 Hello everybody. In this training you are going to carry out the analysis of the transcript term of the model organism Arabidoptis thaliana. The ultimate goal of this tutorial is the identification of potential elements belonging to the brassinosteroids mediated gene regulator network. The tutorial is divided in two blocks. In the first one, we will explore the steps necessary to perform the analysis of micro-irona transcripts. In the second block, which will be developed by Pafun-Kumach by then, we'll carry out the analysis of the total transcripts as well as the target identification. Let's start with the introduction. As cesil organisms, the survival of plant under adverse environmental conditions depends to a large extent to their ability to perceive stress stably and respond appropriately to counteract the potentially damaging effects. Coordination of phthermones and reactive of jetting species are considered a key element for enhancing stress resistance, allowing fine tuning of gene expression in response to environmental changes. These molecules constitute complex analgenic words, endowing with the ability to respond to a variable natural environment. There's currently a broad consensus in classifying plant hormones into 9 groups. Absicic acid, oxins, cytokinins, ethylent, gibrelins, jansmonate, salicylic acid, strinolactones and bracinosteroids. VIN, this lab group of phthermones, the one on which we are going to focus our analysis. Bracinosteroids are a group of plant steroid hormone essential for plant growth and development, as well as for controlling abiotic and viotic stress. Structurally, bracinosteroids are polydroxylated steroid-derivated with close similarity to plant hormones. Bracinosteroids have the ability to stimulate plant growth, influencing germination, rise of genesis, flowering, senescence, abscessing and ripening process. In addition, several experimental results have demonstrated the ability to confer a resistant to several types of biotic and biotic stress, such as heat, coal, salinity and growth. Signs bracinosteroids control several important agronomic threats, such as stress tolerance, the study of gene regulatory network in which this hormone is involved have acquired great importance in the field of biotechnology, offering enormous potential for increasing growth yields. According to several studies, microRNA are one of the molecular mechanisms involved in the regulation of bracinosteroid-mediated gene expression networks. MicroRNA are 20 to 22 nucleotide-small RNAs characterized for regulating gene expression at porous transcripts and a level. MicroRNAs are distinguished from other small RNAs by being generated from precursor, harboring an imperfect stem block structure. Unlike in animals, the pre-processing of plant microRNAs occur in the nucleus. The pre-microRNA are then exported to the cytoplasm after methylation and then incorporated into the argonate-1 protein to form the ricks complex. The microRNA itself doesn't have the ability to cleave mRNAs or interfere with translation, but it plays a role in scanning the appropriate target. Power factors determine that the microRNA are considered master regulators. The first one is that multiple microRNA genes are regulated under given environmental conditions. The second one is that computational prediction estimates that each microRNA regulates hundreds of genes. The third factor is that the majority of plant microRNAs regulate genes encoding for transcription factors. And the last one is that target genes include not only mRNA, but also long non-coding RNAs. Now we'll introduce the experimental design on which the analysis is based. As mentioned above, we can divide the analysis into three stages, differential expression of microRNAs, differential expression of mRNAs, and target identification. The starting hypothesis is that there should be sequence complementarity between upregulated microRNAs and downregulated mRNAs. In this tutorial, we'll focus on the differential expression of microRNAs. The result obtained will be used later for target identification using the Tiger 5D tool. Now we'll provide some details about the data and the tools. For the analysis, we'll use data generated by the Illumina Genome Analyzer 2X sequencing platform. MicroRNA was sequenced from three viological replicate samples of Arbidoxy-Stalina in each of two conditions, control and epipyrocynolite treated synthylins. In order to simplify the analysis, the viological replicates will be grouped in Galaxy into data collections. Collections allow to combine numerous datasets in a single entity that can be easily manipulated. In this part of the tutorial, we'll use six main tools, which can be divided into three categories, quality assessment, microRNA quantification, and differential expression. FastQC provides a simple way to do some quality control checks on raw sequence data coming from high throughput sequencing. MultiQC allows aggregated results generated by FastQC across many samples into a single report. Tringalore is a wrapper tool around kubeladat and FastQC, which allow consistently to apply quality and adapt their trimming to FastQ files. To carry out the microRNA quantification, we'll use two modules belonging to the MIRDEEP tool, MIRDEEP MIPER, and MIRDEEP QUANTIFYER. Finally, we'll use DSEC2, a package for differential expression analysis of common data based on negative binomial distribution. Now, let's start.