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Transcriptome characterization and high throughput SSRs and SNPs discovery in Cucurbita pepo (Cucurbitaceae)



Cucurbita pepo belongs to the Cucurbitaceae family. The "Zucchini" types rank among the highest-valued vegetables worldwide, and other C. pepo and related Cucurbita spp., are food staples and rich sources of fat and vitamins. A broad range of genomic tools are today available for other cucurbits that have become models for the study of different metabolic processes. However, these tools are still lacking in the Cucurbita genus, thus limiting gene discovery and the process of breeding.


We report the generation of a total of 512,751 C. pepo EST sequences, using 454 GS FLX Titanium technology. ESTs were obtained from normalized cDNA libraries (root, leaves, and flower tissue) prepared using two varieties with contrasting phenotypes for plant, flowering and fruit traits, representing the two C. pepo subspecies: subsp. pepo cv. Zucchini and subsp. ovifera cv Scallop. De novo assembling was performed to generate a collection of 49,610 Cucurbita unigenes (average length of 626 bp) that represent the first transcriptome of the species. Over 60% of the unigenes were functionally annotated and assigned to one or more Gene Ontology terms. The distributions of Cucurbita unigenes followed similar tendencies than that reported for Arabidopsis or melon, suggesting that the dataset may represent the whole Cucurbita transcriptome. About 34% unigenes were detected to have known orthologs of Arabidopsis or melon, including genes potentially involved in disease resistance, flowering and fruit quality. Furthermore, a set of 1,882 unigenes with SSR motifs and 9,043 high confidence SNPs between Zucchini and Scallop were identified, of which 3,538 SNPs met criteria for use with high throughput genotyping platforms, and 144 could be detected as CAPS. A set of markers were validated, being 80% of them polymorphic in a set of variable C. pepo and C. moschata accessions.


We present the first broad survey of gene sequences and allelic variation in C. pepo, where limited prior genomic information existed. The transcriptome provides an invaluable new tool for biological research. The developed molecular markers are the basis for future genetic linkage and quantitative trait loci analysis, and will be essential to speed up the process of breeding new and better adapted squash varieties.


The botanical family Cucurbitaceae, commonly known as cucurbits, includes several economically and nutritionally important vegetable crops cultivated worldwide, such as cucumber, melon, watermelon and pumpkins, gourds and squashes [1]. The cucurbit family displays a rich diversity of many traits, being primary models for sex expression analysis, for the study of vascular biology and for the analysis of the mechanisms involved in fruit ripening [25].

Despite the agricultural and biological importance of cucurbits, knowledge of their genetics and genome has been very limited till now. So far, genomic efforts have largely focused on cucumber and melon. Recently, the whole genome sequencing of the cucumber, C. sativus var. sativus L., has been completed by combining traditional Sanger and next-generation Illumina GA sequencing technologies [6]. Also an effort is in progress through a Spanish Initiative to obtain the whole genome sequence of melon, Cucumis melo L. [7]. Many genomic resources are available for both crops and also for watermelon, Citrullus lanatus (Thunb.) Matsum. & Nakai. BAC libraries, collections of genetic markers, detailed physical and genetic maps, mapping populations, microarrays, sequence databases and mutant collections [811] are facilitating the use of cucurbits by the research community. Many genomic resources are available at the web site of the International Cucurbit Genomics Initiative (ICuGI) [12].

Cucurbita genus (2n = 2 × = 40), that include squashes, gourds and pumpkins, has been less studied. It contains some of the earliest domesticated plant species [13]. Today, three of them, C. pepo L., C. moschata Duchesne, and C. maxima Duchesne, have considerable impact on human nutrition, being appreciated by their nutritional and medical properties [1417]. In addition to the use of the edible fruits, flowers, leaves, and vine tips are consumed, and seeds are also important as snacks, as a source of edible oil and protein for human and animal consumption, and in the pharmaceutical industry. Squashes are also popular as containers and for ornamental purposes. The economic value of Cucurbita spp. as rootstocks for overcoming soil borne diseases in cucurbits is significant [18].

C. pepo is the most economically important species within the genus distributed worldwide, and one of the most variable in the plant kingdom. Cultivated C. pepo is considered to comprise two subspecies each one including several cultivar-groups, ssp. pepo (Pumpkin, Vegetable Marrow, Cocozelle, and Zucchini) and ssp. ovifera (Acorn, Scallop, Crookneck, and Straightneck) [19, 20]. Its great economic value is based mainly on the culinary use of the immature fruits as vegetables, often referred to collectively as "summer squashes", but also the Pumpkin and Acorn groups display a major use as mature fruits, known as "winter squashes". The great diversity of uses makes breeding objectives quite variable.

The currently available genetic and genomic tools for Cucurbita are very limited. Until now three genetic maps have been constructed: two maps from inter-specific crosses between C. pepo and C. moschata[21, 22] and the third from an intra-specific cross of C. pepo (a USA oil-Pumpkin variety and an Italian Crookneck variety) [23]. These maps contained mostly RAPDs (Random Amplified Polymorphic DNA) and AFLPs (Amplified Fragment Length Polymorphism) markers. Only recently a collection of genomic microsatellites (Simple Sequence repeats, SSRs) has been developed and used to increase the map density [24]. The last map version comprises 178 SSRs, 244 AFLPs, 230 RAPDs, and two morphological traits (h (hull-less seed) and Bu (Bush growth habit). It contains 20 linkage groups with a map density of 2.9 cM and genome coverage of 86.8%. These SSRs were also used to study synteny between C. pepo and C. moschata[25].

The lack of denser genetic maps, larger high-throughput marker collections, and suited mapping populations is limiting gene isolation and squash breeding. Many C. pepo genes have been reported, mainly related to fruit quality and resistance to poty- and other viruses and several fungi, such as downy and powdery mildew [26], but only the transcripts of a few have been cloned and molecularly characterized in individual studies in C. pepo or other Cucurbita spp, for example genes involved in the biosynthesis or signaling pathways of growth regulators, affecting plant development, sex expression and response to stress [2732].

Single nucleotide polymorphisms (SNPs) are the most abundant variations in genomes and, therefore, constitute a powerful tool for mapping and marker-assisted breeding. These markers are replacing microsatellites in many model and non-model plants for saturating genetic maps [10, 33]. In genomes that have been poorly studied, sequence availability is the limiting factor for the discovery of SNPs.

Expressed sequenced tags (ESTs) represent a valuable sequence resource for research and breeding as they provide comprehensive information regarding the transcriptome. ESTs have played significant roles in accelerating gene discovery, allowing large-scale expression analysis, improving genome annotation, elucidating phylogenetic relationships and facilitating breeding programs for both plants and animals by providing SSRs and SNPs markers [6, 8, 11, 3437].

Currently, there are more than 66 million ESTs in the NCBI public collection [38]. However, less than 1,000 EST sequences are available for Cucurbita spp (C. maxima, C. moschata and C. pepo), and approximately 500,000 for all the species in the Cucurbitaceae family, most of them of cucumber and melon, included in the ICUGI Cucurbit Genomics Database [12], as compared to more than 1.5 and 2 million ESTs available for Arabidopsis and maize, respectively.

Recent advances in next-generation sequencing technologies allow us the large scale generation of ESTs efficiently and cost-effectively [39, 40]. There are increasing studies in which 454 technologies, combined or not with Solexa/Illumina, are used to characterize transcriptomes in cereals and legumes [4143]. Even in model species, such as Arabidopsis, this deep sequencing is allowing to identify new transcripts not present in previous ESTs collections [44]. Also specific transcriptomes are being generated in species for which previous genomic resources are lacking [4547]. The new transcripts are being used for microarrays design [48], and also for high throughput SSRs or SNPs identification. SNP detection is performed by aligning raw reads from different genotypes to a reference genome or transcriptome previously available, as in maize, cucumber and even in poliploid species such as Brassica napus[4951]. De novo assembly of raw sequences coming from a set of genotypes, followed by pairwise comparison of the overlapping assembled reads has also successfully used in species lacking any significant genomic or transcriptomic resources [52].

In this study, we describe the generation of 49,610 Cucurbita unigenes de novo assembled from about 500.000 ESTs obtained from roots, leaves and flowers of two contrasting C. pepo cultivars (Zucchini and Scallop, belonging to the two C. pepo subspecies) using Roche/454 GS FLX Titanium massive parallel pyrosequencing technology. These unigenes are functionally annotated and represent the first C. pepo transcriptome. They have been also screened for SSR motifs and used to identify a large SNPs collection suited for high-throughput mapping purposes. This sequence will allow accelerating genetics and breeding of this crop. It is also an important advance for cucurbit genomics as it is the first genomic resource for this genus, allowing comparisons among members of the three most economically important cucurbit genera, Cucumis, Citrullus and Cucurbita.

Results and Discussion

EST sequencing and assembly

We performed a half 454 GS FLX Titanium run on each of the two libraries constructed from leaves, flowers and roots from two C. pepo cultivars with contrasting plant, flower and fruit phenotypes, MU16 (C. pepo subsp. pepo cv Zucchini) and UPV196 (C. pepo subsp. pepo cv Scallop). A total of 407,723 and 392,370 raw sequence reads were obtained from each library (Table 1). Raw reads were processed using the Ngs_backbone software [53] to eliminate adapter sequences, low quality chromatograms and sequences of less than 100 base pairs (bp). This analysis gave rise to 261,962 and 250,789 processed sequences, comprising 164.6 Mbp of sequence, with an average length of 318.7 and 323.4 bp, respectively. The length distribution of these expressed sequence tags (ESTs) is shown in Figure 1. More than 85% ESTs fell between 200 and 500 bp in length.

Table 1 Sequence statistics of Cucurbita 454 ESTs
Figure 1

Length distribution of the Cucurbita ESTs. Data obtained after sequencing, with a half run of 454 GS FLX Titanium each one of the two Cucurbita cDNA libraries (Zucchini, Mu-16; Scallop, UPV-196), and processing the 454 raw reads, are presented.

The reads produced by the GS FLX Titanium platform were used for clustering and de novo assembly, independently of the genotype of origin. 459,439 ESTs were finally assembled using the Mira assembler [54] yielding a total of 49,610 high-confident tentative consensus sequences (non-redundant sequences or unigenes). The distribution of the number of ESTs per unigene is shown in Figure 2. The majority of unigenes were assembled from a moderate number of ESTs (from 2 to 10), with an average of 9.3 ESTs per unigene. Of all unigenes, about a 10% contained more than 20 reads, and only 2.4% more than 50, which represented the most abundant transcripts in these cDNA libraries. This low redundancy is probably due to the success of the normalization process, responsible for the suppression of superabundant transcripts. Normalization precludes in silico analysis of gene expression, but greatly increases the number of unigenes that can be determined by reducing redundancy.

Figure 2

Distribution of number of ESTs in each Cucurbita unigene.

The assembled unigenes had an average length of 626 bp, comprising approximately 31 Mbp in total. The length distribution of the unigenes is shown in Figure 3. The analysis revealed that more of the 50% of unigenes were larger than 537 bp, and only a 5% of the sequences were shorter than 290 bp. The number of assembled unigenes is similar to that obtained in previous transcriptome analyses in maize, Eucaliptus, Artemisia, chesnut, olive and cucumber. However, the de novo assembly with the longer reads obtained with the GS FLX Titanium platform render unigenes that average almost two times longer than that reported in these studies performed using 454 GS-20 and GS-FLX platforms [4547, 51, 52, 55]. Our assembled unigenes were also larger than that reported for American ginseng and Glycyrrhiza uralensis transcriptomes obtained using also the 454 GS FLX Titanium platform [56, 57]. Cucurbita unigenes length is comparable to that reported in melon transcriptome using the conventional (Sanger) dideoxy-based sequencing [8]. These differences in length might be due to the different assemblers used. The longer unigenes present the advantage of being more accurately annotated. The raw data files are available in the Sequence Read Archive at the National Center for Biotechnology Information (NCBI) [58], accession number SRA029105.1. The sequences of the unigenes in fasta format are available in the additional file 1:'Cucurbita unigenes', with unigene numbers from CUTC000001 to CUTC049610.

Figure 3

Length distribution of the Cucurbita unigenes de novo assembled from 454 ESTs.

Structural and functional annotation

Most unigenes, 47,626 (96%) were predicted to have one ORF. By aligning the unigenes with the melon genomic sequence (available for the partners of the MELONOMICS project [7]), introns were identified in 16,697 unigenes (33.7%). Annotation results regarding ORFs and introns position are included in additional file 2: 'Annotation of ORFs and Introns'.

Codon usage was estimated using a subset of the unigenes predicted to contain full-length ORFs, with start and stop codons and without frame-shift errors. All codons were found in the dataset, with the least frequent codon represented 590 times (data not shown). As expected, the codon usage of Cucurbita shared some similarities with that of melon, Arabidopsis and other dicots. For example, T is the preferred base in the third codon position for most amino acids except for glycine, phenylalanine, serine and arginine. The preferred stop codons were UAA and UGA that occurred in the 41.4 and 41.1% of the sequences, respectively. Suppression of the CG dinucleotide in the last two codon positions is very frequent in dicots, possibly as a consequence of methylation of C in the CG dinucleotide, resulting in an increased mutation rate; the ratio XCG/XCC for Cucurbita was 0.69, then the suppression was more intense than in Arabidopsis (0.92), but milder than that reported for melon (0.52) tomato (0.58), pea (0.51), potato (0.48) or Populus (0.38). The GC content in third base position was similar in Cucurbita as compared to melon and Arabidopsis (46% vs 39,9% and 42%) [8, 35].

In order to identify C. pepo unigenes potentially encoding proteins with known function, a BLAST analysis [59] was performed in a sequential way using Swiss-Prot [60], Arabidopsis proteins [61], and Uniref90 [62] protein databases. Over 63% of the unigenes (31,159) had at least one significant hit (E-value cutoff = 1e-20). Most were annotated with the accurate databases Swiss-Prot (55%) and Arabidopsis (36%) and less with Uniref (9%). The majority of the unigenes have significant hits with Arabidopsis proteins (67%). Hits with Cucumis and Cucurbita previously reported proteins were also found.

Gene Ontology provides a structured and controlled vocabulary to describe gene products according to three categories: molecular function, biological process and cellular component [63, 64]. We added GO terms using Blast2GO [65], based on the automated annotation of each unigene using BLAST results against the GenBank non redundant protein database (nr) from NCBI [66]. A total of 29,676 unigenes (60%) could be assigned to one or more ontologies. Figure 4A show the unigenes distribution regarding the number of GOs to which they were assigned. The number of GO terms per unigene varied from 1 to 34. More than the 78% of the unigenes could be assigned to more than one GO term, being the majority of the unigenes mapped to 2 to 7 GO terms. In total, 103,045 GO terms were retrieved, 25%, 47% and 28% in the biological process, in the molecular function and in the cellular component category, respectively. The distribution of annotated unigenes under different GO levels of each category (Figure 4B) shows a concentration in 4-7, 3-7 and 4-7 levels respectively for biological process, molecular function, and cellular component, indicating a good accuracy of annotation. The GO annotation analysis reinforces the idea that a broad diversity of genes was sampled in our multi-tissue normalized cDNA libraries.

Figure 4

Number of GO terms (A) and GO level distribution (B) in the annotated Cucurbita unigenes. A. Distribution of GO terms in the annotated Cucurbita unigenes. B. GO level distribution in each category for the annotated Cucurbita unigenes.

We used the GO annotations to assign each unigene to a set of GO Slims of the biological process and molecular function categories, which are a list of high-level GO terms providing a broad overview of the ontology content. A summary with the number of unigenes annotated in each GO slim term is shown in Figure 5. GO annotations for the unigenes showed fairly consistent sampling of functional classes. Cellular process, metabolic process, and biosynthetic process were among the most highly represented groups under the Biological Process category. This might be indicating the analyzed tissues were undergoing rapid growth and extensive metabolic activities. Genes involved in other important biological processes such as reproduction, stress and stimulus response, signaling, and developmental processes were also identified (Figure 5A). Under the molecular function category, assignments were mainly to the catalitic and binding activities. A large number of hydrolases, kinases and transferases were annotated which suggests that this study may allow for the identification of genes involved in the secondary metabolite synthesis pathways. Also, transcription and translation factors were well represented (Figure 5B). The distribution of Cucurbita unigenes follow similar tendencies to that reported for Arabidopsis and also for the melon transcriptome [8, 52, 61], suggesting that the Cucurbita dataset could be representative of the whole squash transcriptome. All annotation results, regarding BLAST hits and GO annotations for the whole Cucurbita unigene collection are compiled in the additional file 3: 'Blast hits and GO terms'.

Figure 5

Number of Cucurbita unigenes in each functional category. Cucurbita unigenes were classified into different functional groups based on a set of GO slims in the A) Biological Process category and B) Molecular Function category.

Doing a reciprocal blast search, we have also identified 19,312 Cucurbita unigenes (38.9%) with an ortholog in Arabidopsis[61] (11,022 (22.2%)) and a melon ortholog (12,461 (25.1%)) of the ICUGI data base [12] (Table 2). A list of the identified orthologs is included in additional file 4: 'Arabidopsis and melon orthologs'.

Table 2 Functional annotation statistics

Only 11,580 (23%) of the unigenes did not show significant similarity to any protein in the databases and could not be annotated. Shorter sequences are less likely to align with a significant E-value. However, the average length of these non annotated unigenes was 425 bp, with 50% being longer than 413 bp. For homology searches against known genes, unigenes longer than 200 bp are widely accepted for the effective assignment of functional annotations [57]. In previous studies performed with massive sequencing techniques a similar or even higher number of unigenes did not match with previously known sequences representing newly detected transcripts [44, 49, 55, 57].

Genes potentially encoding proteins involved in pathogen resistance, flowering, fruit quality and root traits

Viral and fungal pathogens affect severely the productivity of cucurbits crops. The Cucurbita unigene collection contains genes potentially involved in disease resistance and disease response [6769] (see additional file 5: ' Genes potentially encoding pathogen resistance, flowering, fruit and root traits'). We have found at least one ortholog to TOM1, TOM2A (Tobamovirus multiplication 1 and 2A) and THH1 (Tom Three Homolog 1), genes encoding proteins that play essential roles on the tobamovirus replication [70], and also orthologs to the eukaryotic translation factors of the EIF4 family (EIF4E, EIF(ISO)4E and EIF4EG), known to mediate recessive resistance to poty- and other RNA viruses [7173]. Regarding fungal responses, we have found orthologs to the Arabidopsis RPH1 gene (resistance to Phytophthora 1), involved in immune response to Phytophthora brassicae[74], and other genes required for non-race specific resistance to bacterial and fungal pathogens [75].The powdery mildew is the main fungal disease affecting Cucurbita cultivation [15]. An ortholog to Arabidopsis PMR5 (powdery mildew resistant 5) and to the MLO10 gene, belonging to the family of Arabidopsis homologs of the barley mildew resistance locus mlo, have been identified [76, 77].

Cucurbits are models for sex determination studies due to its diverse floral sex types. Significant progress has been made in elucidating the mechanisms of plant sex determination by cloning several major sex-determining genes in cucurbit species [2, 78, 79]. Despite such advances, the whole mechanisms of sex determination are still unknown. Both Cucurbita sequenced genotypes are monoecious, but have large differences in the flowering time and the femaleness tendency. The Cucurbita unigene collection includes orthologs of Arabidopsis genes involved in flower development and flowering-time regulation (see additional file 5) [8082]. Sex expression in cucurbits can be regulated by plant hormones and environmental factors. Ethylene is highly correlated with the femaleness and has been regarded as the primary sex determination factor [6]. Some genes related with the ethylene synthesis and also transcription factors and receptors involved in the ethylene perception and signaling have been found (various EIN (Ethylene insensitive) and ETR (Ethylene response) genes). It has been reported that auxin and also brassinosteroids can induce pistillate flower formation in part through its stimulation of ethylene production. The Cucurbita unigene collection has also different orthologs of proteins involved in auxin and brassinosteroids signaling, affecting flowering-time in Arabidopsis (SAR, suppressor of auxin resistance, BZR, Brassinazole resistant) [8385]. Cucumber orthologs involved in those mechanisms have been reported to have differential expression in Gynoecious/hermaphroditic flowers in a recent study performed with massive sequencing [51]

We also identified a number of genes putatively involved in gibberellins (GA) biosynthetic and signaling pathways. These specific genes might be associated with the role of gibberellic acid in developmental regulation and plant stress response. The cucurbits represent a model plant system on which to examine the contents of the phloem translocation stream. A recent study reported a large-scale analysis of proteins from pumpkin (C. maxima) phloem exudates [5]. Identified proteins mainly corresponded to enzymes that carry out pivotal roles in stress and defense pathways. Furthermore, the detection of proteins related to GA synthesis in phloem supports the theory that the phloem is the route for transport and modification of GAs. Some orthologs of the genes encoding the main phloem sap proteins are included in our C. pepo collection.

Fruit development and ripening are the most important processes determining the fruit quality. At present most of the molecular and genetic data available about fruit development and ripening come from Arabidopsis and tomato [86]. In recent years, several genes and quantitative trait loci controlling fruit quality traits have been described in cucurbits [87, 88]. As for the previously described processes, orthologs to genes involved in fruit development and quality have been found in the Cucurbita dataset (see additional file 5) [89, 90]. These include several cell wall-metabolism enzymes and genes involved in the isoprenoid biosynthetic pathway (provides intermediates for the synthesis of sterols, carotenoids and chlorophylls, and also phytohormones and terpenes involved in plant defense). Cucurbita species are important sources of vitamins in many developing countries due to their high carotenoids content [91]. The Cucurbita unigene collection includes some enzymes involved in carotenoids biosynthesis (PSY, Phytoene synthase, PDS, Phytoene desaturase and ZDS, Zeta-carotene desaturase) [92]. The root tissue also provided some root specific genes involved in root development or stress tolerance [93].

SSRs and SNPs discovery and validation

We performed a general screen on the Cucurbita unigene dataset for the presence of microsatellites, analyzing its nature and frequency [94]. A search for di-, tri-, and tetra-nucleotide repeats yielded a total of 1,935 potential SSRs in 1,822 unigenes, that is approximately 4% of the unigenes contained at least one of the considered SSR motifs. This percentage agrees with previous studies using EST databases that shows that approximately 3-7% of expressed sequences contain putative SSR motifs [34, 51].

The maximum and minimum lengths of the repeats were 129 and 17, and the average length was 24 nucleotides. These were mostly tri-nucleotide (71.7%), and less di- (15.3%) and tetra- (13%). The most common repeat motifs are indicated in Table 3. A similar bias towards AG, AAG and AAAG and against CG repeats has been reported in EST-SSRs of many crops, including other cucurbits like melon and cucumber. It has been proposed that this may be due to the tendency of CpG sequences to be methylated which potentially might inhibit transcription [8, 51]. Genomic SSRs identified in C. pepo and C. moschata also showed the same predominant di- and tri-nucleotide motifs [24]. The complete list of SSRs and their corresponding information are provided in additional file 6: 'Cucurbita SSRs'.

Table 3 Simple Sequence repeats (SSRs) statistics

Most SSRs (55%) were located in ORFs, being a similar number in the 5' and the 3' untranslated regions (UTRs) (Table 4). An analysis of the localization of di-, tri- and tetra-repeats showed that tri-nucleotides localized preferentially in ORFs, consistently with maintenance of the ORFs coding capacity, whereas di- and tetra-nucleotides were more frequent in UTRs. It is known that the UTRs are richer in SSRs than coding regions, particularly the 5'-UTRs [34, 95]. Thus, the prevalence of tri-nucleotide repeats in the Cucurbita dataset may account for our high proportion of ORF-SSRs. These results agree with those reported in melon, where the most frequent SSRs in ORFs were tri-nucleotide [8].

Table 4 Localization of SSRS with respect to putative initiation and termination codons in the Cucurbita unigene dataset

We selected a set of 30 ESTs-SSRs for validation, 26 (86.7%) amplified polymorphic fragments in a set of 10 genotypes of Cucurbita, 9 representative of the diversity within C. pepo (accessions of 3 morphotypes of spp. pepo, including several landraces and commercial types of the zucchini type, and two morphotypes of spp. ovifera) and 1 C. moschata accession. All but one could be transferred to the related crop C. moschata, producing alleles unique of this species in most cases (60%). On an average we found 3.2 alleles per primer pair in C. pepo and C. moschata. Most of EST-SSRs assayed are useful to detect variability within and between the two subspecies of C. pepo. A 77% were polymorphic between the two genotypes used for sequencing, and 50% and 88% detected variation within spp. ovifera and within spp. pepo, respectively. Also 62% detected variation among the landraces and commercial lines of zucchini. Details of these validated SSRs are included in the additional file 7: 'Validated Cucurbita SSRs'.

SSR markers derived from EST sequences have been extensively used in constructing genetic maps of cucurbit species [9, 96]. Until recently, only a few microsatellites have been available for Cucurbita, and transferability from other cucurbits, such as cucumber of melon, has been demonstrated to be very low [9], then the development of SSRs for this genus is highly desirable. Gong et al. [24] developed SSRs-enriched partial genomic libraries from an Austrian oil-pumpkin variety C. pepo subsp. pepo and one accession of C. moschata, generating a collection of 1,058 putative SSRs. They reported a 81% validation in a set of genotypes representing the cultivar groups, also indicating a high intra-genus transferability. EST-SSRs have several advantages versus genomic; they are related to genes, being functional markers that can be used as candidate genes to study their association with phenotypic variation and the flanking sequences are more likely to be conserved among close or distant species, making their use as markers for comparative mapping easier. We will use the identified EST-SSRs markers for the construction of a genetic map, using a Recombinant Inbreed population (RILs) derived from the Zucchini (MU16) × Scallop (UPV196) cross. They will be also useful for fingerprinting commercial Zucchini cultivars, breeding lines and landraces and for genetic diversity studies within the genus, mainly performed with RAPDs or AFLPs to date [20, 97].

Massive sequencing of ESTs in a number of diverse genotypes has been previously used for developing large SNPs collections [4952]. Since the ESTs generated under the present study, using the 454 technology, are from two different cultivars belonging to two subspecies, with MU16 and UPV96 representing the 51% and 49% of the EST sequences, respectively, we expected SNPs to be frequent in our collection. The SNP calling was done with the default parameters recommended by the ngs_backbone software [53]. Stringent quality criteria were used for distinguish sequence variations from sequencing errors and mutations introduced during the cDNA synthesis step. Only variations with allele and mapping quality over the established thresholds were annotated. By applying these criteria, we initially identified a total of 19,980 SNPs and 1,174 INDELs distributed in 8,147 unigenes (16.4%), averaging a total of 2.6 single variations per unigene. Different filters were applied to facilitate the management of the variants. INDELs can be filtered out with VKS (It is not an SNP). The detailed information about the identified SNPs and INDELs is included in the additional file 8:'Cucurbita SNPs and INDELs'.

Within the detected SNPs, transitions (68%) were much more common than transversions (32%) (Table 5). A similar number of A/G and C/T transitions and also similar percentages of the four transversion types (A/T, A/C, G/T, C/G) were found. A set of SNPs could be accurately located with respect to putative initiation and termination codons, being mostly located in ORFs (82%).

Table 5 Single nucleotide polymorphism (SNPs) statistics

Other filters allowed an accurate in silico selection of the SNPs to identify the ones more suited for mapping purposes. All located in sequences with more than 4 SNPs or INDELs per 100 bases (filtered out with HVR4) were discarded (71, 0.36%) and also those that were variable within one or both genotypes, MU16 and UPV196 (10,937, 54.7%) (filtered out with NVSM2, NVSM1 or both filters). This requirement is intended to minimize the discovery of false polymorphisms due to the alignment of paralogs, a potentially significant problem when aligning short sequence reads. Therefore, only nucleotide variants in relatively conserved or recently derived paralogs may have been incorrectly identified as SNPs. The drawback is that some true SNPs in hotspots of genetic diversity or genes under high diversifying selection may be discarded.

From the remaining 9,043 higher confident SNPs, that were monomorphic within and polymorphic between the two sequenced genotypes, we selected a set that met different criteria for facilitating validation and for their use in a Golden Gate genotyping assay [98, 99], discarding those that were closer than 60 bp to another SNP or INDEL, and/or were located closer than 60 bp to an intron and/or were closer than 60 bp to the unigene edge (filtered out with CS60, I60 and CL60, respectively). Finally, 3,538 SNPs were selected that met all criteria (see those with only a dot or a CEF tag in the filter field in additional file 8). From these, 144 SNPs were identified that can be detected as CAPS as they generate allele-specific restriction targets. We selected 50 of this putative CAPS, and 39 (80%) gave amplicons polymorphic between MU16 and UPV196 after digestion with the corresponding enzyme, which is comparable to that reported in previous studies with maize and Eucaliptus[49, 52]. Information of the validated CAPS is included in additional file 9:'Cucurbita SNPs validated as CAPS'. These CAPS markers are especially useful when experience or equipment for SNPs detection using other methods is not available.

All annotation results (ORFs, introns, descriptions, GO terms, Arabidopsis and melon orthologs, SNVs and SSRs) have been also added in additional file 10 using the GFF3 standard file format of The Sequence Ontology Resources [100]. This format for annotations results facilitates its uploading, representation and analysis.


The length and amount of the ESTs obtained with the 454 GSLX-Titanium platform has facilitated de novo assembly of the transcriptome in Cucurbita pepo, species for which limited prior sequence information is available, providing unigene sequences with length comparable to that obtained with traditional Sanger methodology. The unigene sequences constituted the first genomic resource for the Cucurbita genus. Cucurbita, along to Cucumis and Citrullus, are the three most economically important genera of the Cucurbitaceae family, whose economic importance is second only to the Solanaceae. Then this resource will enhance comparative studies within the family. The transcriptome will be important for gene discovery in Cucurbita and for future annotations of the Cucurbita genome sequence. The identified genes provide candidates for resistance genes against RNA viruses, fungal or bacterial pathogens. This is also an important resource for further study of sex determination and fruit quality in Cucurbita. The SSRs and SNPs identified here will constitute an important resource for mapping and marker-assisted breeding in Cucurbita pepo and closely related crops. The Zucchini and Scallop types are used as vegetables and highly valued in international markets, but C. pepo and also C. moschata, C. maxima and other minor Cucurbita species included a number of highly variable types that are food staples and rich sources of fat and vitamins in developing countries. All these crops will also take benefit from this genomic resource.


Plant material

Two cDNA libraries were constructed using material from the MU16 "Zucchini" Spanish cultivar (belonging to Cucurbita pepo L. ssp. pepo) and the UPV196 "Scallop" (belonging to C. pepo L. ssp. ovifera). Seeds of both cultivars were maintained at the COMAV Genebank. These two genotypes are representative of the sub specific variation of C. pepo, are readily crossable and have been selected as parentals of a RILs mapping population. They have contrasting phenotypes for traits interesting in squash breeding [15]: growth habit, sex expression, fruit shape and color, parthenocarpy tendency, shelf life, response to diseases., and are molecularly distant enough for mapping purposes [20]. Seeds were germinated and grown in trays containing a mixture of peat and sand. They were properly watered and grown at day/night temperatures of 28/20°C with a 16-h photoperiod. From each variety, tissue was sampled from the second and third true leaves, and from male and female flowers in pre and post-anthesis stage. Also the whole roots of 15 days-old plants were sampled. All tissues were collected and immediately frozen in liquid nitrogen and stored at -80°C till use.

cDNA preparation and sequencing

Total RNA was extracted from each tissue using the TRIzol® Reagent (Invitrogen, USA). We combined equivalent amounts of RNA from each tissue into two pools, one per cultivar. mRNA was purified from the total RNA using the illustraTM mRNA Purification Kit (GE Healthcare, Amersham Bioscience, Buckinghamshire, UK). Double-stranded cDNA was then synthesized from the two RNA pools with the SMART cDNA Library Construction Kit (Clontech, USA). A normalization step was carried out with TRIMMER Kit (Evrogen, Moscow, Russia) in order to prevent over-representation of the most common transcripts. The PCR products of cDNA were purified using the QIAquick PCR Purification Kit (QIAGEN, Germany). Normalization quality of cDNAs libraries was checked by quantitative PCR. The cDNA length and normalization are critical factors to have a good transcriptome representation, to have SNPs along the whole gene sequence, and to have a high quality SNP prediction. Approximately 1 μg of double-stranded cDNA from each of the two normalized cDNA pools were used for sequencing on a 454 GLX Titanium platform. A half-plate sequencing run was performed for each sample (Creative Genomics [101]). All raw sequences are available in the Sequence Read Archive at the National Center for Biotechnology Information (NCBI) [58], accession number SRA 029105.1

cDNA sequence processing and assembly

The whole sequence analysis was carried out by using the ngs_backbone pipeline [53]. The tools and analysis mentioned in the following sections were all performed by ngs_backbone, but here the third party tools, databases, and parameters used by ngs_backbone are described.

The raw 454 sequences were processed prior to the assembly. To remove the adaptors an alignment with the adaptors used during the sequencing process was done by Exonerate [102]. The low quality regions were removed by using Lucy [103]. Sequences shorter than 100 pb were discarded and not used for the assembly. The processed 454 sequences were assembled with Mira [54]. Default ngs_backbone options for this process were used.

Cucurbita gene annotation

Structural and functional annotation was performed by sequence comparison with public databases. All unique assembled sequences (unigenes) were sequentially compared using blast (cutoff e value of 1e-20) with the sequences in three major public protein databases, prioritizing handmade annotation databases. The used database order was Swiss-Prot [60], Arabidopsis proteins [61] and UniRef90 [62]. Once a sequence had a blast hit in one of the databases, a description was build from the description of that hit. Also, a bi-directional blast search comparison was performed in order to obtain a set of putative orthologs with Arabidopsis[61] and melon, using the melon unigenes contained in the Cucurbits genomic database (ICUGI).

Additionally, we performed a functional classification of the unigenes following the Gene Ontology (GO) scheme [64]. Blast2GO [65] was used for this purpose. Blast2GO used the results of a blast nr search (cutoff e value of 1e-20) to infer the relevant GO terms for every sequence. ORFs were predicted in the unigenes with the aid of the ESTScan software [104]. We used the Arabidopsis codon usage table to perform the ORF searching. Introns were assigned by aligning the unigenes with the melon genomic sequence using the Emboss: est2genome [105].

To assess codon usage, we used a set of the Cucurbita unigenes predicted to contain full-length coding regions. We performed a manual inspection, to ensure that no sequences containing frame-shift errors were included in the analysis. From this dataset, containing 4,118 sequences (529,864 codons), ORFs were defined and a codon usage table was created.

Identification of SSRs and SNPs

SSRs were annotated using the Sputnik software [94]. Sequences containing ≥ 4 di-, tri-, or tetra-nucleotide repeats were selected. A set of SSRs were validated using the sequenced genotypes (the Zucchini MU16 and the Scallop UPV96) and a set of 7 genotypes of C. pepo representative of the diversity within the species: representing 3 morphotypes of spp. pepo (3 additional zucchini, 1 vegetable marrow, 1 pumpkin, 1 Styrian pumpkin, an oil-pumpkin variety) and one additional morphotype of spp. ovifera (1 croockneck) and 1 accession of C. moschata. Primer pairs flanking each SSR locus were designed using the Primer3 program [106]. PCR reactions were performed in a final volume of 15 μL with 1× PCR buffer (100 mM Tris-HCl, 15 mM MgCl2, 500 mM KCl, pH 8.3), 200 μM dNTPs, 0.15 μM each primer and 2 μL of template (aprox. 10 ng/μL). PCR was performed as follows: denaturation at 95°C for 3 min, followed by 10 cycles of 30 s at 95°C, 30 s at 65°C (with each cycle the annealing temperature decreasing 1°C), and of 30 s at 72°C. Products were subsequently amplified for 20 cycles at 95°C for 30 s, 55°C for 30 s and 72°C for 30 s, with a final extension at 72°C for 5 min. The forward primer was designed adding an M13 tail to its 5' end. PCR products were separated using 6% polyacrylamide gels, 1× TBE buffer in a LI-COR 4300. IRD700 and IRD800-labeled amplicons were visualized by adding to PCR mixture 0.2 μM of fluorescent label (IR700 or IR800) M13 tail.

This Cucurbita ESTs collection has been produced using two representatives of the two C. pepo subspecies appropriate for SNP discovery. Ngs_backbone was also used to detect SNVs (Single nucleotide variations, SNPs and INDELs) by mapping the 454 processed reads against the unigene assembly using BWA (Burrows-Wheeler Aligner) [107]. We only kept SNVs meeting stringent quality criteria: 1) Minimum allele quality: accumulated sequence quality for every allele; 2) Minimum mapping quality. The default threshold set by ngs_backbone was set for both parameters.

Despite satisfying the quality criteria, not all the SNVs seemed equally reliable. Several filters were applied in order to maximize a successful validation and/or implementation in high throughput genotyping platforms such as Golden gate genotyping assay [98, 99]. For example, a filter to differentiate SNPs from INDELs (VKS: It is not an SNP) was applied, and also a filter that dismisses SNVs in highly variable regions (HVR4: The region has more than 4 SNVs per 100 bp). Other used filter were; CS60 (SNV is closer than 60 bp to another SNV), I60 (an intron is located closer than 60 bp), CL60 (SNV is closer than 60 bp to the sequence end), NVSM1 (SNV is variable within UPV196 or not sequenced in this genotype), NVSM2 (SNV is variable within MU16 or not sequenced in this genotype).

We also detected those SNVs that can be analyzed via CAPS (searching for alele-specific restriction targets, filter CEF) and validated a subset of them. To do this PCR conditions were used as described for SSRs and fragments digested with the corresponding enzymes were detected by agarose gel electrophoresis


  1. 1.

    Schaefer H, Heibl C, Renner SS: Gourds afloat: a dated phylogeny reveals an Asian origin of the gourd family (Cucurbitaceae) and numerous oversea dispersal events. Proc Biol Sci. 2009, 276: 843-851. 10.1098/rspb.2008.1447.

    PubMed  Google Scholar 

  2. 2.

    Boualem A, Fergany M, Fernandez R, Troadec C, Martin A, Morin H, Sari MA, Collin F, Flowers JM, Pitrat M, Purugganan MD, Dogimont C, Bendahmane A: A conserved mutation in an ethylene biosynthesis enzyme leads to andromonoecy in melons. Science. 2008, 321: 836-838. 10.1126/science.1159023.

    CAS  PubMed  Google Scholar 

  3. 3.

    Ezura H, Owino WO: Melon, an alternative model plant for elucidating fruit ripening. Plant Sci. 2008, 175: 121-129. 10.1016/j.plantsci.2008.02.004.

    CAS  Google Scholar 

  4. 4.

    Li Z, Huang S, Liu S, Pan J, Zhang Z, Tao Q, Shi Q, Jia Z, Zhang W, Chen H, Si L, Zhu L, Cai R: Molecular isolation of the M gene suggests that a conserved-residue conversion induces the formation of bisexual flowers in cucumber plants. Genetics. 2009, 182: 1381-1385. 10.1534/genetics.109.104737.

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Lin MK, Lee YJ, Lough TJ, Phinney BS, Lucas WJ: Analysis of the pumpkin phloem proteome provides functional insights into angiosperm sieve tube function. Mol Cell Proteomics. 2009, 8: 343-356.

    CAS  PubMed  Google Scholar 

  6. 6.

    Huang S, Li R, Zhang Z, Li L, Gu X, Fan W, Lucas WJ, Wang X, Xie B, Ni P, Ren Y, Zhu H, Li J, Lin K, Jin W, Fei Z, Li G, Staub J, Kilian A, van der Vossen EA, Wu Y, Guo J, He J, Jia Z, Ren Y, Tian G, Lu Y, Ruan J, Qian W, Wang M, Huang Q, Li B, Xuan Z, Cao J, Asan , Wu Z, Zhang J, Cai Q, Bai Y, Zhao B, Han Y, Li Y, Li X, Wang S, Shi Q, Liu S, Cho WK, Kim JY, Xu Y, Heller-Uszynska K, Miao H, Cheng Z, Zhang S, Wu J, Yang Y, Kang H, Li M, Liang H, Ren X, Shi Z, Wen M, Jian M, Yang H, Zhang G, Yang Z, Chen R, Liu S, Li J, Ma L, Liu H, Zhou Y, Zhao J, Fang X, Li G, Fang L, Li Y, Liu D, Zheng H, Zhang Y, Qin N, Li Z, Yang G, Yang S, Bolund L, Kristiansen K, Zheng H, Li S, Zhang X, Yang H, Wang J, Sun R, Zhang B, Jiang S, Wang J, Du Y, Li S: The genome of the cucumber, Cucumis sativus. L Nat Genet. 2009, 41: 1275-1281. 10.1038/ng.475.

    CAS  PubMed  Google Scholar 

  7. 7.

    González VM, Rodríguez-Moreno L, Centeno E, Benjak A, Garcia-Mas J, Puigdomènech P, Aranda MA: Genome-wide BAC-end sequencing of Cucumis melo using two BAC libraries. BMC Genomics. 2010, 11: 618-

    PubMed  PubMed Central  Google Scholar 

  8. 8.

    Gonzalez-Ibeas D, Blanca J, Roig C, Gonzalez-To M, Pico B, Truniger V, Gomez P, Deleu W, Cano-Delgado A, Arus P, Nuez F, García-Mas J, Puigdomènech P, Aranda MA: MELOGEN: an EST database for melon functional genomics. BMC Genomics. 2007, 8: 306-10.1186/1471-2164-8-306.

    PubMed  PubMed Central  Google Scholar 

  9. 9.

    Fernandez-Silva I, Eduardo I, Blanca J, Esteras C, Picó B, Nuez F, Arús P, Garcia-Mas J, Monforte AJ: Bin mapping of genomic and EST-derived SSRs in melon (Cucumis melo L.). Theor Appl Genet. 2008, 118: 139-150. 10.1007/s00122-008-0883-3.

    CAS  PubMed  Google Scholar 

  10. 10.

    Deleu W, Esteras C, Roig C, González-To M, Fernández-Silva I, Blanca J, Aranda MA, Arús P, Nuez F, Monforte AJ, Picó MB, Garcia-Mas J: A set of EST-SNPs for map saturation and cultivar identification in melon. BMC Plant Biol. 2009, 9: 90-10.1186/1471-2229-9-90.

    PubMed  PubMed Central  Google Scholar 

  11. 11.

    Mascarell-Creus A, Cañizares J, Vilarrasa J, Mora-García S, Blanca J, González-Ibeas D, Saladié M, Roig C, Deleu W, Picó B, López-Bigas N, Aranda MA, Garcia-Mas J, Nuez F, Puigdomènech P, Caño-Delgado A: An oligo-based microarray offers novel transcriptomic approaches for the analysis of pathogen resistance and fruit quality traits in melon (Cucumis melo L.). BMC Genomics. 2009, 10: 467-10.1186/1471-2164-10-467.

    PubMed  PubMed Central  Google Scholar 

  12. 12.

    Cucurbit Genomics Database of the International Cucurbit Genomics Initiative (ICuGI). []

  13. 13.

    Smith BD: Reassessing Coxcatlan Cave and the early history of domesticated plants in Mesoamerica. Proc Natl Acad Sci USA. 2005, 102: 9438-9445. 10.1073/pnas.0502847102.

    CAS  PubMed  Google Scholar 

  14. 14.

    Robinson RW, Decker-Walters DS: Cucurbits. CAB International. 1997, Wallingford

    Google Scholar 

  15. 15.

    Ferriol M, Picó B: Pumpkin and Winter Squash. Handbook of Plant Breeding. Vegetables I. Part 4. Edited by: Prohens J, Nuez, F. 2008, Springer, 1: 317-349. full_text.

    Google Scholar 

  16. 16.

    Paris H: Summer Squash. Handbook of Plant Breeding. Vegetables I. Part 4. Edited by: Prohens J, Nuez, F. 2008, Springer, 1: 351-381. full_text.

    Google Scholar 

  17. 17.

    Shokrzadeh M, Azadbakht M, Ahangar N, Hashemi A, Saeedi Saravi SS: Cytotoxicity of hydro-alcoholic extracts of Cucurbita pepo and Solanum nigrum on HepG2 and CT26 cancer cell lines. Phcog Mag. 2010, 6: 176-179. 10.4103/0973-1296.66931.

    CAS  PubMed  Google Scholar 

  18. 18.

    Fita A, Pico B, Roig C, Nuez F: Performance of Cucumis melo ssp agrestis as a rootstock for melon. J Hort Sci Biotech. 2007, 82: 184-190.

    Google Scholar 

  19. 19.

    Paris HS: A proposed subspecific classification for Cucurbita pepo. Phytologia. 1986, 61: 113-138.

    Google Scholar 

  20. 20.

    Ferriol M, Picó B, Nuez F: Genetic diversity of a germplasm collection of Cucurbita pepo using SRAP and AFLP markers. Theor Appl Genet. 2003, 107: 271-282. 10.1007/s00122-003-1242-z.

    CAS  PubMed  Google Scholar 

  21. 21.

    Brown RN, Myers JR: A genetic map of squash (Cucurbita sp.) with randomly amplified polymorphic DNA markers and morphological markers. J Am Soc Hort Sci. 2002, 127: 568-575.

    CAS  Google Scholar 

  22. 22.

    Lee YH, Jeon HJ, Hong KH, Kim BD: Use of random amplified polymorphic DNAs for linkage group analysis in interspecific hybrid F2 generation of Cucurbita. J Kor Soc Hort Sci. 1995, 36: 323-330.

    CAS  Google Scholar 

  23. 23.

    Zraidi A, Stift G, Pachner M, Shojaeiyan A, Gong A, Lelley TA: Consensus map for Cucurbita pepo. Mol Breed. 2007, 20: 375-388. 10.1007/s11032-007-9098-6.

    CAS  Google Scholar 

  24. 24.

    Gong L, Stift G, Kofler R, Pachner M, Lelley T: Microsatellites for the genus Cucurbita and an SSR-based genetic linkage map of Cucurbita pepo L. Theor Appl Genet. 2008, 117: 37-48. 10.1007/s00122-008-0750-2.

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Gong L, Pachner M, Kalai K, Lelley T: SSR-based genetic linkage map of Cucurbita moschata and its synteny with Cucurbita pepo. Genome. 2008, 51: 878-887. 10.1139/G08-072.

    CAS  PubMed  Google Scholar 

  26. 26.

    Paris HS, Brown RN: The genes of pumpkin and squash. HortScience. 2005, 40: 1620-1630.

    CAS  Google Scholar 

  27. 27.

    Nakajima N, Mori H, Yamazaki K, Imaseki H: Molecular Cloning and Sequence of a Complementary DNA Encoding 1-Aminocyclopropane-l-carboxylate Synthase Induced by Tissue Wounding. Plant Cell Physiol. 1990, 31: 1021-1029.

    CAS  Google Scholar 

  28. 28.

    Yamaguchi S, Saito T, Abe H, Yamane H, Murofushi N, Kamiya Y: Molecular cloning and characterization of a cDNA encoding the gibberellin biosynthetic enzyme ent-kaurene synthase B from pumpkin (Cucurbita maxima L.). Plant J. 1996, 10: 203-213. 10.1046/j.1365-313X.1996.10020203.x.

    CAS  PubMed  Google Scholar 

  29. 29.

    Ellard-Ivey M, Hopkins RB, White TJ, Lomax TL: Cloning, expression and N-terminal myristoylation of CpCPK1, a calcium-dependent protein kinase from zucchini (Cucurbita pepo L.). Plant Mol Biol. 1999, 39: 199-208. 10.1023/A:1006125918023.

    CAS  PubMed  Google Scholar 

  30. 30.

    Nishida I, Sugiura M, Enju A, Nakamura M: A Second Gene for Acyl-(Acyl-Carrier-Protein): Glycerol-3-Phosphate Acyltransferase in Squash, Cucurbita moschata cv. Shirogikuza, Codes for an Oleate-Selective Isozyme: Molecular Cloning and Protein Purification Studies. Plant Cell Physiol. 2000, 41: 1381-1391. 10.1093/pcp/pcd072.

    CAS  PubMed  Google Scholar 

  31. 31.

    Manzano S, Martínez C, Domínguez V, Avalos E, Garrido D, Gómez P, Jamilena M: A major Gene Conferring Reduced Ethylene Sensitivity and Maleness in Cucurbita pepo. J Plant Growth Regul. 2009, 29: 73-80. 10.1007/s00344-009-9116-5.

    Google Scholar 

  32. 32.

    Manzano S, Martínez C, Gómez P, Garrido D, Jamilena M: Cloning and characterization of two CTR1-like genes in Cucurbita pepo: regulation of their expression during male and female flower development. Sex Plant Reprod. 2010, 23: 301-313. 10.1007/s00497-010-0140-1.

    CAS  PubMed  Google Scholar 

  33. 33.

    Rafalski JA: Novel genetic mapping tools in plants: SNPs and LD-based approaches. Plant Sci. 2002, 162: 329-333. 10.1016/S0168-9452(01)00587-8.

    CAS  Google Scholar 

  34. 34.

    Thiel T, Michalek W, Varshney RK, Graner A: Exploiting EST databases for the development and characterization of gene-derived SSR-markers in barley (Hordeum vulgare L.). Theor Appl Genet. 2003, 106: 411-422.

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Sterky F, Bhalerao RR, Unneberg P, Segerman B, Nilsson P, Brunner AM, Charbonnel-Campaa L, Lindvall JJ, Tandre K, Strauss SH, others: A Populus EST resource for plant functional genomics. Proc Natl Acad Sci USA. 2004, 101: 13951-13956. 10.1073/pnas.0401641101.

    PubMed  Google Scholar 

  36. 36.

    Li X, Wu HX, Dillon SK, Southerton SG: Generation and analysis of expressed sequence tags from six developing xylem libraries in Pinus radiata D. Don BMC Genomics. 2009, 21: 41-10.1186/1471-2164-10-41.

    CAS  Google Scholar 

  37. 37.

    Raju NL, Gnanesh BN, Lekha P, Jayashree B, Pande S, Hiremath PJ, Byregowda M, Singh NK, Varshney RK: The first set of EST resource for gene discovery and marker development in pigeonpea (Cajanus cajan L.). BMC Plant Biol. 2010, 10: 45-10.1186/1471-2229-10-45.

    PubMed  PubMed Central  Google Scholar 

  38. 38.

    NCBI-dbEST database. []

  39. 39.

    Shendure J, Porreca GJ, Reppas NB, Lin X, McCutcheon JP, Rosenbaum AM, Wang MD, Zhang K, Mitra RD, Church GM: Accurate multiplex polony sequencing of an evolved bacterial genome. Science. 2005, 309: 1728-1732. 10.1126/science.1117389.

    CAS  PubMed  Google Scholar 

  40. 40.

    Metzker ML: Sequencing technologies-the next generation. Nat Rev Genet. 2010, 11: 31-46. 10.1038/nrg2626.

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Cheung F, Haas BJ, Goldberg SMD, May GD, Xiao Y, Town CD: Sequencing Medicago truncatula expressed sequenced tags using 454 Life Sciences technology. BMC Genomics. 2006, 7: 272-10.1186/1471-2164-7-272.

    PubMed  PubMed Central  Google Scholar 

  42. 42.

    Emrich SJ, Barbazuk WB, Li L, Schnable PS: Gene discovery and annotation using LCM-454 transcriptome sequencing. Genome Res. 2007, 17: 69-73. 10.1101/gr.5145806.

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Eveland AL, McCarty DR, Koch KE: Transcript Profiling by 3'-Untranslated Region Sequencing Resolves Expression of Gene Families. Plant Physiol. 2008, 146: 32-44. 10.1104/pp.107.108597.

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Weber AP, Weber KL, Carr K, Wilkerson C, Ohlrogge JB: Sampling the Arabidopsis transcriptome with massively parallel pyrosequencing. Plant Physiol. 2007, 144: 32-42. 10.1104/pp.107.096677.

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Alagna F, D'Agostino N, Torchia L, Servili M, Rao R, Pietrella M, Giuliano G, Chiusano ML, Baldoni L, Perrotta G: Comparative 454 pyrosequencing of transcripts from two olive genotypes during fruit development. BMC Genomics. 2009, 10: 399-10.1186/1471-2164-10-399.

    PubMed  PubMed Central  Google Scholar 

  46. 46.

    Barakat A, DiLoreto DS, Zhang Y, Smith C, Baier K, Powell WA, Wheeler N, Sederoff R, Carlson JE: Comparison of the transcriptomes of American chestnut (Castanea dentata) and Chinese chestnut (Castanea mollissima) in response to the chestnut blight infection. BMC Plant Biology. 2009, 9: 51-10.1186/1471-2229-9-51.

    PubMed  PubMed Central  Google Scholar 

  47. 47.

    Wang W, Wang Y, Zhang Q, Qi Y, Guo D: Global characterization of Artemisia annua glandular trichome transcriptome using 454 pyrosequencing. BMC Genomics. 2009, 10: 465-10.1186/1471-2164-10-465.

    PubMed  PubMed Central  Google Scholar 

  48. 48.

    Bellin D, Ferrarini A, Chimento A, Kaiser O, Levenkova N, Bouffard P, Delledonne M: Combining next-generation pyrosequencing with microarray for large scale expression analysis in non-model species. BMC Genomics. 2009, 24: 55-

    Google Scholar 

  49. 49.

    Barbazuk WB, Emrich SJ, Chen HD, Li L, Schnable PS: SNP discovery via 454 transcriptome sequencing. Plant J. 2006, 51: 910-918. 10.1111/j.1365-313X.2007.03193.x.

    Google Scholar 

  50. 50.

    Trick M, Long Y, Meng J, Bancroft I: Single nucleotide polymorphism (SNP) discovery in the polyploid Brassica napus using Solexa transcriptome sequencing. Plant Biotechnol J. 2009, 7: 334-346. 10.1111/j.1467-7652.2008.00396.x.

    CAS  PubMed  Google Scholar 

  51. 51.

    Guo S, Zheng Y, Joung JG, Liu S, Zhang Z, Crasta OR, Sobral BW, Xu Y, Huang S, Fei Z: Transcriptome sequencing and comparative analysis of cucumber flowers with different sex types. BMC Genomics. 2010, 11: 384-10.1186/1471-2164-11-384.

    PubMed  PubMed Central  Google Scholar 

  52. 52.

    Novaes E, Drost D, Farmerie W, Pappas G, Grattapaglia D, Sederoff RR, Kirst M: High-throughput gene and SNP discovery in Eucalyptus grandis, an uncharacterized genome. BMC Genomics. 2008, 9: 312-10.1186/1471-2164-9-312.

    PubMed  PubMed Central  Google Scholar 

  53. 53.

    Bioinformatics at COMAV. Ngs_backbone. []

  54. 54.

    Chevreux B, Pfisterer T, Drescher B, Driesel AJ, Müller WE, Wetter T, Suhai S: Using the miraEST assembler for reliable and automated mRNA transcript assembly and SNP detection in sequenced ESTs. Genome Res. 2004, 14: 1147-1159. 10.1101/gr.1917404.

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Vega-Arreguin JC, Ibarra-Laclette E, Jimenez-Moraila B, Martinez O, Vielle-Calzada JP, Herrera-Estrella L, Herrera-Estrella A: Deep sampling of the Palomero maize transcriptome by a high throughput strategy of pyrosequencing. BMC Genomics. 2009, 10: 299-10.1186/1471-2164-10-299.

    PubMed  PubMed Central  Google Scholar 

  56. 56.

    Sun Ch, Li Y, Wu Q, Luo H, Sun Y, Song J, Lui EMK, Chen S: De novo sequencing and analysis of the American ginseng root transcriptome using a GS FLX Titanium platform to discover putative genes involved in ginsenoside biosynthesis. BMC Genomics. 2010, 11: 262-10.1186/1471-2164-11-262.

    PubMed  PubMed Central  Google Scholar 

  57. 57.

    Li Y, Luo HM, Sun C, Song JY, Sun YZ, Wu Q, Wang N, Yao H, Steinmetz A, Chen SL: EST analysis reveals putative genes involved in glycyrrhizin biosynthesis. BMC Genomics. 2010, 11: 268-10.1186/1471-2164-11-268.

    PubMed  PubMed Central  Google Scholar 

  58. 58.

    The Sequence Read Archive at the National Center for Biotechnology Information. []

  59. 59.

    Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997, 25: 3389-3402. 10.1093/nar/25.17.3389.

    CAS  PubMed  PubMed Central  Google Scholar 

  60. 60.

    The Swiss-Prot Database. uniprot_sprot_ release of 2010 04 23, []

  61. 61.

    The TAIR Database: The Arabidopsis Information Resource. Tair_9_pep_ release 2009 06 19, []

  62. 62.

    Uniref90. uniref90_release 2010 04 23, []

  63. 63.

    The Gene Ontology Consortium: Gene Ontology: tool for the unification of biology. Nat Genet. 2000, 25: 25-29. 10.1038/75556.

    PubMed Central  Google Scholar 

  64. 64.

    Gene Ontology Database. []

  65. 65.

    Conesa A, Götz S, Blast2GO: A Comprehensive Suite for Functional Analysis in plant Genomics. Int J Plant Genomics. 2008, 619832-

    Google Scholar 

  66. 66.

    NCBI Nr, Nonredundant protein Database. release of 20010-03-27, []

  67. 67.

    Cartieaux F, Thibaud MC, Zimmerli L, Lessard P, Sarrobert C, David P, Gerbaud A, Robaglia C, Somerville S, Nussaume L: Transcriptome analysis of Arabidopsis colonized by a plant-growth promoting rhizobacterium reveals a general effect on disease resistance. Plant J. 2003, 36: 177-188. 10.1046/j.1365-313X.2003.01867.x.

    CAS  PubMed  Google Scholar 

  68. 68.

    Tan X, Meyers B, Kozik A, West M, Morgante M, St Clair D, Bent A, Michelmore R: Global expression analysis of nucleotide binding site-leucine rich repeat-encoding and related genes in Arabidopsis. BMC Plant Biol. 2007, 7: 56-10.1186/1471-2229-7-56.

    PubMed  PubMed Central  Google Scholar 

  69. 69.

    Ascencio-Ibanez JT, Sozzani R, Lee TJ, Chu TM, Wolfinger RD, Cella R, Hanley-Bowdoin L: Global analysis of Arabidopsis gene expression uncovers a complex array of changes impacting pathogen response and cell cycle during geminivirus infection. Plant Physiol. 2008, 148: 436-454. 10.1104/pp.108.121038.

    CAS  PubMed  PubMed Central  Google Scholar 

  70. 70.

    Yamanaka T, Imai T, Satoh R, Kawashima A, Takahashi M, Tomita K, Kubota K, Meshi T, Naito S, Ishikawa M: Complete Inhibition of Tobamovirus Multiplication by Simultaneous Mutations in Two Homologous Host Genes. J Virol. 2002, 76: 2491-2497. 10.1128/jvi.76.5.2491-2497.2002.

    CAS  PubMed  PubMed Central  Google Scholar 

  71. 71.

    Nicaise V, Gallois JL, Chafiai F, Allen LM, Schurdi-Levraud V, Browning KS, Candresse T, Caranta C, Le Gall O, German-Retana S: Coordinated and selective recruitment of eIF4E and eIF4G factors for potyvirus infection in Arabidopsis thaliana. FEBS letters. 2007, 581: 1041-1046. 10.1016/j.febslet.2007.02.007.

    CAS  PubMed  Google Scholar 

  72. 72.

    Nieto C, Morales M, Orjeda G, Clepet C, Monfort A, Sturbois B, Puigdomenech P, Pitrat M, Caboche M, Dogimont C, Garcia-Mas J, Aranda MA, Bendahmane A: An eIF4E allele confers resistance to an uncapped and non-polyadenylated RNA virus in melon. Plant J. 2006, 48: 452-462. 10.1111/j.1365-313X.2006.02885.x.

    CAS  PubMed  Google Scholar 

  73. 73.

    Piron F, Maryse N, Minoïa S, Piednoir E, Moretti A, Salgues A, Zamir D, Caranta C, Bendahmane A: An induced mutation in tomato eIF4E leads to immunity to two potyviruses. PloS ONE. 2010, 5 (6): e11313-10.1371/journal.pone.0011313.

    PubMed  PubMed Central  Google Scholar 

  74. 74.

    Belhaj K, Lin B, Mauch F: The chloroplast protein RPH1 plays a role in the immune response of Arabidopsis to Phytophthora brassicae. Plant J. 2009, 58: 287-298. 10.1111/j.1365-313X.2008.03779.x.

    CAS  PubMed  Google Scholar 

  75. 75.

    Zhang J, Lu H, Li X, Li Y, Cui H, Wen CK, Tang X, Su Z, Zhou JM: Effector-Triggered and Pathogen-Associated Molecular Pattern-Triggered Immunity Differentially Contribute to Basal Resistance to Pseudomonas syringae. Mol Plant Microb Inter. 2010, 23: 940-948. 10.1094/MPMI-23-7-0940.

    CAS  Google Scholar 

  76. 76.

    Chen Z, Hartmann HA, Wu MJ, Friedman E, Chen JG, Pulley M, Schulze-Lefert P, Panstruga R, Jones A: Expression analysis of the AtMLO Gene Family Encoding Plant-Specific Seven-Transmembrane Domain Proteins. Plant Mol Biol. 2006, 60: 583-97. 10.1007/s11103-005-5082-x.

    CAS  PubMed  Google Scholar 

  77. 77.

    Maeda K, Houjyou Y, Komatsu T, Hori H, Kodaira T, Ishikawa A: AGB1 and PMR5 Contribute to PEN2-Mediated Preinvasion Resistance to Magnaporthe oryzae in Arabidopsis thaliana. Mol Plant Microb Inter. 2009, 22: 1331-1340. 10.1094/MPMI-22-11-1331.

    CAS  Google Scholar 

  78. 78.

    Li Z, Huang S, Liu S, Pan J, Zhang Z, Tao Q, Shi Q, Jia Z, Zhang W, Chen H, Si L, Zhu L, Cai R: Molecular isolation of the M gene suggests that a conserved-residue conversion induces the formation of bisexual flowers in cucumber plants. Genetics. 2009, 182: 1381-1385. 10.1534/genetics.109.104737.

    CAS  PubMed  PubMed Central  Google Scholar 

  79. 79.

    Martin A, Troadec C, Boualem A, Rajab M, Fernandez R, Morin H, Pitrat M, Dogimont C, Bendahmane A: A transposon-induced epigenetic change leads to sex determination in melon. Nature. 2009, 461: 1135-1138. 10.1038/nature08498.

    CAS  PubMed  Google Scholar 

  80. 80.

    Mara CD, Huang T, Irish VF: The Arabidopsis Floral Homeotic Proteins APETALA3 and PISTILLATA Negatively Regulate the BANQUO Genes Implicated in Light Signaling. Plant Cell. 2010, 22: 690-702. 10.1105/tpc.109.065946.

    CAS  PubMed  PubMed Central  Google Scholar 

  81. 81.

    Xu ML, Jiang JF, Ge L, Xu YY, Chen H, Zhao Y, Bi YR, Wen JQ, Chong K: FPF1 transgene leads to altered flowering time and root development in rice. Plant Cell Rep. 2005, 24: 79-85. 10.1007/s00299-004-0906-8.

    CAS  PubMed  Google Scholar 

  82. 82.

    Lee H, Yoo SJ, Lee JH, Kim W, Yoo SK, Fitzgerald H, Carrington JC, Ahn JH: Genetic framework for flowering-time regulation by ambient temperature-responsive miRNAs in Arabidopsis. Nucl Acids Res. 2010, 38: 3081-3093. 10.1093/nar/gkp1240.

    CAS  PubMed  Google Scholar 

  83. 83.

    Papadopoulou E, Grumet R: Brassinosteriod-induced femaleness in cucumber and relationship to ethylene production. HortSci. 2005, 40: 1763-1767.

    CAS  Google Scholar 

  84. 84.

    Parry G, Ward S, Cernac A, Dharmasiri S, Estelle M: The Arabidopsis SUPPRESSOR OF AUXIN RESISTANCE Proteins are Nucleoporins with an Important Role in Hormone Signaling and Development. The Plant Cell. 2006, 18: 1590-1596. 10.1105/tpc.106.041566.

    CAS  PubMed  PubMed Central  Google Scholar 

  85. 85.

    Zhang H, Harry DE, Ma C, Yuceer C, Hsu CY, Vikram V, Shevchenko O, Etherington E, Strauss SH: Precocious flowering in trees: the FLOWERING LOCUS T gene as a research and breeding tool in Populus. J Exp Bot. 2010, 61: 2549-2560. 10.1093/jxb/erq092.

    CAS  PubMed  Google Scholar 

  86. 86.

    Giovannoni JJ: Genetic regulation of fruit development and ripening. Plant Cell. 2004, 16: 170-180. 10.1105/tpc.019158.

    Google Scholar 

  87. 87.

    Monforte AJ, Oliver M, Gonzalo MJ, Alvarez JM, Dolcet-Sanjuan R, Arus P: Identification of quantitative trait loci involved in fruit quality traits in melon (Cucumis melo L.). Theor Appl Genet. 2004, 108: 750-758. 10.1007/s00122-003-1483-x.

    CAS  PubMed  Google Scholar 

  88. 88.

    Cuevas HE, Staub JE, Simon PW, Zalapa JE, McCreight JD: Mapping of genetic loci that regulate quantity of beta-carotene in fruit of US Western Shipping melon (Cucumis melo L.). Theor Appl Genet. 2008, 117: 1345-1359. 10.1007/s00122-008-0868-2.

    CAS  PubMed  Google Scholar 

  89. 89.

    Liu JP, Van Eck J, Cong B, Tanksley SD: A new class of regulatory genes underlying the cause of pear-shaped tomato fruit. Proc Natl Acad Sci USA. 2002, 99: 13302-13306. 10.1073/pnas.162485999.

    CAS  PubMed  Google Scholar 

  90. 90.

    Manning K, Tor M, Poole M, Hong Y, Thompson AJ, King GJ, Giovannoni JJ, Seymour GB: A naturally occurring epigenetic mutation in a gene encoding an SBP-box transcription factor inhibits tomato fruit ripening. Nat Genet. 2006, 38: 948-952. 10.1038/ng1841.

    CAS  PubMed  PubMed Central  Google Scholar 

  91. 91.

    Tadmor Y, Paris HS, Meir A, Schaffer AA, Lewinsohn E: Dual role of the pigmentation gene B in affecting carotenoid and vitamin E content in squash (Cucurbita pepo) mesocarp. J Agric Food Chem. 2005, 53: 9759-9763. 10.1021/jf0520591.

    CAS  PubMed  Google Scholar 

  92. 92.

    Maass D, Arango J, Wüst F, Beyer P, Welsch R: Carotenoid crystal formation in Arabidopsis and carrot roots caused by increased phytoene synthase protein levels. PLoS ONE. 2009, 4 (7): e6373-10.1371/journal.pone.0006373.

    PubMed  PubMed Central  Google Scholar 

  93. 93.

    Singh SK, Fischer U, Singh M, Grebe M, Marchant A: Insight into the early steps of root hair formation revealed by the procuste1 cellulose synthase mutant of Arabidopsis thaliana. BMC Plant Biol. 2008, 8: 57-10.1186/1471-2229-8-57.

    PubMed  PubMed Central  Google Scholar 

  94. 94.

    Sputnik. []

  95. 95.

    Morgante M, Hanafey M, Powell W: Microsatellites are preferentially associated with nonrepetitive DNA in plant genomes. Nat Genet. 2002, 30: 194-200. 10.1038/ng822.

    CAS  PubMed  Google Scholar 

  96. 96.

    Levi A, Wechter P, Davis A: EST-PCR markers representing watermelon fruit genes are polymorphic among watermelon heirloom cultivars sharing a narrow genetic base. Plant Gen Res. 2009, 7: 16-32. 10.1017/S1479262108014366.

    CAS  Google Scholar 

  97. 97.

    Paris HS, Yonash N, Portnoy V, Mozes-Daube N, Tzuri G, Katzir N: Assessment of genetic relationships in Cucurbita pepo (Cucurbitaceae) using AFLP, ISSR, and SSR markers. Theor Appl Genet. 2003, 106: 971-978.

    CAS  PubMed  Google Scholar 

  98. 98.

    Fan JB, Chee MS, Gunderson KL: Highly parallel genomic assays. Nat Rev Genet. 2006, 7: 632-644. 10.1038/nrg1901.

    CAS  PubMed  Google Scholar 

  99. 99.

    Gupta PK, Rustgi S, Mir RR: Array-based high throughput DNA markers for crop improvement. Heredity. 2008, 101: 5-18. 10.1038/hdy.2008.35.

    CAS  PubMed  Google Scholar 

  100. 100.

    The Sequence Ontology Resources. []

  101. 101.

    Creative Genomics. []

  102. 102.

    Exonerate, a generic tool for sequence alignment. []

  103. 103.

    Lucy DNA sequence quality and vector trimming tool. []

  104. 104.

    ESTScan software. []

  105. 105.

    The Emboss: est2genome. []

  106. 106.

    Primer 3. []

  107. 107.

    Li H, Durbin R: Fast and accurate short read alignment with Burrows-Wheeler Transform. Bioinformatics. 25: 1754-60. 10.1093/bioinformatics/btp324.

    CAS  PubMed  PubMed Central  Google Scholar 

  108. 108.

    VCF (Variant Call Format) version 4.0. []

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Acknowledgements and Funding

This research has been funded by the project of the Spanish Instituto Nacional de Investigación y Tecnología Agraria INIA (RTA2008-00035-C02-02). Authors thank Cristina Esteras for providing technical help in markers validation and MELONOMICS project (2009-2012) of the Fundación Genoma España for providing access to the draft of the melon genomic sequence.

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Correspondence to Belén Picó.

Additional information

Authors' contributions

CR, JC, and BP prepared the cDNA libraries for sequencing. BP and CR selected and validated the SSRs and CAPS. JB and PZ performed the bioinformatic analysis. JC participated in the annotation analyses. BP is the main coordinator of the Cucurbita project and participated in the conception of the study together with JB and JC. BP was primarily responsible for drafting and revising the manuscript with contributions from co-authors. FN is the director of COMAV and critically reviewed the manuscript. All authors read and approved the final manuscript.

Electronic supplementary material

Additional file 1:Cucurbita unigenes. The fasta sequence of the 49,610 Cucurbita unigenes assembled from 454 ESTs is included. (ZIP 10 MB)

Additional file 2:Annotation of ORFs and introns. Unigene length and predicted position of ORFs and introns is indicated for the whole Cucurbita unigene collection. (XLSX 4 MB)

Additional file 3:Blast Hits and GO terms. Descriptions build from the blast hit obtained by a sequential blast search of 3 protein databases [6062] and GO annotations for the whole Cucurbita unigene collection are compiled. (XLS 5 MB)

Additional file 4:Arabidopsis and melon orthologs. Orthologs found by reciprocal search with Arabidopsis and melon databases [12, 61] are indicated. (XLS 2 MB)

Additional file 5:Genes potentially encoding pathogen resistance, flowering, fruit and root traits. Genes were identified in the Cucurbita data set by comparison with the Arabidopsis and melon databases [12, 61]. A brief description and the corresponding Arabidopsis and melon locus are given for each unigene. (XLSX 16 KB)

Additional file 6:Cucurbita SSRs. The table provides the list of SSRs identified from the Cucurbita unigene dataset, their length, motif sequences, position in the unigene, and scores. (XLSX 101 KB)

Additional file 7:Validated Cucurbita SSRs. The table provides the list of Cucurbita SSRs experimentally validated using a collection of C. pepo. Primers used for validation, number of alleles, and polymorphism detected between the sequenced genotypes, within subspecies, and within the zucchini morphotypes are included. Also transference to C. moschata is indicated. (XLSX 14 KB)

Additional file 8:Cucurbita SNPs and INDELs. The file provides the list of SNPs and INDELs (SNVs) identified from the Cucurbita unigene dataset, their position, the nucleotide change (or I or D for insertion and deletion in INDELs), the quality of the polymorphic base, and additional information about allele number and frequency. The different filters applied for the in silico selection of the SNVs are also indicated: VKS (is not an SNPs, is an INDEL), HVR4 (SNV is in a region with more than 4 SNVs per 100 bp), CS60 (SNV is closer than 60 bp to another SNV), I60 (SNV is located closer than 60 bp to an intron), CL60 (SNV is closer than 60 bp to the sequence end), NVSM1 (SNV is variable within UPV196, or not sequenced in this genotype), NVSM2 (SNV is variable within MU16, or not sequenced in this genotype), CEF (SNV does not alter a restriction target and cannot be detected as a CAPS). The VCF (Variant Call Format) version 4.0 has been used for this file [108]. (VCF 2 MB)

Additional file 9:Cucurbita SNPs validated as CAPS. The table provides the list of SNPs that affected restriction targets and were validated via CAPS, their position, location, primers used for validation and the occurrence of polymorphism between the two sequenced varieties. (XLSX 13 KB)

Additional file 10:Annotation results in GFF3. All the annotation results (ORFS, introns, descriptions, GO terms, Arabidopsis and melon orthologs, SNVs and SSRs) are provided also in the standard format GFF3 of The Sequence Ontology Resources [100] that facilitate annotations uploading, representation and analysis. (ZIP 4 MB)

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Blanca, J., Cañizares, J., Roig, C. et al. Transcriptome characterization and high throughput SSRs and SNPs discovery in Cucurbita pepo (Cucurbitaceae). BMC Genomics 12, 104 (2011).

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  • Gene Ontology
  • Powdery Mildew
  • Melon
  • Cucurbita Pepo
  • Cucurbitaceae Family