Extrapolative microRNA precursor based SSR mining from tea EST database in respect to agronomic traits

Genome Survey and SSR Analysis of Camellia nitidissima Chi (Theaceae)

Camellia nitidissima Chi (CNC), a species of golden Camellia, is well known as "the queen of camellias." It is an ornamental, medicinal, and edible plant grown in China. In this study, we conducted a genome survey sequencing analysis and simple sequence repeat (SSR) identification of CNC using the Illumina sequencing platform. The 21-mer analysis predicted its genome size to be 2,778.82 Mb, with heterozygosity and repetition rates of 1.42% and 65.27%, respectively. The CNC genome sequences were assembled into 9,399,197 scaffolds, covering ∼2,910 Mb and an N50 of 869 base pair. Its genomic characteristics were found to be similar to those of Camellia oleifera. In addition, 1,940,616 SSRs were identified from the genome data, including mono-(61.85%), di-(28.71%), tri-(6.51%), tetra-(1.85%), penta-(0.57%), and hexanucleotide motifs (0.51%). We believe these data will provide a useful foundation for the development of novel molecular markers for CNC as well as for further whole-genome sequencing of CNC.

NCodR: A multi-class support vector machine classification to distinguish non-coding RNAs in Viridiplantae

Non-coding RNAs (ncRNAs) are major players in the regulation of gene expression. This study analyses seven classes of ncRNAs in plants using sequence and secondary structure-based RNA folding measures. We observe distinct regions in the distribution of AU content along with overlapping regions for different ncRNA classes. Additionally, we find similar averages for minimum folding energy index across various ncRNAs classes except for pre-miRNAs and lncRNAs. Various RNA folding measures show similar trends among the different ncRNA classes except for pre-miRNAs and lncRNAs. We observe different k-mer repeat signatures of length three among various ncRNA classes. However, in pre-miRs and lncRNAs, a diffuse pattern of k-mers is observed. Using these attributes, we train eight different classifiers to discriminate various ncRNA classes in plants. Support vector machines employing radial basis function show the highest accuracy (average F1 of ~96%) in discriminating ncRNAs, and the classifier is implemented as a web server, NCodR.

Development and validation of heat-responsive candidate gene and miRNA gene based SSR markers to analysis genetic diversity in wheat for heat tolerance breeding

Being a major staple food crop of the world, wheat provides nutritional food security to the global populations. Heat stress is a major abiotic stress that adversely affects wheat production throughout the world including Indo-Gangatic Plains (IGP) where four wheat growing countries viz., India, Bangladesh, Nepal and Pakistan produce 42% of the total wheat production. Therefore, identification of heat stress responsive molecular markers is imperative to marker assisted breeding programs. Information about trait specific gene based SSRs is available but there is lack of information on SSRs from non-coding regions. In the present study, we developed 177 heat-responsive gene-based SSRs (cg-SSR) and MIR gene-based SSR (miRNA-SSR) markers from wheat genome for assessing genetic diversity analysis of thirty- six contrasting wheat genotypes for heat tolerance. Of the 177 SSR loci, 144 yielded unambiguous and repeatable amplicons, however, thirty-seven were found polymorphic among the 36 wheat genotypes. The polymorphism information content (PIC) of primers used in this study ranged from 0.03-0.73, with a mean of 0.35. Number of alleles produced per primer varied from 2 to 6, with a mean of 2.58. The UPGMA dendrogram analysis grouped all wheat genotypes into four clusters. The markers developed in this study has potential application in the MAS based breeding programs for developing heat tolerant wheat cultivars and genetic diversity analysis of wheat germplasm. Identification of noncoding region based SSRs will be fruitful for identification of trait specific wheat germplasm.