Ophir et al. BMC Genomics 2010, 11:269 http://www.biomedcentral.com/1471-2164/11/269 nal intensity in microarray hybridization using labeled genomic DNA (gDNA). SFPs can be used, with no further information, as markers, or they can be sequenced to identify the genetic difference (frequently a single nucle- otide polymorphism (SNP) or a small indel). SFP technol- ogy draws its strength from the fact that it can be implemented in a variety of genetic applications, such as marker discovery and fine mapping of traits, as well as for genome-wide association studies [3,6,7]. SFP discovery has been relatively successfully implemented in model organisms such as Arabidobsis [5] and Drosophila [8], and in non-model organisms such as rice and soybean [9,10]. To date, all hybridizations have been performed on high-density short oligonucleotides (Affymetrix arrays). These types of arrays are only available for a few organ- isms, they are expensive and they are not flexible in their design. Therefore, the ability to implement this technol- ogy on any custom array (Agilent, Nimblegen, and oth- ers) has the potential to create a very useful tool in many breeding programs for agricultural crops. Melon is among the most important fleshy fruits for fresh consumption. It belongs to the family Cucurbita- ceae which includes other important crops, such as watermelon, cucumber and squash. Melon is a diploid species (2n = 24) of African origin with a high level of phenotypic and molecular variation [11] and a genome size estimated at 450 Mb [12]. Over the last 15 years, sev- eral research groups have published genetic maps for melon [13-22]. An integrated map, proposed to serve as a reference map, was constructed, based on two recombi- nant inbred line (RIL) populations ('Vedrantaise' × PI161375 and 'Vedrantaise' × PI414723). This map con- tains 668 loci; it spans 1654 cM on 12 linkage groups. It also includes 23 morphological traits [20]. Another map, based on two populations derived from a cross between 'Piel de Sapo' and PI161375, identified several quantita- tive trait loci (QTLs) controlling fruit-quality traits [18]. The Katzir group has recently established a reference map based on a cross between PI414723 and 'Dulce' [14] and constructed a QTL map using RILs developed from this population [17]. Other resources that were created for melon are a limited EST collection [23] and BAC libraries [24]. Although a substantial amount of effort has been invested in creating genetic maps and molecular tools for melon, only a few genes have been cloned by map-based cloning [25-27]. The genetic maps that exist for melon are not dense enough (containing only a few hundred markers) and are still the limiting factor for MAS, cloning of genes of interest and WGA (genome- wide association studies). We chose melon as representative of a large group of vegetables and other crops that are agriculturally impor- tant but are still lagging behind in their genomic resources. Although sequencing has become extremely Page 2 of 10 affordable using new technologies, we are still not at a stage at which we can sequence a variety of the same plant again and again. In this paper, we present the ability to use a custom-designed microarray with longer oligos (45-55 mer), based on a partial EST database of melon, to discover thousands of new markers for melon. These markers can be used for genetic mapping, breeding and association studies. Results SFP discovery We designed a custom oligo microarray (Agilent) based on the melon ESTs presented in the Cucurbit Genomic database (ICuGI) [23]. The microarray contains 186,600 unique probes from 16,114 UniGenes covering approxi- mately 9 Mb of cDNA sequence from different ecotypes and different conditions (for further details, see Meth- ods). For marker discovery, we used DNA from the par- ents of our mapping population, developed by Katzir's group from a cross between representatives of two sub- species of Cucumis melo L.: PI414723 (subspecies agres- tis) and 'Dulce' (subspecies melo). Two biological replicates from PI414723 and two from 'Dulce' were used. Each biological replicate contained gDNAs pooled from 10 different plants. gDNA samples were labeled and hybridized using standard Agilent procedures for com- parative genomic hybridization (CGH) (see Methods). Predicted SFPs were identified using a linear model for microarray (LIMMA) [28]. The data were analyzed and ranked by two statistics: the adjusted p-value (q-value) and the B statistic, which calculates the log odds that a given SFP will be different between two populations (Fig- ure 1). The number of SFPs found using an adjusted p- value (q-value) of 5% was 6184 in 3849 UniGenes. Using the B statistic with B ≥ 1.5, which indicates 82% probabil- ity of having differential alleles (SFPs), we found 2503 SFPs in 1771 genes. Due to the fact that the q-value and B statistic rank the SFPs in the same order, and since the B statistic requires prior knowledge of the proportion of SFPs while the moderated-t statistic does not, we used the critical value of moderated-t statistic, i.e., a q-value < 0.05. SFP validation Since the number of available sequences from 'Dulce' and PI414723 in the database is relatively low, we performed validation by direct sequencing of PCR fragments that were amplified from gDNA of the two parents, PI414723 and 'Dulce'. SFPs were chosen randomly for sequencing (with one limitation applied: the flanking regions of the SFPs were set to 50 bp to be able to identify oligos for PCR). Oligos flanking the SFPs were designed and PCR products were amplified from PI414723 and 'Dulce' gDNA pools: 245 putative SFPs were fully sequenced