Ophir et al. BMC Genomics 2010, 11:269 http://www.biomedcentral.com/1471-2164/11/269 Page 7 of 10 Figure 5 Conservation of genetic markers between two mapping populations. Shared SFPs between two mapping populations, PI161375 (PIS)/'Piel de Sapo' (PS) and PI414723 (PI)/'Dulce'. If the same probe was found statistically significant in both comparisons, the SFP was flagged as common. The subset of intersecting SFPs is presented in a Venn diagram. The number of genes including these SFPs is pre- sented in brackets. The large set of probes allows identification of large sets of genetic markers in different mapping populations. Although these sets of markers are not ordered, one can still use them for breeding experiments as a whole - genome screening tool and for creating "classical" genetic maps. Developing a custom-designed array creates the possi- bility of using SFP technology in many agricultural sys- tems that do not have an Affymetrix platform and allows much more flexibility in design and use. The low price of the screening and the relatively rapid execution and flexi- bility make it a useful technology. Although SFP technol- ogy is far from perfect, and a reduction in FDRs is needed to make thi approach more efficient, we firmly believe that it can contribute to marker discovery, MAS and WGS approaches in various crop plants. We therefore envision the ability to use WGS (SFP technology and oth- ers) as a routine element in the process of MAS in breed- ing programs as the next challenge in the field of breeding technology. Conclusion SFPs can be discovered using custom-designed microar- rays and partial genomic data. This approach can be used with many agricultural organisms for marker discovery and breeding. Methods Array design A single-feature array was designed based on 16,637 non- redundant cDNA sequences of the Melon Genomics Database from different genotypes and conditions [23]. This set of sequences was used as input for OligoWIZ 2.0 [38] to design an optimal set of probes along the melon partial transcriptome. We designed probes of 50 base pairs (bp) in length ± 5 bp with Tm optimization (76.6°C). The Tm, as well as other features of the probes such as cross-hybridization, folding, and low complexity, were summed to total scores with weightings of 0.245, 0.61, 0.025, and 0.12, respectively. The output of this process was 198,500 probes. Probes with a total score lower than 0.3 that included "N" in their sequence were filtered out, leaving a total of 186,600 probes. This design was loaded on the Agilent eArray site for array production https:// earray.chem.agilent.com/earray/. Genomic DNA preparation Genomic DNA was extracted using standard procedures [39] from young leaves pooled from 10 plants of each of the different cultivars. Two separate pools were created for each cultivar. The DNA was treated with RNAseH (Sigma, USA) and re-purified using phenol/chloroform/ isoamyl alcohol followed by ethanol precipitation. Microarray labeling, hybridization and scanning gDNA (2.5 μg) was digested with restriction enzymes AluI and RsaI (Promega, USA). The digested DNA was used for labeling and hybridization following Agilent pro- cedures for CGH [40]. Scanning of the array was per- formed with the Agilent scanner. Labeling, hybridization and scanning were done at the Weizmann Institute's DNA Microarray Facility (Rehovot, Israel). Statistical analysis Probe signal pre-processing and fitting were performed with the R-package for LIMMA [28], following back- ground subtraction; for within-array normalization we applied the "lowess" method [41], and for between-array normalization the "Aquntile" method [42]. To select for statistical significance of the signal ratios, two subsequent steps were applied. 1) A least-square fitting using the following equation: ⎛ P − D ⎞ ⎡ 0 ⎜⎜ D − P ⎟⎟ = ⎢⎢ 1 ⎜ D − D ⎟ ⎢ −1 ⎝⎜⎜ P − P ⎠⎟⎟ ⎢⎣ 0 1 0 0 0 ⎤ −01 ⎥⎥⎥ * ⎛⎜⎜ DP12−−DD11 ⎟⎟⎞ ⎥ ⎝⎜ P2 − D1 ⎠⎟ −1 −1 ⎦ (1) Where the vector of P-D, D-P, D-D, and P-P is the vec- tor of log signal ratios, the matrix of 1, 0, and -1 is the design matrix, and the vector of D2-D1, P1-D1, and P2- D1 is a vector of the estimated coefficients. Next we used these coefficients to estimate three comparisons (con- trasts): the inter-variation