, ultimately resulting in 39,263 genes in Eg-DBG and 35,298 genes in Eg-DCM. This refinement aligns with previous studies that emphasize the importance of transcriptomic and protein evidence in improving gene annotation accuracy\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. The final mRNA counts, slightly higher than the total gene count, suggest alternative splicing events contributing to transcript diversity. The spatial distribution of genes across chromosomes, as revealed by the Circos plot, follows a typical pattern observed in plant genomes, where gene-rich regions are concentrated in euchromatic regions. In contrast, heterochromatic regions harbour fewer genes due to the dominance of repetitive sequences\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. The observed inverse correlation between gene density and Copia/Gypsy retrotransposons further supports the role of transposable elements in genome organization and gene silencing\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. Conversely, the positive correlation between gene-rich regions and SINEs, LINEs, and simple repeats may reflect their functional relevance in regulatory elements and genome stability\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe Cluster of Orthologous Groups (COG) category distribution is the functional classification of genes within a genome based on their evolutionary relationships and predicted biological roles. The difference in COG category distribution between Eg-DBG and Eg-DCM suggests potential genomic variations that may influence biological functions, particularly in DNA repair and genome maintenance. The higher proportion of genes associated with \u003cem\u003eReplication, recombination, and repair\u003c/em\u003e in Eg-DBG may indicate plant resilience, as efficient DNA repair systems help maintain genomic integrity under biotic and abiotic stress\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. Additionally, the similarity in gene distribution for \u003cem\u003eTranslation, ribosomal structure, and biogenesis\u003c/em\u003e, and \u003cem\u003ePost-translational modification, protein turnover, and chaperones\u003c/em\u003e suggests that fundamental cellular processes are conserved between the two genomes, as these functions are essential for protein synthesis and stress response in plants\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. On the other hand, the large proportion of genes categorized under \u003cem\u003eFunction unknown\u003c/em\u003e highlights the need for further functional characterization, particularly in non-model species like oil palm.\u003c/p\u003e \u003cp\u003eR-genes are crucial to annotation in this genome sequencing effort, as they play a fundamental role in plant immunity and adaptation to biotic stress. The reduction in the number and proportion of R genes in Eg-D1 compared to Eg-DCM and Eg-DBG suggests a potential trade-off between genetic resistance and selective breeding for agronomic traits. The substantial decline in key R-gene classes, particularly CC-NBS (CN) and CC-NBS-LRR (CNL), which are commonly associated with pathogen recognition and immune signaling\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e, indicates that selection may have inadvertently reduced the diversity of resistance-related loci in the Eg-D1 cultivar. This pattern is consistent with previous studies showing that domestication and artificial selection can lead to the loss of genetic variation, especially in genes related to biotic stress responses\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. The reduction in CK and NL classes further supports the hypothesis that selection pressure has influenced the retention of specific R-gene families.\u003c/p\u003e \u003cp\u003eDomestication and artificial selection have profoundly influenced the genetic composition of oil palm, and their effects can be observed through codon usage patterns. Previous studies have demonstrated that domesticated plants exhibit distinct shifts in codon usage bias, often driven by selective pressures that optimize gene expression and translational efficiency\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e,\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. The neutrality plot analysis in this study highlights a stronger correlation between GC3 and GC12 in Eg-D1, showing that mutational bias has a greater influence on this domesticated accession than its wild counterparts. Additionally, the relationship between GC3 and ENC reveals a trend toward codon optimization in Eg-D1, further supporting the role of selection in shaping codon usage. This pattern mirrors findings in domesticated rice, where selective breeding has led to significant shifts in codon usage preferences\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn conclusion, the whole-genome sequencing and comparative analysis of Eg-DBG and Eg-DCM give valuable insight into the genetic consequences of oil palm domestication. The observed differences in repetitive elements, gene content, functional classification, and R-genes distribution highlight the genomic shifts due to selection pressures. Notably, the reduction in R-gene diversity and the codon usage bias in Eg-D1 strongly suggest that domestication and artificial selection have influenced genetic variation, potentially impacting stress resistance and adaptation. These findings emphasize the importance of keeping genetic diversity in breeding programs to balance productivity and resilience. In particular, the characterization of R-genes from wild and ancestral accessions can support the development of \u003cem\u003eGanoderma\u003c/em\u003e-resistant oil palm varieties, as \u003cem\u003eGanoderma boninense\u003c/em\u003e remains a major constraint in Southeast Asian plantations\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. Leveraging these genomic resources could enhance marker-assisted selection and accelerate breeding strategies aimed at mitigating basal stem rot.\u003c/p\u003e \u003cp\u003eFurther investigation is needed to comprehensively understand the genetic shifts that have shaped modern oil palm cultivars. Structural variation analysis through a pan-genome approach will give a more complete view of genome-wide diversity and its role in adaptation, disease resistance, and agronomic traits. By integrating multiple accessions, a pan-genome analysis could reveal presence/absence variations, copy number variations, and large-scale structural rearrangements not captured in single-genome studies. Such insights will be crucial for refining breeding strategies, optimizing desirable traits, and ensuring long-term genetic sustainability in oil palm cultivation. Future research should also focus on functional validation of key genomic elements and on using wild genetic resources to develop oil palm cultivars with enhanced disease resistance and environmental adaptability.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThe Oil Palm Research Grant (\u003cem\u003eGrant Riset Sawit\u003c/em\u003e, No. GRS_20220227110350) from the Indonesian Palm Oil Fund Management Agency (BPDPKS) supported this research. We appreciate the management of Bogor Botanical Gardens and the National Research and Innovation Agency (BRIN) for giving access to the Eg-DBG sample. We also thank the Indonesian Oil Palm Research Institute (IOPRI) for giving the Eg-DCM samples. Furthermore, we acknowledge valuable support from the management team of PT SMART Tbk, whose contributions helped the successful completion of this study.\u003c/p\u003e \u003cp\u003e Plant material permission\u003c/p\u003e \u003cp\u003e All authors state proper permissions and/or licenses for collecting plant or seed specimens used in this study.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eThe whole-genome sequence data of this study have been deposited at GenBank (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ncbi.nlm.nih.gov\u003c/span\u003e\u003cspan address=\"https://ncbi.nlm.nih.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) under BioProject Accession No. PRJNA1241826.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eXu W, Martin J, Li JJ, X., et al (2024) Unveiling the Secrets of Oil Palm Genetics: A Look into Omics Research. 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Plant Dis 107:603\u0026ndash;615\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"Bogor Agricultural University","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Comparative genomics, Codon usage adaptation, Domestication-driven selection, Resistance gene evolution, Transposable elements dynamics","lastPublishedDoi":"10.21203/rs.3.rs-6543597/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6543597/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOil palm (\u003cem\u003eElaeis guineensis Jacq.\u003c/em\u003e) is a vital global crop, and its genetic improvement benefits from comprehensive genome sequencing. This study presents the whole-genome sequencing and annotation of two key genetic resources: the wild (Eg-DCM) and ancestral (Eg-DBG) \u003cem\u003eDura\u003c/em\u003e accessions. Comparative analysis with the advanced breeding \u003cem\u003eDura\u003c/em\u003e (Eg-D1) provides insights into genome structure, gene content, and evolutionary shifts during domestication. Retroelements dominated both genomes, constituting 46.10% in Eg-DBG and 43.85% in Eg-DCM. Gene prediction found 61,256 and 53,985 genes, refined into high-confidence sets of 39,263 (Eg-DBG) and 35,298 (Eg-DCM). A decline in resistance genes (R-genes) was observed in Eg-D1, suggesting that selection pressures influenced their retention. Codon usage analysis revealed significant shifts, showing increased mutational bias, with a stronger correlation between GC content at the third (GC3) and the first and second (GC12) codon positions in Eg-D1. Moreover, the relationship between GC3 and the effective number of codons (ENC) suggests stronger selection pressure driving codon optimization, a trend commonly observed in domesticated crops. These findings highlight how artificial selection has shaped genome composition, resistance mechanisms, and translational efficiency. The assembled genomes provide essential resources for understanding oil palm evolution and optimizing breeding strategies for enhanced productivity and sustainability.\u003c/p\u003e","manuscriptTitle":"Whole-genome sequencing of wild and ancestral Dura provides insight into genetic shifting during the domestication of oil palm (Elaeis guineensis Jacq.)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-29 11:43:15","doi":"10.21203/rs.3.rs-6543597/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f66d1b3c-d21a-40a7-b81a-d1be48915b15","owner":[],"postedDate":"April 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":47769468,"name":"Plant Molecular Biology and Genetics"}],"tags":[],"updatedAt":"2025-12-29T16:15:42+00:00","versionOfRecord":{"articleIdentity":"rs-6543597","link":"https://doi.org/10.1093/dnares/dsaf033","journal":{"identity":"dna-research","isVorOnly":true,"title":"DNA Research"},"publishedOn":"2025-11-28 00:00:00","publishedOnDateReadable":"November 28th, 2025"},"versionCreatedAt":"2025-04-29 11:43:15","video":"","vorDoi":"10.1093/dnares/dsaf033","vorDoiUrl":"https://doi.org/10.1093/dnares/dsaf033","workflowStages":[]},"version":"v1","identity":"rs-6543597","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6543597","identity":"rs-6543597","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}