Funct Integr Genomics (2011) 11:651–657 ICPL 87, ICPL 8755, and ICPL 227. For each comparison, we used the top 15% outlying score as cutoff for calling SFP-containing probe sets, within which a probe will be identified as a SFP probe if it accounts for more than 40% of overall outlying score of its residing probe set. Primer designing and sequencing For validating the predicted SFPs, primer pairs were designed for the selected SFPs and used for amplification and sequencing of the genomic fragments. In this context, polymerase chain reaction (PCR) primer pairs were designed to bind at least 100 bases upstream or downstream of the probes predicted to contain SFPs. Sequence data for the soybean genes corresponding to the selected SFPs (available at www.Affymetrix.com) were used for BLASTN analysis with the pigeonpea transcriptome assembly (Dubey et al. 2011) and corresponding 200–500 bp sequence region from the pigeonpea genes was used to design the primer pair with help of Primer3 (Rozen and Skaletsky 2000). The primer pairs were used to generate the amplicons using the same PCR conditions as given in Nayak et al. (2009). Subsequently, the amplified products were se- quenced on an ABI 3730 DNA Analyzer using BigDye Terminator V1.1 (Macrogen, Seoul, Korea). Good quality sequence data were used to form contigs by using DNA Baser software (http://www.dnabaser.com). Further, contigs were aligned and viewed with the Bioedit software (http:// www.mbio.ncsu.edu/BioEdit/). In-house developed soft- ware “Divest” (Jayashree et al. 2009) was used to detect the presence of SNPs and INDELs in the sequence data. Gene ontology analysis Functional assignment of pigeonpea tentative unique sequences (TUSs), homologues to the soybean genes that detected SFPs in the parental genotypes segregating for drought tolerance, was accomplished by finding significant hits in the UniProt database (e value ≤ 10−5; Jain et al. 2009). The gene ontology IDs were retrieved from the UniProt database using keywords obtained in the BLASTX descriptions of the most significant hits. Based on the Gene Ontology ID, unique sequences were categorized into three principal categories: biological processes, cellular local- izations, and molecular functions. Results and discussion Single feature polymorphism discovery The Affymetrix Soybean Genome Arrays were used with cRNA of six genotypes of pigeonpea and differentially 653 expression data were analyzed using RPP method to identify SFPs in pigeonpea (Das et al. 2008). In the genome arrays, only 37,376 transcripts were represented by probe sets consisting of 11 PM. Therefore, the microarray data obtained for these 37,376 transcripts were analyzed for generating “present”, “marginal”, and “absent” calls for these transcripts in the genotypes for SFP analysis. Scatter plots of 411,136 PM probes for all pair-wise combinations revealed much less variation between two biological replicates of each accession (except replicates of ICP 28) compared to that between any two accessions, suggesting the feasibility of detecting SFP probes between accessions. The number of “present” calls varied, ranging from 4,882 to 5,810 probes for the 15 pair-wise comparisons. It is important to mention here that the number of probes showing differential hybridization and thus qualifying for potential SFPs in the combination ICPL 8755 and ICPL 227 (4,989) were less than those in ICP 28 and ICPW 94 (5,405) or ICPL 151 and ICPL 87 (5,455) pairs (Table 1). By using stringent criteria and RPP analysis, a total of 5,692 potential SFPs were discovered across the six genotypes (Table 1). As an example, Fig. 1 shows detection of SFP between ICPL 8755 and ICPL 227 genotypes for the probe #5 of the SFP probe set GmaAffx.5953.1.A1_at. As the six genotypes analyzed in this study represents the parents of three mapping populations, an effort was made to identify the SFPs for different parental combinations. In this context, 850 SFPs were identified from 5,405 “present” probe sets in the ICP 28×ICPW 94 (cross 1), 854 SFPs out of 5,455 “present” probe sets in the ICPL 151×ICPL 87 (cross 2), and 780 SFPs out of 4,989 in the ICPL 8755×ICPL 227 (cross 3). However, many SFPs that were detected were unique to one parental combination. The number of SFPs in common between cross 1 and 2 was 17, between cross 1 and 3 it was 14, and between cross 2 and 3 a total of 19 SFPs were in common. A total of 10 SFPs were in common for all the three crosses used. These results reconfirm earlier observations on occurrence of low level of genetic diversity based on other marker systems such as amplified fragment length polymorphisms (Panguluri et al. 2006), Diversity Array Technology (Yang et al. 2011) and SSRs (Saxena et al. 2010a). Nevertheless, the present study adds a set of about 1,000 novel markers (SFPs) for genetics and breeding analysis in pigeonpea. Validation of SFPs With an objective to validate the SFPs at sequence level, a subset of SFPs was selected for allele-specific resequencing. In this context, homologues sequences in pigeonpea for the soybean probes detecting SFPs were identified by sequence analysis of the corresponding genes of soybean with the transcriptome assembly of pigeonpea (Dubey et al. 2011) comprising 127,754 TUSs defined based on cluster analysis