The discovery of novel resistance genes (R-genes) is an important component in disease resistance breeding. Nevertheless, R-gene identification from wild species and close relatives of plants is not only a difficult but also a cumbersome process. In this study, ResCap, a support vector machine-based high-throughput R-gene prediction and probe generation pipeline has been developed to generate probes from genomic datasets. ResCap contains two integral modules. The first module identifies the R-genes and R-gene like sequences under four categories containing different domains such as TIR-NBS-LRR (TNL), CC-NBS-LRR (CNL), Receptor-like kinase (RLK) and Receptor-like proteins (RLPs). The second module generates probes from extracted nucleotide sequences of resistance genes to conduct sequence capture (SeqCap) experiments. For the validation of ResCap pipeline, ResCap generated probes were synthesized and a sequence capture experiment was performed to capture expressed resistance genes among six spring barley genotypes. The developed ResCap pipeline in combination with the performed sequence capture experiment has shown to increase precision of R-gene identification while simultaneously allowing rapid gene validation including non-sequenced plants.;The ResCap pipeline is available at http://rescap.ltj.slu.se/ResCap/.;sandeep.kushwaha@slu.se or sandeep@niab.org.in.;Supplementary materials are available at Bioinformatics Advances online.