 Okay, so this webinar series consists of a total of four sessions that will take place in April and May. As you know, the Galaxy ecosystem is very broad in scope and reaches different audiences, each one with different needs. So the aim of these four sessions is to show to those different profiles which resources are available and useful for them. And today we will focus on the researchers and in the upcoming weeks we will go through training and education, tool development and admin roles. Next please. So I'm happy to introduce to our speaker today that is Dave Clemens. I guess that Dave needs no introduction. But for those that don't know him, he has a background in computer science and he is now for more than 10 years, a worldwide community coordinator for Galaxy. He has a very extensive experience in all community-related topics, training, outreach. So Dave, thanks for presenting today. I'm looking forward to learning from you as always. Thanks, Ben. Good morning, good evening, wherever you are. So thanks for spending some time with us today. Let's see. Let's talk about our goals and what our not goals are. So our goals for today are for you to learn where and how to find the resources and help you need to use Galaxy successfully. So a lot of lengths today, a lot of pointers to stuff, not spending a huge amount of time on any one thing, but a lot of resources. Okay, not goals for today are to teach how to use Galaxy. Don't worry if you're brand new to Galaxy or if you only know a little bit because we will highlight resources for that today. How to learn how to do that and how to become an expert. But the goal of today is not to teach you how to use Galaxy, but rather to give you resources you can come back to next week when you have a question. And you vaguely remember hearing something about X. So that's our goal for today. Okay. The slides are at this URL. They're now linked to from that URL anywhere. Anyway, and this is important. Again, because of our goal, we're going to link to a lot of resources today. And so a week from now, you want to be able to find these slides, find those links. So bit.ly slash gr4-researchers. We'll take you to the event page for this which has a link to the slides. So very important. Okay. So before we dive in, two or three slides on what Galaxy is. Projects definition. Galaxy is an open web-based platform for accessible, reproducible and transparent data analysis. This reflects our core goals and this is what we aim to do. But it's possibly not how most people think of Galaxy. And I think the way a lot of people think of Galaxy is a lot closer to Keith Bradenham's definition. Galaxy is a web-based platform that provides a simplified interface to many popular bioinformatics tools. So put another way, what Galaxy does is protect researchers from the command line. And it protects you from Linux systems administration. So you can use Galaxy to do sophisticated analyses. You don't have to learn what to do when LiveZ does not install on your system. You don't have to learn how to use the command line. You can do everything in Galaxy from a web browser. Okay. So again, two definitions basically different sides of the same coin. But it's worth pointing out. So this only captures half the picture though because equally important is that Galaxy is a community. And this is one of my favorite quotes. James Taylor was one of the founders and PIs of Galaxy and this is from a keynote at the 2019 community conference. The most important thing about Galaxy is this community. And as we go through slides today, you're going to see that it really is a huge community and that community contributes an awful lot. And we really support each other. And so keep in mind that we are not just a platform. We are a global, vibrant, helpful community around the world. And without that, we would not be successful. So I cannot stress this enough. We are equally a platform and a community. Okay. So today is divided into three sections. One is learning how to crawl if you're brand new to Galaxy. This is the shortest of the three. So if you're new to Galaxy and even if you're not new to Galaxy, your absolute best friend is the Galaxy Training Network. And I want to spend some time exploring that. So we're going to go here, which is traininggalaxyproject.org. Okay. And I'm going to zoom in a little. Okay. And if you're new, you want to spend some time here. And in particular, I recommend starting here, Introduction to Galaxy Analysis. I'm going to zoom in one more time. There we go. Introduction to Galaxy Analysis. Start there. And I clicked on that. There's the core material. There's also some stuff down here about R. So you can run R in Galaxy using RStudio. And this tells you how to do that. And these get really rave reviews, by the way. But let's focus up here for now. There are lessons. And for each lesson, there are a number of things it can have. So some of them have slide decks. Some of them have hands-on tutorials, input data sets, workflows, and Galaxy instances. We'll cover most of that right now. So let's start with slides here, a short introduction to Galaxy. Let's click on that. And it brings up a slide deck. And you can walk through this and learn what Galaxy is about. Okay. Or whatever the topic is. So this is just about Galaxy in general. I'm going to use the arrow keys to move forward and backward. So basically, Introduction to Galaxy. Note at the bottom here it says Press P to view the presenter notes. So we can do that. And this will show us what the presenter notes are. So you can actually teach using Galaxy. And in fact, that's what next week's webinar is about, is Galaxy Resources for educators and trainers. And they will talk about this as well as many other things. But there are extensive notes here. And you can use this to present materials. So I just hit P to get out of that. Okay. We're going to go back to this. So there are slide decks. The next thing I want to highlight is these little down arrows. And they're available on the hands-on and on the slide. If you click on that, it says you can look at this in French or Japanese or Spanish or so on. And so I'm going to try French. I'm going to click there. And it uses Google Translate to do that. Okay. Now that's going to work mostly. Okay. But we do some extra stuff to make sure it works. When the slides and the tutorials are developed, the authors try to use simplified, straightforward English so that Google Translate can cope. Okay. And so in theory and usually in practice, what you get is useful translations from that because we actually do put some effort into making sure it works. Let's go back. So slides, multiple translations are available. This right here, I think this is the, is that the only one on this page? Yes. That's the only one on this page. Some slide decks are also available as videos. So I'm going to click on that. I think I am also sharing my sound. So hopefully you'll hear this. And it brings up a video of the slides. And I'm going to play this. A short introduction to Galaxy. What is Galaxy? So what you're hearing there? Are you hearing that, Bea? Yes. Yeah. Oh, good. Okay. It's a pleasant, vaguely British voice. And that voice is automatically generated from the presenter notes that we saw earlier. So some of the slide decks, and we're improving more of them as we go, some of the slide decks, a lot of thought has been given to those presenter notes so that you can automatically generate these videos. And the text shows up here. So you can actually hear what's being said. And all of it is driven by the presenter notes. And all of this happens automatically. So it's kind of magic to have that. Okay. So lots of videos, lots of slide decks. Let's come over here to the hands-on. Again, available in translation. I'm going to click on this. A short introduction to Galaxy by Anna Syme. Anna is in Australia. She is helping with the plant transcriptomics training that's going on this week worldwide. She's also doing a workshop, I think, today in the Australian sense or maybe tomorrow in the Australian sense, because I'm in the US and the dates are confusing. But she's doing a workshop this week on plants as well. So this is what a typical tutorial looks like. There's an overview at the top. And then we scroll down and hear this. Since this is the introduction, it tells you about what Galaxy looks like, three-panel interface, all the things you can do. And then it comes down here and there are hands-on sections. And it says, okay, name your history. And it tells you how to name it and where you type it. And it walks you through. So these tutorials are a mixture of why you're doing it and how to do it. And they have comments. They have hands-on. I'm scrolling down. They have use a tool like here, FastQC. Right there, that is a tool for looking at the quality of reads and here's some output from FastQC. Okay. So there's also questions here. I think I saw one. No, it's down here. Anyway, there's a lot here right there. It's questions, solutions. And it's really thorough. And it's a great way to get introduced to Galaxy. So I'm going to go back right there. Okay, so that's hands-on. And there are a lot of these. I think our current number of lessons is over 150. Maybe over 170 by now. They mostly have input data sets. And these are stored in Zenodo. So they're online. If you ever create training materials, you know, creating the input data sets to use is actually very challenging. And the tutorials will all have links to those. And you can also get them directly from Zenodo. The last thing I want to talk about here is this last one. So Galaxy is open source software. It's been installed in hundreds, if not thousands of places around the world. Some of those are public instances. And what the training network does is it walks through this tutorial and sees what tools it needs. And then it walks through the public instances of Galaxy, of which there are over 100, and says which of these support the tools in this tutorial. Since this is a pretty basic one, there are a lot of public servers that support it. And you can pick any of these. So if I click on High City Explorer, it'll take me to that website. And then I can run this tutorial. Okay. We'll come back to this. More Galaxy instances in a bit. So let's go here. Okay. I think we covered... Oh, we didn't get one thing, which is the chat. Okay. So most Galaxy training network pages have this at the bottom, open chat. And if we click on that, it opens up a chat room. And most of these will link to the general chat room for the Galaxy training network. So if you have a question and it's not working for you, or you want to know how to do things, or if you want to even know how to write a tutorial, you can click on that and you can start chatting. This is implemented in Gitter. And you can go there by clicking on that, or you can just start chatting here. Okay. So I'm going to close that. Really, if you have any troubles, I can't recommend that enough. This is me going to chat and actually asking questions of people. And there are people on all day around the world. So let's see. Okay. So learning to walk. So if you're new to Galaxy, those are resources I think you should spend time with. So the training network and focus on the introductory material. This will get you introduced to how things work, what the terminology is, what's a history when we talk about histories? What do we mean by dataset? What the three panel interfaces, how to run a tool, how to connect tools, things like that. So now that you have the basics of how to use Galaxy, you probably want to use Galaxy for your own analysis to ask your own questions. And this is going to require a bit more knowledge than what you got in the learning to crawl. So that's what this section is about. Okay. This is much longer than the previous section. But first question is where to run your analysis? And we saw in the training network that it gave you some options there for where to run the tutorials. But it turns out there are a lot more. So I'm going to click on that. Go to the Galaxy platform directory. And let's zoom in on that. One more. Yeah. Okay. So as I mentioned, it's open source software installed in hundreds, not thousands of places. If it's publicly accessible, we list it in this directory. Okay. And right now I'm going to focus on two of these. So we classify the public servers according to if they're use galaxy.star servers or public, or they're available on clouds or containers or VMs. Right now, let's focus on these two, use Galaxy and public servers. This is the use galaxy.star alliance. And these three servers are great places to start. So if you don't know where to go, start with one of these, you won't go wrong. They are all backed by large groups in each of the three continents. This one's based in North America, Europe and Australia. They have lots of compute resources, lots of storage. I think they all have storage limits of 250 gigabytes per count. So there are limits, but they're pretty robust. Okay. Next thing is public servers. This lists every public server you can just go to on the web and start using. Okay. This includes the use galaxy.star servers. And there are currently 134 of these. Okay. And they run the whole range. So there are some that are very specific for very particular problems. So this is about genomic variations. This is for marine science, although it's pretty generic. If you see genomics over here, it means it will have a lot of tools that you can use on it. This one is about age-seq, a particular method. Okay. So if you're doing age-seq, that's a great one to go. If you're doing something pretty generic like RNA-seq and differential gene expression, there's going to be a lot of choices for that. If you're doing something with antibiotic resistance, ARGS OAP is hugely popular. There's also RGA. There are a couple others. Areas is about public health. Turns out we actually have a lot of presence in public health. I didn't realize that until recently. But you can go through a lot of these if you're doing something very specific, like molecular dynamics and free energies, you probably want to use bridge. Okay. But something more generic, you're going to have a lot of choices. You can search here as well for what's going on. If I search for molecular, there we go, molecular. I've got this, that chemical toolbox. So if I'm doing something like this, I may actually have a couple of options. Okay. So that's the public server directory. A couple of things. If I scroll down, which platform type to use, and then it shows us our six categories, use Galaxy Public Servers plus these. Right now, let's focus on these two. And in particular, I want to focus on this one. And we will come back to this. If you have absolute data security requirements, meaning maybe you have data with personally identifiable information in it. So it's about people. You should probably not upload that to our public servers. And in fact, there's a warning at the bottom of all the used galaxies anyway that says don't do clinical here. Okay. So they are as secure as we can make them. But still, you don't want to upload that data to a public resource. So if this describes you, we will come back to this and it will be over here in this area, the local, the commercial and the academic class. Okay. So that's the first thing is where can you run it? Okay. And again, lots of choices at all at that URL, the platform directly, galaxyproject.org slash use. Okay. When you did the intro, you will have learned the basics, but each of those has more to it than you will learn in the intro. And I really want to recommend these three tutorials. So all these three link out to tutorials in GTN. Yes, so getting data in a galaxy, it turns out there's a lot of ways you can use FTP. And as you scale up what you're doing, you're going to want to know the other ways to do it. Because what works well for doing two or three or five datasets is not going to be so good for doing 10 or 50. Okay. So getting data in a galaxy, understanding the history system becomes important as you get more than one history. And as your history start to get longer. So history is an analysis that you've done or are doing. And again, when you start out, you can just do it. But as you go and you get longer, you're going to want to know the tips and tricks. So how galaxy supports bigger histories and more of them. Finally, you also want to know downloading and deleting data in galaxy. So you can get your results out, downloading and deleting because you may want to because you may run out of your allocation. So these are very important as well. So something about this slide is I'm not going to visit any three of these tutorials today. They're just links. And as we go, I'm going to do more and more of that just have links that point you to things instead of exploring them. And that's because of time. So, okay. So that's that now. If you've done all that, then you'll have a pretty good idea of how to use galaxy in a way that will let you scale up to do more histories, longer analysis. But you actually want to do it with a particular domain. And so let's follow this up. So if you have it, if you are using a galaxy server that is on a relatively recent version, it's going to have this. So let's do that. Let's take a look at use galaxy.org, which is the use galaxy North America. And I'm going to zoom in a little one, two, three, let's do four. Okay. So this is use galaxy.org. And it's sure enough, it has this. So I'm going to argue that when you are trying to learn how to do your domain analysis, I've got epigenetics I'm trying to do for transcriptomics or you name it any number of topics that the galaxy training network, which I'm going to bring up over and over today is a great place to start. And you can get to it how we got there before, which is training.galaxyproject.org, or you can also, excuse me, get to it from this. And there are reasons for doing this. And let's, you know, let's actually highlight that. So you're on your server and you see this and you click on that. And it doesn't take you there necessarily as it so much as it presents a window within a window here. And so this is the same website we were looking at before. And let's see, there's all sorts of different areas here. So computational chemistry on bridge, I think there's a tutorial there for bridge climate. This is a really valuable point about galaxy that I should make is galaxy was created for biology for life sciences, but it's actually a domain agnostic platform. And it's been applied to domains outside of biology as well, including climate. So computational chemistry sort of related to biology, you know, at least more so than climate. But a whole bunch of things here that you can do tutorials about genome annotation, if you're doing metagenomics, I recommend taking a look at these tutorials and say, okay, introduction of metagenomics, how to do 16 s stuff in here about mother, okay, good, things like that. So if you are doing a domain question, and we have tutorials, I recommend looking at them if you're working in proteomics, take a look at that, you may learn there are tools and resources you didn't know about. But let's come back to, yeah, so we got to this by clicking on this. I'm going to go into introduction, same place we were before. This is the same tutorial we were in before. Okay, short introduction to galaxy by Anna, again, and I scroll down. And maybe I'm running this one. Okay, although it has nothing to do with domains. And I get down here and I get to the fast QC. Now you may or may not recall before, when we were here, that this was not a link. And now it is. And what this means is that since, well, since I invoked the GTN site from within a galaxy server, the GTN site knows that. And it translates these toolboxes from just being tool text boxes into links. And so here it says what you want to do is type fast you see in the tools box, and you want to do this, that and the other thing. And then you want to click execute. Since I came here from here, I can now do this. I'm going to click on that. It takes me back to the server that invoked it. And it takes me to the fast QC tool. Okay, and maybe I forgot what it told me. So I go here. And I go, okay, I want to do okay, good. And so I do that. And then I type that in, you know, whatever it told me to do. Okay. So the GTN is integrated with galaxy. And it's not to a particular galaxy. Here I'm using usegalaxy.org. But it knows which, which platform called it. Okay. So really recommend this. If you're learning, it's a much less frustrating way to do things. And it can take you back and forth. Okay. Okay. So let's see. I recommend the GTN for learning how to use galaxy and for at least looking at some, some domain specific training. So epigenetics, transcriptomics, whatever you're doing, take a look, it'll give you an idea of how to do it in galaxy. Okay. It may be that if you're already, excuse me, it may be that if you are, are already an expert on your domain, you don't need to do that. You already know what the tools are. You're just using galaxy for its reproducibility and storage capability. Okay. That's fine too. Okay. So I did that. Okay. So get your domain knowledge from the training network. No, you can get your domain knowledge, get your knowledge about that domain and galaxy from the training network is what I should say. Okay. Next thing in learning to walk, there are a whole bunch of other resources that, that can help you get your question answered to run your analysis. And that's what this is about. So something I want to talk about is tools. That's what you do in galaxy is you run tools. Let's see. And this is fast. Yeah, let's do fast QC. Where am I right there? Okay. So you run tools and you find it by, you know, yeah, you find it over here, fast QC, which is going to be in the short reads data quality show sections. There we go. Quality control, fast QC. It's right there. Click on that. So in galaxy, you're going to run a lot of tools and you may have questions about those tools. I don't know what the contaminant list is. Okay, well, maybe you don't. There's some help right here. Okay, usually tells you, okay, what it's expecting. That's cool. Okay, what if there's not or what if that's not enough information, you can scroll down and let's see here, there's documentation about each tool at the bottom of everybody's tool panel. And it says, okay, here's what we're about. Here's where we are described some inputs and outputs. Okay, so you may be able to answer your question by scrolling down to the bottom. Finally, if that's not enough, there's often a link out to documentation. And here there is. Okay. And if you actually want to understand the that's not there. Okay, if you actually want to understand what the box plot that comes out of fast QC means, this is actually great this documentation. And so if you follow this out, you can find out, okay, what does that box plot mean? Fast QC has very good documentation. So this is highly recommended. Sometimes that doesn't exist. Okay, and what happens if you can't find that? Let's talk about that next. Oops, wrong one. There we go. So yeah, go to the bottom. Okay. So most tools like fast QC, they weren't written specifically for Galaxy. They are general purpose tools. They're available outside of Galaxy. That means that they have help information typically on the web. That's outside of Galaxy as well. So if you have a question about a tool, things like Google and Duck Duck Go are your friend and really search the web. There's also two websites I want to highlight in particular the Stack Exchange Bioinformatics site and Biostar because they are question and answer forums online where you can ask questions about bioinformatics. Okay. So if you can't find information about your tool that you have a question on through Google, you can ask it there or there. Okay. What if you aren't sure if your question is about a tool or if it's about Galaxy itself or you're pretty sure it's about Galaxy itself? Then you have a whole bunch of options. And let's explore those. So pan galactic search here. Let's go there. So maybe you have a question about Galaxy. This is the, excuse me, the Galaxy Community Hub at galaxyproject.org. This is at the top of every page. And I'm going to search for my, what happened to my history? Or my history disappeared? I don't know. My history disappeared. So maybe your history, that was good. Okay. So maybe your whole history has disappeared and like, I don't know why. And so you come here and you type this in. And what this does is this uses Google, but it uses a custom Google search to search only sites about our Galaxy. You're not going to get results about football teams or about cars or chocolate bars or astronomy or phones. You're going to get results about the Galaxy Bioinformatics platform. And so here we go. It came out pretty good hits here. This is on BioStar, which we used to use. So that one's pretty old. My data has disappeared. History completely disappeared. Okay. That's probably what we want. Okay. So our second hit is good. Missing history. Okay. Support missing history. Okay. So let's go to this one because I think that's our next item. So again, you go here, you type in your question. And then we go there. Okay. This is the Galaxy Help site. And it's an online forum for asking questions and getting answers. And here someone has said, history completely disappeared from local Galaxy after relaunching the instance. Okay. This may not be exactly what my question was because I was probably on a public server, but there's actually a lot of stuff here. Zoom in. Okay. And I could search here for history. I don't know if that works. History and data disappear. Maybe it's here. Okay. But there's actually an awful lot of help here. And you can ask questions here or you can find out who else has asked questions. And if no one has asked your question before, you can sign up and post your question. Okay. So that's a really great resource. So those are the first two, I think. Yep. Angolactic Search. Start there. Helpgalaxyproject.org to help website. We were just at. There's also Gitter Chat. Okay. So if we come back, where's it right here? Okay. Nope. That's not it. Okay. Again, back on the hub, Galaxy Community Hub, every page has this on it. And you can click on that and it will take you to Gitter. But a different Gitter channel than you get to in the Galaxy Training Network. This is a general Galaxy help. Okay. And you can ask questions there. Okay. So there's Chat as well. Galaxy Fact Page. That's an excellent resource. Let's go there. This is maintained by Jen Jackson, who leads our support. And using Galaxy Basic Facts and scroll down here. Unexpected results. My history is missing. There we go. It shows up again. Good news. It probably isn't. So that's a page that directly addresses my question. Okay. So lots of great resources there. Videos. Let's talk about videos. Galaxy has a lot of them. And we have a YouTube channel that manages the recent ones. And there's two I want to highlight in particular for this. And one is this collection. Get started. Galactic introductions is a whole bunch of videos. It didn't help. Okay. It's a whole bunch of videos. It looks like 16 videos. And I think they're all very short. And they talk about various... Stop. Okay. We're going to talk about various aspects of Galaxy. So what is Galaxy? How to get your data in uploading by FTP. Very helpful short videos. Okay. Quick tips are longer than these. But again, same sort of thing. Lots and lots of helpful stuff. The other thing I want to highlight is training. So if you go to training, we have lots and lots of videos about training. Now, these are different or distinct from the ones we saw within the Galaxy training network. These were created for training events. Like the one that's going on right now, plant transcriptomic analysis using Galaxy. And their videos created for training events. And there are lots and lots and lots of these. Okay. And so if your topic of interest has been presented in the last year, there's going to be a video here about it. And it covers the whole thing from intro to Galaxy to machine learning with Galaxy. How to do all sorts of things. So these are really valuable resources. You can play them. It's just like being in the training. Can't recommend those enough. So, okay. Got both of those. Okay. Other community resources. The Galaxy event horizon. This is a webpage that lists all upcoming events that we know about. There's a few things I want to highlight. One is the 2021 Galaxy Community Conference being held online. It was going to be in Ghent, Belgium, which is lovely, but we won't be there. We'll be online. 28th of June through the 10th of July, this starts with a week of training on the 28th of June. And then we have a three-day meeting this week and a two-day collaboration fest. And it's really affordable. Really, really affordable. So if you want to learn a whole bunch more about data intensive research and how to do it with Galaxy, this is the place to be. Another webpage is the webinars. It'll list this one plus the upcoming three plus a couple more. There's another one this week by a group in Norway that set up their own Galaxy server for using machine learning and immunology. So that's, I think, tomorrow. There's a news page, including a monthly newsletter. It lists all sorts of upcoming stuff in the Galaxy. We have blog posts from all over the web that we collect. We have a communities page that lists things like Galaxy Australia. It does not yet list things like the public health community. I will get to that hopefully in this month. We also have mailing lists. These are not as popular as they used to be. We've moved to forums and chat. But we still use them. We have an announced mailing list that the monthly newsletters go to. And there's a couple other lists as well. We're also on Twitter. And Galaxy is really on Twitter. We have a lot of accounts. Everything uses the hashtag use Galaxy. And there's a link to those postings. So if you want to keep up, I recommend that. At least following this hashtag, maybe following this or this and or this or the UK fee. There's a whole bunch of ones. The last one is the Galaxy community hub. I am actually going to go there. Let's go there. Okay. And this is the hub for the community. There's lots and lots of resources here. If you scroll down on the landing page, there's links out to everything I just talked about. The news feed, the newsletters, the events page, the Twitter videos, blogs, careers. There's a lot of demand for people who understand Galaxy. So we have a careers page as well. And then platform news as well as some recent hubs. If you go up here, community events, news blogs, same sort of thing. Okay. So lots and lots of stuff is there. Okay. How are we doing? Okay. Let's do that. Present. Okay. Going the wrong place. Okay. So that's it for learning how to walk. So we went through learning to crawl the very basics concepts. What's the history to learning to walk, which is how to do your domain, how to do more sophisticated histories, more sophisticated analyses, how to find out what's going on, community resources. But now our last topic is learning to run, becoming a Galaxy Power user. So as you scale up what you're doing, you're going to need the features that were introduced by the stuff in learning to walk. But if you start to get bigger, if you start doing stuff with 100 data sets, for instance, you know, maybe paired in reads times 50, and maybe 10 individuals over five time points. Okay. Those methods are still going to work, but they're going to become tedious unless you start using the Galaxy features that support large experiments. So that's what this section is about. Okay. How to really scale Galaxy up to support large experiments. Okay. Which it does, but not through the ways you learned in the first two parts. Okay. So the first thing I want to talk about is what if I want to rerun my same analysis over and over? Maybe you've got an experiment and you get new data every Monday morning. And you don't want to have to type in and click and point and select the same thing every Monday morning. What you want to do then is use workflows. Okay. And workflows are repeatable recipes in Galaxy. And what that means is instead of redoing everything Monday morning, on Monday morning, you upload your data. You say run this workflow I've saved, you tell your workflow where your data is, and then you hit run workflow. And then it goes off and it reruns your analysis on new data every Monday morning. So that's workflows. There are two tutorials that cover workflows in the basics. Extracting workflows from a history tutorial. So you can actually create an analysis and then say from the analysis I just ran, please create a repeatable recipe or workflow. That's what that's about. And this also talks about the same thing. Okay. But workflows are way more than this. They have a whole bunch of features. This is a video from a webinar maybe a month ago, maybe a little over a month ago. And it is a tour de force of workflow features. And if you're repeating your work, you should absolutely watch this because it talks about all sorts of cool ways to use workflows, workflow features for how you change parameters each time you use it, checking for best practices. It's an amazing video. So really can't recommend that enough. Do you want to learn more about creating and editing and importing Galaxy workflows? I recommend this tutorial. It turns out you can create them from scratch as well. And then once they're created, you can edit them and change them. Important Galaxy workflows, you can create a workflow on one server and then import it into another. And as I mentioned, they're also parameters and there's a tutorial about that. So really, if you're doing something more than once, absolutely you have to look into workflows. Okay. What if you are starting to do complex slash large experiments? Okay. So, you know, the methods you've learned already might scale, well, certainly scale to five, maybe 10 data sets. Not going to scale to 100. What do you do if you have 100 data sets you're operating on and you actually have a complex experiment, like 10 individuals at five different time points? Okay. In this case, you're going to want to spend some time learning the rule-based uploader, the collections and tags. Okay. And here are resources for learning all of those things. Briefly, rule-based uploader says when you bring data into Galaxy, you can actually use the information in the files and the names of the files to pre-group them into collections. And what are collections? Collections are semantic relationships between files. So in the intro and even some of the medium tutorials, you may not be using collections. Every data set is an individual data set that works fine when you have five or 10. When you have 100, it's going to drive you crazy unless you start using collections. And it goes everywhere from the bare minimum, which is I have a paired end data set, here's the forward, here's the reverse reads, put them together in a collection. Two, I have a collection of 10 individuals over five time points. Each of those have two files. So it can really be arbitrarily complex structures and you can create these semantic relationships. So Galaxy then knows that those files are related. You can then run tools on collections. So you don't have to run a tool 50 times. If you have 50 pairs of forward and reverse reads, you can run a tool once and say, please run it on this collection. Behind the scenes, it will run it 50 times. But in the interface, it doesn't have to do that. Tags are a way to keep track of where things came from in the longer histories. So once you get past 10 or 20 steps, it's really hard to keep track of where things came from. I really recommend using tags once, well, once you get past 10 steps. So really spend some time learning these things. It will make your experience much more efficient and rewarding. So what if you are a programmer, you're a bioinformatician and you don't want to use that graphical user interface, it drives you crazy to do all the appointment clicking, you know how to script things, you know how to do things efficiently. It turns out that you can also access Galaxy programmatically through Python. There's a BioBlend library, that's what we call it, documentation. And it talks about how to actually access Galaxy. So if we go back to our Monday morning example, instead of you manually starting that workflow every Monday morning, you could have a script that runs every Monday morning, checks the directory, if the data is there, it then invokes Galaxy, logs in, transfers the data from that directory into Galaxy, and then launches the workflow. And so you don't even have to do anything every Monday morning, it's just all automatically set up. And what you get for that compared to just using your own scripts is you get everything that Galaxy offers. You get the user verification, the authentication, you get its sharing capabilities, and you get its reproducibility. Okay. So there's also a PHP library for doing this, this is used by triple, if you're familiar with that. So what if you need more power? How am I doing for time? Okay, what if you need just more compute power, more storage than you can get on any of these public resources? So you remember this from early in the talk, we talked about these two. You can use academic or commercial clouds, or you can do a local installation. So let's talk about some of these. This is the academic clouds tab that you'll see on the platform directory. And let's see, what is it good? It's okay. Yeah. So it turns out that Galaxy has been ported, or there are ready to run Galaxy instances on a lot of academic clouds. And this is, excuse me, this just shows the first five, but it's a pretty good sampling. So let's talk about them. Let's start with Climb and GenAP and Jetstream. These are all national cloud infrastructures. This is in the UK, this is in Canada, this is in the US. There are also infrastructures in lots of other countries like Italy and Norway, Czech Republic, you name it. A lot of places have these and they have ready to run. You can launch your very own Galaxy instances on the academic cloud platforms in those countries. So that's what these three are. Anvil, I'm really glad this isn't the top. So this is funded by NHGRI or NIH in the US. And it is a FISMA moderate environment. So it's not CLIA certified or HIPAA certified, those are terms used in the US for clinical stuff. But it is a secure environment. And so you can actually launch your own Galaxy in Anvil and it runs in a FISMA moderate environment. So it's not quite clinical, but it's a step in the right direction. And this one actually integrates with a whole bunch of controlled access data sets at NIH. And it's kind of cool. So the advantage of these cloud instances is you can often scale them up arbitrarily. And that is certainly true of the commercial clouds. But you need to get an allocation. You can get an allocation from Jetstream or GenAP or Anvil or Climb. And then you can spin up your instances. You can run your analyses. You can export your data sets. And then you can shut it down so other people can use those resources. It's a very cool thing. What if that doesn't work for you? You can set up your own Galaxy instance. Why might you want to do this? Security is a big one. Okay. Oops. That's not what I wanted. Security is a big one because then you can put it behind your firewall. Galaxy is used in lots of research hospitals. And it's behind their firewalls. You get to set your compute and storage limits by what you have available. I don't get to set it. You get to. You can also customize your server. So you get to control the tools and a whole bunch of other things that are on that server. But the resources for doing this are outside the scope of this webinar. So please join us on May 26th for I think that's the last one in the series, which is Galaxy Resources for Administrators and Infrastructure Providers. Okay. How am I doing? Oh, okay. I went over one minute from what I wanted to. Thank you. Thank you for listening for the past hour. I really want to thank the Galaxy community. And this is literally thousands of people who have contributed tools, documentation, support, training, resources, code, testing. You name it over the past 15 years. And NHGRI, NIH, and NSF are the three U.S. agencies that have funded me personally over recent years. So I'd like to thank them and you guys for sitting through this and questions and answers now, please. Okay. Thanks to you also, Dave. Yeah. I hope our audience found the talk useful today. There are a couple of questions and we have like eight minutes or so. So I will read them for you. Tatiana asked if you can use usegalaxy.org.au if you are in the U.S. Yeah. Okay. I just stopped sharing. Good. Okay. I thought maybe I stopped recording. Yes. So all of the usegalaxy.stars, at least those three, are accessible to anyone in the world. So, yes, you can use those. Some infrastructures, so usegalaxy.no for Norway is only accessible to Norwegian researchers. But if it's listed in the usegalaxy tab, then anybody can use them. Okay. And we encourage you to do that. Okay. Thanks. There is another one that is somehow related to this. Can you have accounts in multiple servers or move your analysis from one server to another? Yes. You can have multiple accounts in, excuse me, in different servers and that is not considered abuse. Okay. So it's possible to have an account on all 123 of those public servers, 134 of those public servers. And they each are independent. So they don't know about each other. So if you create an account in one, it has no impact on another server. Now, that's good and bad. Okay. It's mostly bad because what we want to have is a federation of galaxy servers where if you run something on this server, that's great. But it doesn't have this tool that you want. It's over on this server. Long-term, our hope is to provide a federation of galaxy servers so that you can move seamlessly from one to the other. We don't have that yet. And I don't think that's anywhere close to funded yet. But that's our goal is to have a federated galaxy where you can just move around. As far as moving analyses from one server to another, yes, you can. You can export either a dataset or a complete history from any given instance. Okay. You can also export a workflow from one instance and then load it into another. Okay. So, yeah, you can move things around. I have never tried importing a history. So I don't know if that works or not. But I know you can export it. So you can actually get all of your datasets downloaded locally to your laptop. And you can do that with workflows. So does anybody know if you can upload a history? Yes. Yes, you can. Okay, good. Never done that. Okay. Okay. That is another question, I think, that it's just new. Can we run NFCore pipelines? Oh, man. Okay. And I'm not the one to answer that because I don't know squat about NFCore besides that is next flow. I can tell you that there is limit support for CWL in Galaxy. And I think next flow supports CWL. So a very tentative answer here. If you can get it into CWL, then I'm pretty sure you can get it into Galaxy and it might actually work. I know this is an active area of research to better support CWL in Galaxy. So not the most solid answer. Just another one now. Where can I find more information to troubleshoot a Galaxy 2 that is not working as expected? Okay. So if it's a specific tool and you're trying to use it, I would go back to the resources I pointed out. Think about using Google because it probably was not written for Galaxy. It's probably a generic tool, probably used by researchers around the world. And they may have a help forum about that. They may have a mailing list. They may have, you know, great documentation. They may not. Some tools have terrible documentation. But start there. Okay. If that doesn't bring you joy, there's the Biostart forum, there's Stack Exchange, the Bioinformatics Stack Exchange. If that doesn't bring you joy, you can go to the Galaxy Help Forum and ask the question as well. And again, sometimes it's not clear if it's a tool question or a Galaxy question. If that's, you know, like, where's the problem here? Is it that I'm not understanding the tool or is it somehow and how it's interfacing with Galaxy? In which case you want to start with the Galaxy Help Forum or the chat. And it's okay to start there too, if you're just not sure. Because it's a very friendly community. I feel like, I don't know, does that answer your question? Does that help at all? No. There is a reply to that. Try to Google and Galaxy Help Forum, but do not bring me joy. Okay. Then you need to ask the question. Okay. So formulate your question. If it's really short, you can do it on chat. If it takes more, then you want to do it in help. And just ask the question. And worst case, someone else has already asked it and someone will say, hey, take a look at this answer. But if you couldn't find it, probably it hasn't been asked. In that case, it's the best case because now we're going to have that question. Everybody can find it and everybody will be able to see the answers to that, the responses. Okay. Hopefully that will bring you joy. There is no reply. So let's assume this is joy. Another question. Is there enough tools to perform single cell RNA-seq analysis in Galaxy? Yes. I don't do single cell research, but there are whole servers that apply for single cell. The European group has a single cell server and the human cell atlas has a lot of single cell stuff as well. And they have a Galaxy instance. But in general, let's see, the usegalaxy.orgs will also have a lot of single cell tools. And I recommend starting with the GTN tutorials about single cell, because there are a lot of those as well. So two years ago, we were not so good with single cell, but now we're all over, in my opinion. Okay. Thanks, Dave. I don't see more questions, but I may have one for you. So you've mentioned different scientific areas and showed them in the GTN at all. So if you are a researcher and the tool that you are using usually is not available in Galaxy, how do you proceed there? Let's see. So if you want to use it in Galaxy, someone is going to need to wrap it. And wrapping a tool is the topic of not the next webinar, but the webinar after that, I believe. So in three weeks time, three weeks, yeah, in three weeks time, that's the topic of the next webinar is how to get tools into Galaxy, how to make your tools accessible through Galaxy. But if you're not the kind of person who knows XML or knows Python, then what do you do if you have a tool that you want imported into Galaxy and it's not currently supported? Well, a couple of things. First, you can check in the Galaxy tool shed. If you Google that, it will actually find our tool shed. That has 8,000 tools in it. So there have been a lot of tools that have already been wrapped for Galaxy. It may be possible then for you to write to a system admin on whatever server you use and say, hey, can I get this tool installed? I was looking for Diff the other day on an Anvil instance, and it wasn't there, but I went to the Galaxy tool shed and it was there. Since it's Anvil, it's a cloud instance, I'm the admin, I can customize it. So I installed Diff on my server. So that's one thing you can do is search the Galaxy tool shed. If it's there, you can ask your admin. If you're using a cloud instance, you can install it yourself because you control that. If it's not there, you have to find someone to wrap it or suggest that it be wrapped. Then it becomes a marketing issue where you might want to put it on the forum like, hey, I really want tool Diff. And if no one's wrapped it yet, then it's like, okay, you can also write to the IUC channel, the chat channel and say, I want this. The IUC stands for the Intergalactic Utilities Commission, which is the group that manages tools overall. So they have a good or chat channel, you can write to them and say, it'd be really helpful to have X. Long answer. Yeah, but a good one. Thank you. Okay, I don't think we have more questions and we are at the top of the hour. Next week, we have Saskia Hilton and colleagues, leading the educators and trainers. So if you are interested, I will share the link now in the chat. And you can register like you did for this one. It's at the same time. And I think that's it for today, Dave. Do you want to add something else? Thank you all. So thanks for your time. Thanks for joining. I hope that was useful. See you next week. Bye-bye.