Ophir et al. BMC Genomics 2010, 11:269 http://www.biomedcentral.com/1471-2164/11/269 R E S E A R C H A R T I C L E Open Access RHesiegarchh -atrthicleroughput marker discovery in melon using a self-designed oligo microarray Ron Ophir1, Ravit Eshed1, Rotem Harel-Beja2, Galil Tzuri2, Vitaly Portnoy2, Yoseph Burger2, Shai Uliel1, Nurit Katzir2 and Amir Sherman*1 Abstract Background: Genetic maps constitute the basis of breeding programs for many agricultural organisms. The creation of these maps is dependent on marker discovery. Melon, among other crops, is still lagging in genomic resources, limiting the ability to discover new markers in a high-throughput fashion. One of the methods used to search for molecular markers is DNA hybridization to microarrays. Microarray hybridization of DNA from different accessions can reveal differences between them--single-feature polymorphisms (SFPs). These SFPs can be used as markers for breeding purposes, or they can be converted to conventional markers by sequencing. This method has been utilized in a few different plants to discover genetic variation, using Affymetrix arrays that exist for only a few organisms. We applied this approach with some modifications for marker discovery in melon. Results: Using a custom-designed oligonucleotide microarray based on a partial EST collection of melon, we discovered 6184 putative SFPs between the parents of our mapping population. Validation by sequencing of 245 SFPs from the two parents showed a sensitivity of around 79%. Most SFPs (81%) contained single-nucleotide polymorphisms. Testing the SFPs on another mapping population of melon confirmed that many of them are conserved. Conclusion: Thousands of new SFPs that can be used for genetic mapping and molecular-assisted breeding in melon were discovered using a custom-designed oligo microarray. A portion of these SFPs are conserved and can be used in different breeding populations. Although improvement of the discovery rate is still needed, this approach is applicable to many agricultural systems with limited genomic resources. Background Genetic maps based on molecular markers are a key step in marker-assisted selection (MAS) for plant breeding and for gene identification based on positional informa- tion [1]. Discovery of novel genetic variation, enhanced selection techniques and the identification of genotypes with new or improved traits can lead to superior strains of interest. The density of the markers on a genetic map defines the map's resolution and the ability to introduce these traits using molecular means. New high-through- put technologies for the discovery of genetic markers and linkage analysis can enhance plant breeding efforts [2]. In the last few years, genomic tools have begun to replace the traditional methods of molecular marker dis- * Correspondence: asherman@agri.gov.il 1 Genomic Unit, Plant Sciences Institute, Volcani Center, Agricultural Research Organization, PO Box 6, Bet Dagan 50250, Israel Full list of author information is available at the end of the article covery. Use of computational tools to search sequence information for molecular markers [3,4], or genomic technologies such as whole-genome scanning (WGS), have enabled the discovery of thousands of markers in a single experiment [3]. These markers can be mapped to create high-density genetic maps or used directly for MAS [3-5]. However, use of these types of technologies for plant breeding is still restricted, as many agricultural systems have a limited amount of genomic resources. Furthermore, plant species contain wide genetic variation that cannot be explored by sequencing only one or two plants from each species. One of the technologies that has been used for identifi- cation of genetic variation is single-feature polymor- phism (SFP) [3-5]. This approach is based on the concept that target DNA that perfectly matches its probes binds with greater affinity than one with a mismatch. Thus, nat- ural imperfections can be detected as a difference in sig- © 2010 Ophir et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons BioMed Central Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.