Genome-wide identification and development of miniature inverted-repeat transposable elements and intron length polymorphic markers in tea plant (Camellia sinensis).

Marker-assisted breeding and tagging of important quantitative trait loci for beneficial traits are two important strategies for the genetic improvement of plants. However, the scarcity of diverse and informative genetic markers covering the entire tea genome limits our ability to achieve such goals. In the present study, we used a comparative genomic approach to mine the tea genomes of Camellia sinensis var. assamica (CSA) and C. sinensis var. sinensis (CSS) to identify the markers to differentiate tea genotypes. In our study, 43 and 60 Camellia sinensis miniature inverted-repeat transposable element (CsMITE) families were identified in these two sequenced tea genomes, with 23,170 and 37,958 putative CsMITE sequences, respectively. In addition, we identified 4912 non-redundant, Camellia sinensis intron length polymorphic (CsILP) markers, 85.8% of which were shared by both the CSS and CSA genomes. To validate, a subset of randomly chosen 10 CsMITE markers and 15 CsILP markers were tested and found to be polymorphic among the 36 highly diverse tea genotypes. These genome-wide markers, which were identified for the first time in tea plants, will be a valuable resource for genetic diversity analysis as well as marker-assisted breeding of tea genotypes for quality improvement.

ASRmiRNA: Abiotic Stress-Responsive miRNA Prediction in Plants by Using Machine Learning Algorithms with Pseudo K-Tuple Nucleotide Compositional Features.

MicroRNAs (miRNAs) play a significant role in plant response to different abiotic stresses. Thus, identification of abiotic stress-responsive miRNAs holds immense importance in crop breeding programmes to develop cultivars resistant to abiotic stresses. In this study, we developed a machine learning-based computational method for prediction of miRNAs associated with abiotic stresses. Three types of datasets were used for prediction, i.e., miRNA, Pre-miRNA, and Pre-miRNA + miRNA. The pseudo K-tuple nucleotide compositional features were generated for each sequence to transform the sequence data into numeric feature vectors. Support vector machine (SVM) was employed for prediction. The area under receiver operating characteristics curve (auROC) of 70.21, 69.71, 77.94 and area under precision-recall curve (auPRC) of 69.96, 65.64, 77.32 percentages were obtained for miRNA, Pre-miRNA, and Pre-miRNA + miRNA datasets, respectively. Overall prediction accuracies for the independent test set were 62.33, 64.85, 69.21 percentages, respectively, for the three datasets. The SVM also achieved higher accuracy than other learning methods such as random forest, extreme gradient boosting, and adaptive boosting. To implement our method with ease, an online prediction server "ASRmiRNA" has been developed. The proposed approach is believed to supplement the existing effort for identification of abiotic stress-responsive miRNAs and Pre-miRNAs.