Almond (Prunus dulcis Mill.), one of the most important nut crops, requires chilling during winter to develop fruiting buds. However, early spring chilling and late spring frost may damage the reproductive tissues leading to reduction in the rate of productivity. Despite the importance of transcriptional changes and regulation, little is known about the almond’s transcriptome under the cold stress conditions. In the current reserch, we used RNA-seq technique to study the response of the reporuductive tissues of almond (anther and ovary) to frost stress. RNA sequencing resulted in more than 20 million reads from anther and ovary tissues of almond, individually. About 40,000 contigs were assembled and annotated de novo in each tissue. Profile of gene expression in ovary showed significant alterations in 5,112 genes, whereas in anther 6,926 genes were affected by freezing stress. Around two thousands of these genes were common altered genes in both ovary and anther libraries. Gene ontology indicated the involvement of differentially expressed (DE) genes, responding to freezing stress, in metabolic and cellular processes. qRT-PCR analysis verified the expression pattern of eight genes randomley selected from the DE genes. In conclusion, the almond gene index assembled in this study and the reported DE genes can provide great insights on responses of almond and other Prunus species to abiotic stresses. The obtained results from current research would add to the limited available information on almond and Rosaceae. Besides, the findings would be very useful for comparative studies as the number of DE genes reported here is much higher than that of any previous reports in this plant.

Motivation: As the COVID-19 pandemic is spreading around the world, the SARS-CoV-2 virus is evolving with mutations that potentially change and fine-tune functions of the proteins coded in its genome.Results: Coronavirus3D website integrates data on the SARS-CoV-2 virus mutations with information about 3D structures of its proteins, allowing users to visually analyze the mutations in their 3D context.Availability and implementation: Coronavirus3D server is freely available at https://coronavirus3d.org.

A novel integrative pipeline is presented for discovery of potential cancer-susceptibility regions (PCSRs) by calculating the number of altered genes at each chromosomal region, using expression microarray datasets of different human cancers (HCs). Our novel approach comprises primarily predicting PCSRs followed by identification of key genes in these regions to obtain potential regions harboring new cancer-associated variants. In addition to finding new cancer causal variants, another advantage in prediction of such risk regions is simultaneous study of different types of genomic variants in line with focusing on specific chromosomal regions. Using this pipeline we extracted numbers of regions with highly altered expression levels in cancer condition. Regulatory networks were also constructed for different types of cancers following the identification of altered mRNA and microRNAs. Interestingly, results showed that GAPDH, LIFR, ZEB2, mir-21, mir-30a, mir-141 and mir-200c, all located at PCSRs, are common altered factors in constructed networks. We found a number of clusters of altered mRNAs and miRNAs on predicted PCSRs (e.g.12p13.31) and their common regulators including KLF4 and SOX10. Large scale prediction of risk regions based on transcriptome data can open a window in comprehensive study of cancer risk factors and the other human diseases.