 Hello everyone. I am Bianca Passat. I am a biologist. Right now I work at Aeneos and I am a member of Aristoteles Hadzianus group. And in September I will start my PhD in cancer research. Today I'm going to talk about our effort to design a workflow for detection and functional annotation of long non-coding RNA. Long non-coding RNAs are RNA molecules that are transcribed from RNA polymerase to are longer than 200 nucleotides, but they do not encode for proteins. Nevertheless, they seem to be major regulators of normal cellular events and they have been connected to various diseases, including cancer. As I said, our goal was to design a workflow for analyzing RNA-safe data regarding long non-coding RNA and we further extended it to include mRNA and microRNA molecules as well. If anyone wants to try the tools or the workflows, she or he can go to this website and register for free, of course. Now, I should say that our workflow is made up of two big sub-workflows and each sub-workflow is made from interchangeable modules that can run independently. Most tools were already available for Galaxy, but for those that weren't available, we made their repository toolset and published them there. And we also resolved all tools dependencies inside Docker containers to make the whole project more portable. Moving on to the workflow. What we actually do is take faster or faster Q-files and try to align them to a reference genome and then assemble those reads into transcripts with string type. And for those transcripts, classify them with TFF compare and choose the categories that we believe that store potential long non-coding RNA. For those potential long non-coding RNA, we will perform an extra step of screening, which is ensuring that we keep only transcripts longer than 200 nucleotides and also estimating the coding potential with three different algorithms and keeping only the consensus of the results. When this is over, we will have a subset of mRNAs, a subset of microRNAs, and a subset of long non-coding RNAs. And what we want to do next is perform a differential expression analysis on them with drug products, which is a non-parametric per robust statistical test. When this is over, we will have a differentially expressed molecules that we would like to further analyze, examine, whether they interact with each other. And we can do that with RNA interactions, which searches databases like Npinter and Trice, or test at sequence level with Miranda or Riblast. After we construct our Interactum, we can functionally annotate it using Biotranslator. And Biotranslator can also help to bring into focus the HUB genes that really regulate the phenotype that we are observing. So we start with FASTA or FASQ files, and we end up with signature extraction. On the next slide, I'm showing you some of the results that the workflow can generate. So on the left you can see a visualization of cytoscape of a small interactum, of a part of our interactum. On the middle you can see the prioritized list that the Biotranslator can generate. And this list can be further used to stratify patients from TCGA cohort, and we offer a tool just for that. And on the right you can see the systemic interpretation of our signature, again done by Biotranslator. That was my presentation. I would like to thank my colleagues for this collective effort, and of course thank Galaxy Community for organizing this conference and giving me the opportunity to talk and share my work. And I would like to hear any questions.