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The development of ideal insertion and deletion (InDel) markers and initial indel map variation in cucumber using re-sequenced data

BMC Genomics volume 26, Article number: 391 (2025) Cite this article

Abstract

Background

InDels are the most common type of length polymorphism and play a critical role in the genetic traits of many important phenotypes in both plants and animals, making them an ideal source for length polymorphism molecular markers. However, in the process of cucumber breeding, researchers still face deficiencies in the identification of InDel loci and the development of genomic-wide molecular markers.

Results

In this study, we conducted InDel identification on 115 cucumber re-sequencing datasets, identifying a total of 7,842,946 InDels, with lengths ranging from 1 to 59 bp and an average density of one InDel every 2.8 kb on the chromosomes. The InDel variations were classified into four main categories, and 81 InDel hotspots were identified, serving as the foundation for constructing a cucumber InDel variation map. Additionally, we utilized an electronic PCR strategy to develop genome-wide InDel markers for cucumber, resulting in the selection of 22,442 InDel primers exhibiting high polymorphism (PIC ≥ 0.5) and major allele differences of ≥ 3 bp. We experimentally validated 50 randomly selected InDel primers, and the results showed that all markers exhibited high polymorphism.

Conclusions

The construction of the cucumber genome InDel variation map aids in understanding the genetic basis of key traits in cucumber derived from InDel variations. The ideal InDel markers developed in this study may enhance the efficiency of cucumber breeding for resistance to both biotic and abiotic stresses, as well as scientific research.

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Introduction

Cucumber (Cucumis sativus L., 2n = 2x = 14) is an annual herbaceous climbing plant and is considered one of the most economically significant vegetable crops worldwide [1]. Currently, China is the top cucumber-producing country, ranking first globally in both the scale of cultivation and yield. The cucumber is also an important model plant for research in vascular plant biology and sex determination and is recognized as a plant diversity center of significant biological value [2]. However, with the continuous improvement of yield and quality in cucumber breeding, the vegetable is beginning to encounter bottlenecks. In addition, in recent years, frequent extreme weather events, droughts, pests and diseases have caused losses to cucumber production worldwide, indicating a need to improve the stress resistance in many traditional varieties. Therefore, molecular marker technology is used to provide scientific means to address these issues and several markers have been applied in stress-related research. For instance, molecular markers such as amplified fragment length polymorphism [3,4,5], random amplified polymorphic DNA [6, 7], SSR [8, 9], single nucleotide polymorphism (SNP) [10], and InDels are widely used in genetic diversity analysis, gene mapping, and marker-assisted breeding (MAB) of cucumbers. The high polymorphic indel markers were also used to analyze the genetic diversity of cucumber inbred lines [11, 12], leading to fine mapping and gene cloning of important trait genes such as bitterness gene Bt [13] and powdery mildew resistance gene [14]. These laid a foundation for research on cucumber germplasm diversity and for further exploration of important quality genes. However, as research on fine mapping and molecular MAB intensifies, the availability of InDel markers is becoming a limiting factor.

InDels are collections of insertions and deletions of bases ranging from 1 to 50 bp and are the most widespread type of length polymorphism variations in genomes. In plants, InDels provide abundant genetic variation and influence gene expression as well as trait changes, and account for approximately 10–15% of genomic variation [15, 16]. For example, InDel variations can influence crop yield by regulating the number of branches or grain size [17,18,19]; they can also regulate the synthesis of anthocyanins, thereby changing the color of the fruit [20, 21]; additionally, they can enhance the cold and disease resistance of plants [22, 23]. Therefore, identifying InDel variations in plant genomes is crucial for understanding their impact on trait development. The rapid development of sequencing technologies and the reduced sequencing costs have promoted the production of large-scale resequencing data, resulting in the publication of several human, rice and maize re-sequenced genome data through which InDels can be sourced. About 20 vegetables and fruits have also been studied using intraspecific resequencing data. These data have led to the identification and characterization of 1.6 million InDels in the human genome [22], and millions and hundreds of thousands of InDels in maize [15] and rice [16], respectively. The rapid development of sequencing technologies and the reduced sequencing costs have promoted the production of large-scale resequencing data, resulting in the publication of several human, rice and maize re-sequenced genome data through which InDels can be sourced. About 20 vegetables and fruits have also been studied using intraspecific resequencing data. These data have led to the identification and characterization of 1.6 million InDels in the human genome [24], and millions and hundreds of thousands of InDels in maize [15] and rice [16], respectively.

The InDel variations provide the highest quality data source for developing high-density length polymorphism molecular markers, named InDel markers. Traditionally, simple sequence repeat (SSR) markers have been the most widely used type of length polymorphism marker in most laboratories due to their low cost, minimal equipment requirements, low technical demands, and high accuracy [25, 26]; however, in maize and rice, the number of SSR markers is significantly smaller than that of InDel markers [15, 16, 27, 28]. Therefore, the high-density character of the InDel markers makes them the most ideal length polymorphism markers. Despite this, there is an increasing demand for high-density InDel markers across different species. However, developing InDel markers from the InDel variations requires several conditions to be met, including distribution at a single locus within the genome, high polymorphism and conservation of flanking sequences, and the length difference suitable for electrophoresis resolution and the amplification length ratio. The large-scale development of InDel molecular markers is also constrained by the need for specialized software skills and computational resources.

Therefore, in this study, we called SNP and InDel variations using GATK in the resequencing data of 115 cucumber samples and used the data to construct a whole-genome InDel variation map for cucumber and analyze their composition and distribution patterns. We also used the previously developed electronic PCR (e-PCR)-based InDel marker development strategy [15, 16] to develop and validate a set of high-density InDel markers. Overall, the established InDel variation map facilitates the identification of polymorphisms that directly affect important cucumber agronomic characters and biological and abiotic stress. This set of developed ideal InDel markers can also enhance the efficiency of both fundamental and applied cucumber research in fine mapping and molecular-assisted breeding (MAB).

Materials and methods

Sequencing data and plant materials of the cucumber

The cucumber reference genome sequences and gene annotation (Cucumber_ v4, 321.53 Mb) of the inbred line 9930 derived from a Chinese long cultivar in China were downloaded from the website (http://www.cucumberdb.com/) [29]. The genome was annotated into five regions, including 5’-UTR, coding determining sequences (CDS), 3’-UTR, intron and intergenic regions. The 500 bp flanking sequences of the gene region were analyzed as TSS_UP_0.5Kb and TES_down_0.5 kb, respectively. A total 115 of cucumber re-sequence genome data were downloaded from NCBI (http://www.ncbi.nlm.nih.gov/sra/?term=SRA056480) [30].

A set of 39 cucumber lines was chosen for the polymorphism verification, including 19 materials of 115 the re-sequenced cucumber samples (Hw 2, JL-14 Dhillon, 10382, 9779, 13598 SC 53-B [6], 2163, SC 50, GY14, Marketmore76, 9930, CM8537-1-2-1-1-0-1-1-1, He Cha Huang Gua, Qian Qi Li Huang Gua, Jia Huang Gua, Sekino No. 2, Yuan Bai Huang Gua, Bai Pi Yuan Huang Gua, Ban Na Huang Gua 53 and Yuan Zong Huang Di Huang Gua),18 new cucumber lines (14#-27 S, 14#-7, 14#-21 S, 14#-23, 14#-67 S, 14#-9, 14#-13, 11#-X45S, 11#-63 S, 11#-X34, 11#-X83B, 11#-X81, 11#-X24, 11#-X55, 11#-X57S, 11#-X67S, 11#-X77S and DA40) and two samples wild cucumber (C. sativus var. hardwickii: 08CS986 and 08CS553) are provided by the Tianjin Kernel Cucumber Research Institute.

Read mapping and indel discovery using GATK

After filtering the reads of the 115 accessions with a cutoff quality score of 20 using the NGS QC toolkit (v2.3.3), the reads were mapped to the reference genome using Burrows-Wheeler Alignment (BWA) (v0.7.17) with the default parameters. The reads that were mapped to multiple loci in the genome were removed using Picard MarkDuplicates (http://picard.sourceforge.net/command-line-overview.shtml). The remaining reads were realigned around InDels using the RealignerTargetCreator and InDelRealigner of Genome Analysis Toolkit (GATK) tools (v4.0.4.0). The InDel variants were called and filtered using the GATK UnifiedGenotyper and VariantFiltration with the default parameters, respectively.

Statistical analysis of variation hotspots

The genome was divided into 100 kb bins, and the variation levels were calculated relative to the InDel and SNP numbers in each bin across the population. Mean and standard deviation analyses were conducted for InDel and SNP variation levels for each chromosome, and a one-sided Z-test was performed for each bin. To control for multiple-sampling errors, the resulting P-values were adjusted using false discovery rate (FDR) analysis [31, 32]. A conservative FDR cutoff of (Q-value) < 0.1 was applied, and the results of highly significant InDel and SNP hotspots are provided in Supplementary Table 2, along with corresponding P-values.

Assembled contigs

The reads with a quality score of 20 and above were selected for subsequent contig assembly using ABySS [33]. Afterwards, we chose a k-mer size of 45 for 90 bp read length samples and 25 for 44 bp read length samples for the contig assembly of re-sequenced samples. In our previous study [34], we utilized resequencing data from 115 samples with an average depth of 16–30 for contig assembly, resulting in a total of 221,985 to 608,332 overlapping groups. The total number of bases ranged from 56,713,982 to 386,317,213 bp, with an N50 of 70 to 17,416. The maximum overlapping group per sample ranged from 11,551 to 235,183 bp. Additional specific data can be found in Supplementary Table 3.

Highly polymorphic indel marker identified by an e-PCR strategy

We employed the InDel calling and polymorphism discovery pipeline previously developed for identifying polymorphic InDel markers in rice and maize [15, 16], which comprises four stages (the detailed procedure can be found in Supplementary File 1). First, using the Python script ‘step1.written_in_one_line_and_primer_design.py’, primers for electronic PCR were designed based on the cucumber reference genome 9930 (CLv4.0) as a template. The primers covered the entire genome, with a primer length of 20 bp and an amplicon length of 60 bp, with a sliding window of 20 bp, ensuring full coverage of the cucumber genome. Then, using the Python script ‘step2.primer_e-PCR.py’, the primer sequences were aligned to the cucumber Clv4.0 genome. The Python script ‘step3.select_single_matching_primers.py’ was then used to filter for unique primers, which have a unique position and sequence in the genome. Finally, these unique primers were used for electronic PCR with contig sequence data from 115 samples, ultimately identifying the InDels. The polymorphism of the primers with a unique genotype in a sample and 20 or more genotypes in the population was then assessed using polymorphism information content (PIC) using the formula.

$$\:\text{P}\text{I}\text{C}i=1-{{\sum\:}_{j=1}^{n}p}_{ij}^{2}$$

Where pij was the frequency of the jth pattern for the ith markers [35].

PCR validation of highly polymorphic indel marker

A 200 bp region, encompassing a 20 bp InDel region with 90 bp flanking sequences on each side, was used to design the primer using Primer5 software. The design parameters were applied: included a primer length of 18–25 nucleotides with an optimal length of 22 nucleotides, a Tm of 55°C to 64°C with 60°C as optimal, an amplicon product length of 60 to 200 bp, and the preference for a G- or C-rich perfect ending at the 3’-end [15, 16].

A set of 50 highly polymorphic InDel markers with a PIC value greater than or equal to 0.5 and major allele differences of at least 3 bp were randomly selected to validate the polymorphism in the DNA extracted from 39 cucumber lines. Selection thresholds were established at 3 bp and 8 bp: primers with allelic differences of ≥ 3 bp are employed to design PCR products approximately 60–100 bp in length, typically detected using polyacrylamide gel electrophoresis; primers with allelic differences of ≥ 8 bp are used to develop PCR products approximately 150–300 bp in length, distinguishable via agarose gel electrophoresis.

The 20 µL PCR reaction mixture comprising 50 ng DNA, 2.0 µL of 10 x buffer (containing Mg2+), 2.5 µL dNTP (2.5 mM), 100 nM of each primer, 1.5 U Taq polymerase, and ddH2O, was amplified as described previously. The amplicons were electrophoresed on a 6% polyacrylamide gel or 2% agarose gel, visualized through silver staining and used to calculate PIC.

GO analysis

The GO enrichment analysis of genes associated with highly polymorphic (PIC ≥ 0.5) InDel markers was conducted using the Singular Enrichment Analysis approach through the online AgriGO tool (http://bioinfo.cau.edu.cn/agriGO/) with the cucumber reference genome as the background [36]. Highly significant enriched terms were selected based on the default P-values and FDR.

Results

InDels variations in cucumber genome

A total of 7,842,946 non-redundant InDels were identified from 115 independent sets of reads. The InDels ranged from 1 to 59 bp with a mean of 2.59 bp, and are distributed throughout the seven chromosomes with an average density of one InDel per 24.39 kb. Four major InDel classes were identified, including single-base pair, monomeric base pair expansion, multi-base pair expansion of 2–15 bp repeat unit, and random DNA sequence InDels. The majority of the InDels (57.15%) were single-base pairs, followed by monomeric base pairs and multi-base repeat expansions of various lengths with 39.76% and random DNA sequence InDels with 3.09%. Among the single-base pair InDels, the AT and TA base pairs were the majority comprising 77.90% of these InDels (Table 1, S1).

Table 1 Classification of indels from 115 re-sequenced cucumber samples
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InDel and SNP hotspots

The number of InDels per 100 kb bin exhibited substantial variation across each chromosome, ranging from 172 to 10,300, with an average of 4,012.38. The number of SNPs per 100 kb ranged from 1,255 to 90,619, with an average of 29,115.09 (Fig. 1; Supplementary Table 2). Upon mapping these data to each bin, 81 InDel variation hotspots were identified across all chromosomes, including 38 InDel-only hotspots, with significant levels of variation. The number of InDels and SNPs within all bins on the chromosomes varied greatly, with a single bin sometimes containing 60 and 72 InDels and SNPs, respectively, more than the lowest counts. Additionally, nearly half of the bins have InDel and SNP counts exceeding the average level, with the top 10% of bins having average counts that are 4.68 and 6.49 times higher than the bottom 10%, respectively. This indicates that these regions represent hotspots of genetic variation.

Fig. 1
figure 1

The distributions for SNPs and InDels on cucumber chromosomes. “A” and “B”: the distributions for overall SNPs and InDels. The identified DNA variations were summarized as numbers in 1 Mb bins along each of seven chromosomes

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Mining ideal indel markers

Using the e-PCR-based InDel marker development strategy [6, 7], we designed 295,728,002 pairs of e-PCR primers with the cucumber reference genome DNA sequence as the template. Among the primers, 8,953,755 (3.03%) pairs were unique with an average density of 0.92 InDels/bp across the seven chromosomes and 18.71% (1,675,641) of them were polymorphic (Table 2, Supplementary Table 4). The PIC of the InDels in the cucumber population ranged from 0.01 to 0.95, averaging 0.10 (Fig. 2). The size difference of more than half of the InDel loci was within 3 bp (Fig. 3). The number of alleles generated for all InDels varied from 2 to 28, averaging 2.37, with the majority (80.09%) being bi-allelic mutations (Fig. 4). The highly polymorphic InDels with PIC ≥ 0.5 were 86,301. Of these, alleles ≥ 3 bp and ≥ 8 bp were 22,442 and 8,089, respectively (Supplementary Table 4). These InDels were easily genotyped using polyacrylamide and agarose gel electrophoresis. This detailed information on molecular markers, coupled with the convenience of Excel files, facilitates users in flexible marker selection, thereby improving efficiency in actual research applications.

Table 2 Distribution of e-PCR primer and polymorphic indels in different regions of the cucumber genome
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Fig. 2
figure 2

PIC value distribution of InDels in different genomic regions

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Fig. 3
figure 3

Size distribution of InDels in different genomic regions

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Fig. 4
figure 4

Size distribution of InDels in different genomic regions

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GO analysis

A total of 1,823 genes with highly polymorphic InDel loci (PIC value ≥ 0.5) were identified in coding regions. Of these, 1,272 genes were assigned to 472 GO terms, with 136 significant terms. Among the terms, 47% belonged to the biological processes, 43% consisted of molecular functions and 10% comprised cellular components. A total of 1,531 of the 1,823 genes with highly polymorphic InDels were found in promoter regions. Of these, 1,012 genes were linked to 474 GO terms, with 113 significant terms. Of these GO terms, 64% belonged to the biological processes, 39% were involved in molecular functions, and 41% comprised cellular components. There were 264 overlapping genes between the coding and promoter regions, involving 96 GO terms, involved in metabolic and cellular processes. Furthermore, differences in GO terms between these regions were observed in DNA metabolism, stress response, ion transport, transmembrane transport, and hydrolase activity (Fig. 5).

Fig. 5
figure 5

Summary of the Functional Analysis of GO Terms

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The experimental verification of indel markers

A total of 50 InDel markers with a PIC ≥ 0.12 (the minimum value in Supplementary Fig. 1) and a major allelic difference ≥ 3 bp were randomly selected for polymorphism verification in the DNA of 19 re-sequenced samples and 20 new lines, respectively (Fig. 6, Supplementary Fig. 1). The PIC of the InDel markers in 103 genome samples ranged from 0.12 to 0.88, averaging 0.53, while the number of allelic InDels ranged from 2 to 15, averaging 4.0. The PIC of the InDel markers in 19 re-sequenced DNA samples ranged from 0.12 to 0.64, with a mean of 0.47, while the number of allelic InDels ranged from 2 to 4, with a mean of 2.2. The PIC of the InDel markers in the DNA of 20 new lines ranged from 0.10 to 0.55, with a mean of 0.41, while the number of allelic InDels ranged from 2 to 3, with a mean of 2.0.

Fig. 6
figure 6

InDel polymorphisms experimental validation. Experimental validation of the InDel markers at position 9,831,160 on chromosome 1 and position 26,838,060 on chromosome 5 (PCR products from line 1 to line 20 originated from 19 cucumber inbred lines and one cucumber wild materials. Names and order are consistent with the previous description in Materials and Methods. M: marker DL2000)

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Discuss

InDels have received increasing attention in crops in recent years. However, the analysis of their variations is not as comprehensive as for SNPs. Their application as an ideal source for developing length polymorphism molecular markers has also been underutilized. In this study, we used cucumber as an example to construct a genome-wide InDel variation map, then utilized these variations to develop and validate ideal InDel markers. This study maybe enhance the efficiency of research on cucumber populations and also provide a reference for InDel variation analysis and marker development in other crops, especially economic crops such as vegetables and fruits.

InDel hotspots of cucumber genome

Variation is the molecular basis of genetic and functional diversity. Identifying regions with concentrated variations can help to identify genetic resources and improve the efficiency of cucumber breeding. For instance, the genetic diversity of highly variable regions can be utilized to enhance heterosis, while in low-variation regions it can be increased to overcome the bottleneck effect in breeding germplasm resources. In our study, the identification of InDel variation hotspot regions revealed that regions with rich genetic diversity in the cucumber genome are relatively few, accounting for about 4.21% of the genome, which is significantly lower than that in the maize and rice [15, 16]. These hotspot regions are likely concentrated areas of important functional genes. Significant InDel variation hotspots have also been found in the cucumber genome, including many important genes controlling key traits, such as CsY1 [37] and CsaARC5 [38] genes related to fruit peel colour, CsFT [39] and CsCRC [40] which control fruit quality, and dm [41] and FW2.2 [42] genes which control disease resistance. Though the number of InDel and SNP hotspots was similar, 38 InDel-only hotspot regions did not overlap with SNP hotspot regions. The discovery of these regions provides new insights into solving the problem of inaccurate genetic diversity evaluation caused by the limitations of markers. This also indicates that when evaluating genetic diversity, the molecular basis of variation sources should be fully considered. This is because a single type of molecular marker may not completely reflect the molecular basis of genetic diversity and could potentially lead to an incomplete evaluation of germplasm resources, ultimately affecting their utilization.

Development of ideal indel markers in cucumber

For a long time, molecular markers have been powerful tools for studying genetic variation and key trait genes in cucumbers. Therefore, developing ideal InDel molecular markers, characterized by high density, high polymorphism and ease of detection, is necessary and offers new tools for cucumber breeding and strategies for molecular marker development in other vegetables. In this experiment, we developed 22,442 InDel primers, greatly surpassing the 134 InDel primers developed in 2013 [43]. The density of InDel markers in this study is higher than that reported for tomato [44] and cowpea [45]. High polymorphism increases the likelihood of detecting genetic differences and reduces primer synthesis costs. In our study, 18.71% of the InDels were polymorphic with the highest polymorphism rate of 0.96%, which is lower than maize [15] and rice [16], likely due to cucumber’s simpler genetic background. The results indicate that it is necessary to use this method to identify highly polymorphic markers in species with low genetic diversity, such as cucumbers. Appropriate differences and ratios in amplification lengths can also improve the detection efficiency of agarose gel electrophoresis. In theory, under the condition of unchanged difference and gel concentration, the shorter the PCR amplification length, the higher the identification efficiency. Compared with SSR, InDels do not require primers to cross over repetitive sequences, so shorter PCR amplification lengths can be designed. Therefore, when designing primers for indel marker sites, we choose primers with the shortest amplification fragment under the premise of meeting the primer design conditions. The set of primers that were rigorously selected in this study are efficient length polymorphism primers.

The advantages of developing indel markers using contigs

Generally, in the development of InDel markers, sequence length affects the efficiency of InDel identification. Theoretically, the longer the fragment, the higher the probability of identifying larger InDel sites. For a given species, the number of available chromosome-level genomes is often very limited. Even if the number of available reference genomes increases, it is still far less than the available small fragment data. For example, although there are hundreds of rice reference genomes, there are still tens of thousands of available small fragment data. In this study, most re-sequencing data fragment lengths are 90 bp. Such smaller fragment sizes limit the identification of larger InDel sites. Therefore, using contig data can effectively compensate for the limitations of small fragment sequencing data in InDel site identification. Moreover, during the contig assembly process, sequencing errors can be minimized to the greatest extent, improving the accuracy and completeness of sequencing data.

Development of indel markers located within the gene in cucumber

The functional InDels in the genome can affect gene expression, thereby influencing biological traits and also play an important role in gene, biological breeding, and medical research. InDels located within the genome are more advantageous for genetic diversity assessment and marker-assisted selection (MAS). When assessing genetic effects, InDel markers derived within the gene are advantageous compared to markers derived near the gene. In MAS, ‘within-gene markers’ help prevent recombination that could prevent the separation of markers from target genes. Many key genes contain InDel variations, such as the Gn1a/OsCKX2 [46], GW2 [47], Chalk5 [48] and DEP1 [49] in rice, as well as Dwarf8 [50], qHO6/DGAT1-2 [51] and Yr36/WKS1 [52] in maize and wheat, respectively. In this study, nearly half of the InDel markers were shown to be distributed within the gene region, with 6,380 highly polymorphic InDels found in the promoter regions and 899 in the coding regions. Interestingly, in coding regions, we found that 43% of the InDels are 3n-bp in length. Therefore, special attention should be paid to non-multiple-of-3 InDel sites in the research, as they are more likely to result in functional differences. Some important shape or functional gene of cucumber are recorded in Table 3 [53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70].

Table 3 Important shape and functional gene in cucumber
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Conclusions

Here, we conducted a comprehensive identification of InDels within the cucumber genome and constructed the initial cucumber indel variation map. Based on this, we developed easy-to-use InDel markers that are distributed throughout the genome, with high polymorphism. We believe that these molecular markers will have high application value in the future, especially in disease resistance breeding and quality breeding and can provide simpler and more effective tools for studying genetic effects. Indel markers are the most densely distributed among length polymorphism markers; however, their overall density remains lower than that of SNP markers. Additionally, the efficiency of screening large-scale indel marker loci is relatively low. Future research should place greater emphasis on improving this aspect.

Data availability

The datasets underlying this article are available in National Center for Biotechnology Information (NCBI), at http://www.ncbi.nlm.nih.gov/sra/?term=SRA056480, and in Cucumber Multi-omics Database (Cucumber-DB), at http://www.cucumberdb.com.

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Acknowledgements

We highly appreciate Prof. Sanwen Huang’s help for providing us with DNA samples, and we also thank Professors Aimin Wei, Yike Han, and Xinghua Cui from the Tianjin Kernel Cucumber Research Institute for their guidance and technical support in the experiments.

Funding

This work was supported by National Key Research and Development Program of China (2022YFD1601501), Major Science and Technology Program in Sichuan Province (2023YFN0006), Natural Science Foundation Project of Sichuan Provincial (2022NSFSC0151), the Major Science and Technology Project of Sichuan Province (2022ZDZX0013), and National Key Research and Development Program of China (2023YFD1201100).

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  1. Jinchang Yang, Pengxu Meng and Hang Mi contributed equally to this work.

Authors and Affiliations

  1. Chengdu Academy of Agricultural and Forestry Sciences, No 200 Nongke Road, Wenjiang District, Chengdu, Sichuan, 611130, China

    Jinchang Yang, Jin Yang, Shaohong Fu, Wanzhuo Gong & Haoran Shi

  2. Maize Research Institute, Sichuan Agricultural University, No 211 Huiming Road, Wenjiang District, Chengdu, Sichuan, 611130, China

    Jinchang Yang, Pengxu Meng, Hang Mi, Xueying Wang, Ruifan Bao, Wujiao Deng, Haimei Wu, Zhiqin Liu, Sijia Yang, Jixing Ni & Jian Liu

  3. Chengdu Research Branch, National Rapeseed Genetic Improvement Center, No 200 Nongke Road, Wenjiang District, Chengdu, Sichuan, 611130, China

    Jin Yang, Shaohong Fu, Wanzhuo Gong & Haoran Shi

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  1. Jinchang Yang
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Contributions

JC Y, PX M, and H M completed the analysis and summary of the data, as well as the writing of the manuscript, and were major contributors in writing the manuscript. XY W, J Y, SH F, WZ G, and RF B were responsible for constructing the article’s framework and providing data during the calculation process. WJ D, HM W, ZQ L, and SJ Y improved the quality of the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Jian Liu or Haoran Shi.

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Yang, J., Meng, P., Mi, H. et al. The development of ideal insertion and deletion (InDel) markers and initial indel map variation in cucumber using re-sequenced data. BMC Genomics 26, 391 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12864-025-11584-z

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BMC Genomics

ISSN: 1471-2164

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