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Ginger (Zingiber officinale Rosc.) is an economically important and valuable spice crop around the world. It is used as food, spice, condiment, and medicine. A considerable extent of genetic diversity in ginger occurs in the Western Ghats and North-Eastern India. However, genetic diversity studies at the molecular level in ginger is limited due to limited availability of genetic and genomic information. In the present study, for the first time, we have identified and validated expressed sequence tag (EST)-simple sequence repeat (SSR) markers from ginger. We obtained 16,790 EST-SSR loci from 78987 unigenes, and 4597 SSR loci in the predicted 76929 coding sequences from RNA-Seq assembled contigs of ginger through Illumina paired-end sequencing. Gene ontology results indicate that the unigenes with SSR loci participate in various biological processes such as metabolism, growth, and development in ginger. One hundred and twenty-five primer pairs were designed from unigenes and coding sequences. These primers were tested for PCR optimization, characterization, and amplification and identified 12 novel EST-SSR markers. Twelve flanking polymorphic EST-SSR primers were validated using 48 ginger genotypes representing North-Eastern India and different eco-geographical adaptations by PCR amplification and allele sizing through capillary electrophoresis. Twelve EST-SSR primers generated a total of 111 alleles with an average of 9.25 alleles per locus and allele sizes ranging between 115-189bp. This study implies that the SSR markers designed from transcriptome sequences provides ample EST-SSR resources, which are helpful for genetic diversity analysis of Zingiberaceae species and molecular verification of ginger genotypes.

Phytophthora is a genus of oomycetes that cause extensive crop damage and economic loss. Specific proteins from the host and pathogen facilitate their interaction and mediate a multifaceted mechanism in infection. In this study, we utilized published protein protein interactions between Phytophthora and its hosts for developing a model for prediction. We applied supervised learning algorithms- Support vector machine (SVM) and Ensemble methods to predict interactions.Different features of proteins in host and pathogen proteins like amino acid composition, dipeptide composition, pseudo amino acid composition, amphiphilic pseudo amino acid composition, C/T/D, conjoint triads, autocorrelation, sequence order coupling number, quasi-sequence order descriptors were utilized to develop the model for the binary classification, whether the proteins interact or not. The relative importance of different protein features in the training model were also evaluated. SVM with radial kernel had an accuracy of 75%. Bagging algorithm Random Forest showed an accuracy of 84.6%. A GLM ensemble of K-Nearest Neighbour (KNN), SVM (radial), rpart and random forest gave an accuracy of 70.1%. The model developed can be trained with more experimentally validated interactions to improve the accuracy. Furthermore we constructed a protein-protein interaction network of host and pathogen proteins to depict the interaction network operating during pathogenesis and evaluated the network topology. The results of the study may be taken forward for experimental validation.