So, like I was saying, collaborate with the Beijing project VGP in on the second version of the pipeline, based on back by your reads high file. You can see on the left we have three separate track for the initial assembly using high file rates, depending on what data are available. You have a regular mode using only back value high file you have high C mode using the high data for the initial phasing of the assembly. And you have a trio mode, because for some project we have data from the parents of the sequence individual, the data for the parents are in the data. In every case, the first workflow is camera profiling workflow using genomes cop and Maryl to evaluate. So the camera provide the size of the genome, the percentage of the resiliency and different parameters that are going to be used in downstream analysis. The next step is high phasing using a phasing to assemble the genome. So, in that case, we have simple as simple as phasing using I see all three of us. This workflow include three tools for the quality control, including mercury busco and Jeff starts Jeff starts provide simple statistics about the gym assembly such as and this kind of data busco evaluate the presence of auto loans and the completeness of the genome and mercury evaluate the level of duplication among other things, depending on the results of mercury. The first one after the tracks can be used to be getting prototypes. It's not as well because a lot of project don't have necessarily the data. And the third step is using salsa for scaffolding with high C data. You can use pretext and Jeff starts pretext provide a map of contact with I see to evaluate the completeness of scaffold. This pipeline has been available six months ago, it has been used on 21 genome for six months, 10 birds to amphibians to fish, six numbers and one repel. There's four more that are currently being assembled. The largest genome. This is the fall. Yeah, base for the eastern. And it has been assembled using the high C. Hi-Fi is unfazed as something. We also have the what I be at the Hawaiian picture here. We also have the zebra finish, which is the weapon genome, and we generated three new assemblies for this. But which has a high level of generosity. So we have the three different but high-fives and high-fives and high-fives and trio. And without surprise here, like the high-fives and trio provide the most complete the highest level of facing. The full pipeline, we have a nearly free face chromosomes. You can see on the right, the contacts map with pretext before on the top and after on the bottom, the highest scaffolding. We also have the water. And we evaluated it on this, on 19 of these genome and you compare it with the combination, the contamination by hand. So we remove contaminants and mitochondrial genome. 12 of them provide exactly the same result that you get with minor contamination. And among 321 scaffold that has been identified as contaminants. We have four positive and six negative, although among the first positive, some evidence later indicate that they might be actually positive. But overall, it's very similar results from the minor decommitamination. So this is the 2.1 version of the workflow. While this is being used to assemble genome currently we're also working on the 2.2 version, including among other things. We also have an HGP based workflow, including HGP FASTA reconciliation to keep to present the graph information that are provided by the initial high-fives analysis until the end of the scaffolds. We're also implementing the yes tool yet another high-scene scaffolding tool. We also have the SASA for the last day of scaffolding, and we are working on multi-tucie add-on to merge the quality control over the whole analysis for final unique reports. So in addition to implementing the pipeline in the galaxy, this project allends to a specific development in the galaxy aiming to big workflow and big analysis. So one of these things is the AWS data retrieval, you can now access the genome report data from Genome Arch, AWS repository, including the genomic data and the completed assemblies from the data output directly into galaxy. The development has been an export tool and workflow. So it's a scary workflow, but the larger works on the extreme right is a tool that takes into inputs, destination, distance files, so in that case it's dedicated to the export back to the AWS repo for VGP. It takes a full path including folder and subfolders so you can build an architecture to send your file to, and that is why the AWS scary workflow is creating the right path for the right for the output of the analysis function. And since the history are gigantic, we have dozens of inputs and dozens of data set in the history. So if we have a filtering like tag, you can see on the bottom right to have a tag filter scaffolded S1 in this example, and this tag are applied during the analysis workflow, and we select as inputs of the workflow, the file, the data sets having this tag. So this is a lot of new developments. The filter by a tag, when you run the workflow, you're going to have in the inputs available for the workflow only the file type in this one. So that way when the people running the VGP analysis want to export everything is already pre-filled, they don't have to manually scroll for all the data sets in the history. So all of this workflow available in IWC. Some of them are already merged in the main repository. Other ones are still in to request while we are working out last test testing, but you can download it from IWC whatever you want. And then the tool are available in .org.en.eu, but there's an assembly.useGalaxy.eu that is dedicated to the VGP program, including presentation with all the workflow available. So thanks for the workflow in ..en.org and running there. Still working progress with Galaxy Australia to get all the tools up and running. Thank you for giving me the opportunity to talk today and also thanks all the people who collaborated with this project in the VGP team and the Galaxy team and people who didn't participate directly in the project but who participates in the development that makes all this possible. If you want to know more about the pipeline we have developed two tutorials that are available in the GTN assembly section. So the question is if the data type has been decided, if there's a reason for choosing this data type, including Nanopore long reads, and the answer is, so this is the data type that has been financed through the VGP project, but there's also in the work of the initial assembly that is really early in the work, but eventually we want to discuss Nanopores as well. For example, in COVID project we have this ability to trigger tutorials. So the question is, for example, in the COVID project there's a possibility to trigger the workflow through API instead of through the interface. And it's been discussed, it's a role we have to be able to run it on common back with the interface, but we haven't started working on it yet, but it's going to be eventually. So this is starting from, sorry, the question is what is the end point of this project if it's going to be available in several other places. And so this is the phase one of the project, the goal is to have all the assembly in the reference to in the end of various people. And the second phase of the VGP project is to build annotation and run analysis. So eventually the goal is to have this resource available completely. And once I send it automatically through the pipeline, the genome also sent to for operation. And so eventually the goal is to have it available everywhere. Awesome. Thank you very much. So our next speaker. Our next speaker is Timothy driven. Great, I will get right to it. Thank you very much. So, Hi everyone, I'm here to present some work from our team. This is work here at the University of Minnesota so a short walk over the river is where this is going on campus. Very much team effort, people from the super computing Institute as well as my laboratory and biochemistry and also some collaborators from Chicago and the Lurie's Children's Hospital there. And we're working on cystic fibrosis analysis using proteomics and the galaxy tool so real quickly just kind of the background so the overall goal of this project is cystic fibrosis obviously a pretty well known disease but there's a lot of questions about the bacterial contributions to it, whether it's a consequence or or actually paste some sort of a positive role in in some of the negative clinical aspects of cystic fibrosis. So we're working with a team that has access to these bronchial lab bronchial angular lavage fluid samples Belf. It's a sample that they can use a special scope into the lungs and basically flush cells and other molecules off of the lungs and bring those out. So we're analyzing those samples using mass spectrometry to try to identify proteins that are coming from bacteria that may give us a clue as potentially even their functional signatures but also as a marker and sort of a quantitative way to to to assay the bacteria as well as the host proteins that that are a part of these samples. So that's kind of the long and short of it using mass spectrometry and also galaxy to analyze this data. So this is kind of the workflow I'm not going to go into every piece of this other than that we have access to cystic fibrosis samples as well as disease controls which are samples that are other lung conditions that are not cystic fibrosis as a control. A couple key aspects that are shown in this slide is that we're what we've done at the start here is taking these Belf samples and pool together different patient groups and analyze these trying to do a really deep dive with the mass spectrometer to understand what can we detect in terms of bacterial and human proteins. We also have some some cyst 16 s data that gives us at least an idea of what bacteria are there that's important because it gives us a sense of kind of the protein space the proteomes that we should be looking at in terms of these samples. And then we've taken them through a pretty rigorous bioinformatic analysis, mostly based in galaxy. This is just kind of another look at that it's really kind of these two phases that we've used. So, in this slide it kind of shows you the overall design so we have disease control cystic fibrosis we also have their microbial diversity, whether it's higher low based on that 16 s status so we kind of group these into these tools samples, push these through the mass spectrometer. It's a little dicey when you're doing what we call metaproteomics because now when we have even with 16 s RNA, lots of proteomes we get these huge databases. We use a tool called metanovo that gives us for sort of a first pass with the mass spec data to say okay here. Here's the organisms that we think are actually present based on the initial signatures of the mass spec data so it kind of reduces down our database. And then kind of taken, I guess, this sort of kitchen sink approach of using different tools to analyze though that reduced database to understand what proteins are there given that these tools have some complimentary identifications. So this is just showing the proteins and the peptides and in blue shows those proteins and peptides and match the bacterial proteins the rest of these are human so this is going to surprise these are primarily human proteins were detecting. But we're trying to tease out those bacterial proteins as well. And from there, we then take these bacterial proteins and take them through a pretty rigorous validation step. We use a tool called pep query to really try to make sure are these really truly confident and real matches bacteria and not human protein so there's a pretty rigorous step there that happens and Catherine Doe is the one who's really put together these figures have done a lot of this this part of this analysis. And also we have what we think are sort of our peptides from from microbial proteins of interest. We can then do some analysis. This is from this unique tool that shows sort of the phylogenetic tree so to speak of where these proteins are coming from. This is a highlighted pseudomonas this is a very well known pathogen within cystic fibrosis so we're definitely seeing some things that are making sense but there's also a sort of profile here of other things as well that are that are interest. And from there, we're then taking these and we've done a lot of manual as well as looking at quantitative response of some of these bacterial peptides. So we come up with this sort of panel of 87 very stringently filtered peptides. This is showing in the mass spectrometer there's some ways to go back in and actually reconstruct the signal from this peptide so these nice colorful plots are sort of all the fragmentations from any given peptide that that that were used to detect that sequence and you can sort of build those back up to confirm that this is a real sequence and also quantify this this data using sort of this area under the curve. So we can finally see some some quantity quantities of these different peptides from different bacteria that that seemed to show some interesting possibilities, as well as more Excel is a very common upper aerial, aerial track bacteria so not a surprise to see that but not necessarily one that is CF CF associated so shows that that that's in these controls but not as much in the CF. And really where we're going to with this is not just the bacteria part of this but what we think is really kind of novel here is that we can in the same assay. But also at the human protein so I know this is kind of a busy slide but what we've done with the human proteins if we have a lot more data on is going in and try to do some some analysis on you know what are some of the sub pathways and functions that we see that are enriched, either in CF or in the in the disease controls at the human protein level. And where we're trying to go with this is now kind of construct this panel of peptides that we could now target, really create an assay that you could go back in the clinical samples and watch the dynamics of bacteria and human proteins of selected pathways, and how do they change under under certain disease conditions and things so we're almost there, we're trying to validate these and do some and develop these targeted research fingers cross will will kind of show some results in clinical samples so last little pieces we have a metaproteomics workshop today if you're interested in metaproteomics is heavily used here so come and check that out at 120 if you want. What I have maybe take a question or two. One question. Full scan acquisition or also targeted. Great question the question was is this a full scan acquisition or targeted so all of the data initially was untargeted just open ended proteomics so just trying to select everything we could and identify what's there. We're starting now and we're starting to follow this up with PRM so targeted so now we know our markers peptides let's see if we can detect those and confirm their real so hopefully it will all become targeted. Up next is a Dr Jeremy Gex from actually the same institution as me, which is uses my opportunity to give more time. Galaxy and she learning. Excellent. Well I'm looking forward to our time. So yeah, my name is Jeremy Gex I'm a faculty member already in health and science university and it's my pleasure to talk to you today about advances in machine learning for galaxy. And I have the pleasure of talking about work from several individuals across the community and I like them along the way. So the key points that I want to talk about today are kind of free for one I want to share an overview of machine learning working with Deming Galaxy from past to present some kind of jobs park across where we started and where we're going. So I want to highlight two different recent advances that we've done with respect to machine learning that would give you some insight into how we're thinking about this process. And how we think galaxy can contribute and make possible machine learning analyses. And then finally and with future directions for machine. So kind of level setting make sure we're on the same page. Many of you probably know machine learning is a subfield of artificial intelligence where you use data to understand patterns and identify different trends that are you can then use to predict outcomes in the future. Deep learning is a further subset of machine learning that happens to use these neural networks, and it's called deep because instead of only a couple layers of neurons you have many layers here. And you can see with this kind of example here up maybe is super high quality projector that as you move across the layers you get more and more advanced feature to touch so you start out with things like edges. Okay, so if we think about where machine learning is applicable. This is a slide that we put together a while back thinking about machine learning from my Madison thinking about the idea of the collected home you can collect data clinic you can apply machine learning models to many of these different data sets. But of course we know machine learning is useful not only in biomedicine but other places like ecology as well. I saw a really interesting study recently where somebody use machine learning to identify the different trees that were present inside. So we're looking at wide applications, we want to do so in galaxy. And so what we've done over the last couple years and we've developed a toolkit called galaxy ML. It took kind of the best practices and libraries they're already out there in the community, and stuck them into things you've probably heard and such as I can learn some other tools such as extra boost in balanced learning. If you're familiar with that where you have challenges of one class being underrepresented and we're classes being overrepresented and also some deep learning libraries as well. We wrap all these into galaxy to provide an end to end approach where you can take your data sets in galaxy do the training do the testing do the evaluation actually do the visualization as well. And when you take these machine learning to look at some stick them inside the galaxy framework, what I think you get is some very nice abilities to do things that are hard to do otherwise. In particular, you can do scalable and reproducible machine learning. So we take advantage of the scalability and reproducibility of the galaxy provides me combined with these machine learning tools. So here you see some heat maps were ran and created thousands of machine learning models and compared them to each other across many data sets to try to identify which models were most appropriate and best performing on different data sets. And then I'm sorry I made my rights and left. So right here a few maps, but on the far left you see examples and deep learning applications that we applied as well. And those curves are simply different architectures and different data sets that we went and mapped out predictions on. So we think that the synergy of these machine learning libraries coupled with galaxies powerful infrastructure is quite valuable. Okay, so that's what we've done. What's new. I want to talk about two things that are new with respect to machine learning galaxy. The first one comes from the University of Freiburg led by a new tomorrow, you know, and this is galaxy Jupiter lab for AI. The idea here is the galaxy can run interact tools. We'll hear more about this, hopefully as the conference goes on. The idea of these interactive tools is that you can spin up these kinds of sub web environments inside of galaxy. And of course quite powerful, but what's been done in this particular instance is they've taken this idea of an interact tool and they've loaded it up with lots of things that you want to be able to do when you do machine learning in the context of Jupiter lab. And Jupiter lab is of course this online or interactive notebook where you can type in your type code for instance executed in real time. So in making this interactive tool, they've put in core aspects of machine learning in particular deep learning approaches to make it possible to run these calculations on GPUs. Many pre installed packages once again so you don't have to spend your time installing things and getting it set up, get integration integration with different model formats, so that you can import and export models. One of the things that is pretty important, I'm going to go a little bit off script here is the idea that you can use models that are widely available in the community. So rather than building up your own models what you really want to be able to do oftentimes is take models that other people created and apply to your own data. Well this requires the ability to import these data sets of recognition of these different formats, and all of this is really nicely baked into Jupiter lab. This particular application. So a particular interest is something that we've seen already, where you start to connect these notebook environments back to galaxy and say well I'm in a notebook environment but I want to use some of the powers that galaxy office. And so here what you're seeing as an example where you can walk through and you can take your machine learning model and go back and use galaxy to train.  And then I want to train on my small doctor container where my notebook is running I want to use the power of Galaxy. So again we're starting to take these aspects of machine learning, take advantage of the framework the galaxy provides. If you want to learn more about galaxy Jupiter lab for AI, there are some links here and I'll make sure I tweet on my slide so you know to try to copy these links down. But there's tutorials there's a preprint on that and then finally there's a lot of you can actually try out there right now. So the second advance I want to talk about something that's called declarative deep learning. So declarative programming and it's probably familiar to many of you either because you've used it explicitly in the context of galaxy you've seen it elsewhere. It's the idea that what you want to do is not necessarily tell the computer how to do something, you just want to say what to do. The particular problem that this is trying to overcome when you think about declarative programming with respect to deep learning is that often difficult to build these models you have many many layers that you're trying to define and connect them together. And there's complex syntax involved. So this is a hard problem to solve, even in the context of galaxy where we provide nice rise, and there are you eyes for building up deep learning models and galaxy. The solution is to use a simpler approach where you take something like the animal once again or another format, and you declare what you want to do. So what you're seeing here on the left is a simple set of commands that say these are the features that I want you to encapsulate here in the outputs that I want you to encapsulate go off and build a model for me. There's a cool kid out there called Ludwig right now started out at Hoover but now it's hosted by Stanford, and it allows you to set up these YAML files and then build up your deep learning models. And so the key advantage here you can take, take full advantage of this framework is that it's minimal and it's simple code into that pretty complex models. We can talk and we can discuss about how much visibility we need to get inside these models to make sure that they're useful and make sure that they're doing what we want them to do. But as a first step they really lower the barrier in terms of taking these models and using them in your own research. So what we've done as you would expect since I mentioned Ludwig and we're here at the Galaxy conference to be taken Ludwig who has exposed its functionality inside Galaxy. In particular the Galaxy tools that we have mirrored the Ludwig concepts of encoders and combiners and decoders. This is a pretty simple concept that says if I want a bunch of input features I'm going to encode them into my network.  So we can do this using custom approaches or we can get these predefined models that I talked about before. So things like ResNet for image analysis is possible to do inside Ludwig with a similar line, and we can do visualizations on top of it. So Ludwig is quite good about saying you're going to run many iterations of your model. Here let me visualize them side by side so I can evaluate the performance. This is an example application I've done so far. Ki67 is a common biomarker in cancer in particular. You care about this because often this is a sign the tumor is growing rapidly or proliferating rapidly. And so in this particular application there was a data set by a public publication, where the goal was to predict Ki67 positive cells versus Ki67 negative cells. This is a common thing so you get a slide from a tumor and you say 20% of the cells are positive. Pathologists are pretty good at this. It would be nice if we could automate this to some degree. And so we took this example data set. You can see some examples of positive and negative cells right here. And we put it into Galaxy and we use Ludwig. And I'm showing you the rough configuration here where we have the input features. In this case just a set of images. We have some output features that we want to get out of it, in particular the label positive or negative, and we ran this through Galaxy. So this wasn't a lot of code to be able to bring this up pretty quickly and to reproduce something that was out there in the community. So this is a nice demonstration of power what we get to. And we got pretty active results as you can see that mirror what they found in the publication. So, looking forward now some of the future directions that we're looking for. Again I emphasize this point of being able to use what's out there in the community and build on top of it. So we're looking to model zoos as it turns out where these zoos are places where you hold these different models that people have created so we'd like to be able to integrate more tightly with these zoos that are out there. Ludwig is a good first step. Galaxy Jupiter lab for AI is a good first step but we can do that. And so we're looking at how to do that. Additional tools for visualizing understanding performance are always needed when it comes to machine learning. These models are complex. It's hard to understand sometimes what they're doing. And of course advanced that by introducing additional visualizations and understanding performance. We of course want to be able to push the state of the art forward in terms of scientific applications as well. So I've talked mostly about infrastructure today, but we of course we want to partner with our scientific collaborators and say, let's actually do new analysis in Galaxy with these machine learning tool kits. COVID and cancer are two examples that I know that are going on into the Galaxy community right now. And finally at more of the infrastructure level there's an interest and there's been a long standing interest in using machine learning to improve how Galaxy works and orchestrates its tools. So in particular when Galaxy chooses to run an analysis job, it has to figure out what resources are likely to be needed and then assign it to the proper queue. So what we're working on in terms of the core Galaxy community and team is to do this prediction of job resource needs based on historical data and then tie that back into Galaxy. So a really nice application of Galaxy that's not scientific, but will help Galaxy function much better. Okay, so the final thing I want to mention today is that there's a machine learning workshop tomorrow morning. Tomorrow afternoon at 120 let that cave on all. So I encourage you all if you're interested in machine learning and learning about what you can do right now with respect to Galaxy. You can stop in tomorrow and learn at that workshop. I had a pleasure today of talking about the galaxy Jupiter led for AI done by Kamar in New York Greening Penn State University cave on Kamali and antenna tanker we're doing a bunch of machine learning work there. And then an organ health and science university Chengdu and the sergeants have helped out tremendously with bringing up these tools as well. So I want to thank you all for funding and fortunate funding from NIH, the variety of places. So, thank you very much. I need to repeat this question for the people online right. So, very exciting question came up with our 2000 turkey pictures and the question is what are we going to do with those 2000 turkey pictures so what are you hoping to do with your processing and normalization tools that are built into this framework to do that type of analysis before you do the machine learning. So, oftentimes machine learning as we've talked about today is this concept of predicting different labels so maybe you want to predict a turkey that's going to grow very big or it's going to be very friendly for instance, and you could label your turkeys and then you can decide on that but if you need to do pre processing up front that's fine. There are also unsupervised machine learning tools in Galaxy as well where you could cluster your turkeys for instance, based on particular attributes, whether it's color size or something. That's a great question. Any other questions. Yes. I would say the models are okay so the question is our models use the same things model registry, then I don't quite understand the context of the second question which is the features that we were asking about. Sure. Okay, so let me address the first question is I think that's a pretty simple answer I would say the model registries are the same things models. They actually sound better so maybe I'll adopt that term in future talks. The second question was about pre processing features. And here it's a little bit. It depends on the model and the approach that you want to use. So, in traditional machine learning you would also often separate your pre processing from your model building for instance so you might normalize your data and scale it and then you would run it through say a logistic regression model. So you create a galaxy pipeline that would do these different steps and hand that off to someone so they can do that deep learning models tend oftentimes to do all of this internal in a black box so you just get machine deep learning model, it will do the feature pre processing along the way as well as the classification So I would say there's absolutely ways to do the feature engineering if you want to inside this toolkit, but it depends on your particular approach when you need one tool or when you need a more traditional workflow where there are multiple steps. Okay, great question. Yeah, I think that's all the time we have for questions right now so any more questions about how to image process turkeys please take them to slack I think we'll have a very interesting discussion there. Okay, next we have Andrew. Good morning. My name is Andrew Chesky. I'm a PhD candidate in the University of Minnesota and I want to talk to you a bit about the project I've been working on bottom up against fire comparing bottom up proteomics and a modified and degradation methodology for automated on targeted protein analysis in the galaxy platform. You and I are constantly being exposed to reactive chemicals. Every day of our lives. This cohort is called the expose on it comes from a number of different sources from exogenous, or in dinos, my products to specific exposures, occupational hazards and environmental pollutants. These are of interest because of their direct toxicity as well as their meter genetic and oncogenic potentialities. Because the expose on consists of highly reactive electric files they can readily form addicts on the field of biomolecules. This collection modifications is termed the adeptome and serves as an endowment record of the expose on as well as potential source of exposure biomarkers to tailor individual patient care. Classically. Sorry. Classically, DNA adults have been highly studied because of their direct impact on DNA replication, exposed gene translation. However, they tend to be very low abundance and have a very short life due to presence of DNA repair enzymes. By contrast, protein adults tend to be very long live as they're not repaired or just recycled as the proteins are so high abundance proteins that are long live such as serum on human and hemoglobin make for ideal candidates from the study of long, the long term expose on. So, one strategy that's been successfully employed in this regard as the analysis of adults at the end term and I have hemoglobin molecules. The tourniquest lab at Stockholm University is pioneering methodology to study these ads called environment. Just an acronym up here fits the measurement of adults are by modified and then procedure fire. It doesn't work any better in Swedish. But basically, if you remember back to your elementary biochemistry, it's essentially modified and degradation where flourishing I assigned is added to hemoglobin, and it will form this cyclic molecule with the internal family which is then comes off of the rest of the protein, which can then be cleaned up by SPE and then analyze via LCMS. While this has been shown to be very successful and identifying a number of as terminus. The problem is that the fire method can have undesirable side reactions and also it's been shown to be somewhat in effectual towards larger electrophiles. At this end, we hypothesize that a simple bottom up proteomics approach, a consultant, not replaced fire. So, and these sorts of experiments human woven is isolated through devices of red blood cells with pure water, and then just digest and trips and cleaned up and run on that kind of sort of standard bottom up proteomics workflow. So the test or hypothesis we got some donor blood and expose it to a series of six electrical compounds. These are integrated blood with blood at various dosages and incubation times after which the ones divided to do is jet fire and bottom up proteomics. So we found a fire that only some of the expected internal addicts were observed these top four and green had a nice linear dose response. The two NGN was only barely detectable in a non linear fashion, and this red, but here DNCB did not see any at all. By contrast, the final proteomics, we can detect all six of these addicts at the end turbine I. Here is a MS tandem mass spectrum of this and terminal beta peptide with the DNCB adept and also with bottom up proteomics, you can differentiate off the chain and beta chain and terminize where these addicts are forming. What's more, when you use bottom up proteomics. This allows for the detection of side chain addicts because many evidence and sidelines are highly empathic. These are detected at us in our human woven samples and it's secure this beta 1693 is especially good reservoir for electric bill at us so what is this a lot of new galaxy. So this experiment we did were done with known electrophiles so in a real sample would be only agnostic we wouldn't know what we're looking for. So to do that, we've adapted software package from the rapcorp lab called atomics are which essentially has a destiny control peptide and you will for mass differences from that control peptide for potential electric files so this program is originally developed for human serum argument and reasons adapted it to take on human hemoglobin as well as rat hemoglobin, given his promises and my work in the zone. So, the way that this workflow works with the galaxy is raw MS data comes in converted to a more amenable file type addicts moments are is able to give you a list of internal addicts will then be searched against a data database of DNA protein addicts. And then statistical validation be done. So, this is currently in process and once that's done, we will be doing a long term study of the unknown addict of smokers and non smokers so with that I'd like to thank all the people that worked on this with me and if you have some questions I'll try to answer them. Thank you. What are you trying to look for with the so you look for known specific adults with constitute certain exposures or. Yes, well, yes and no. With this we're trying to do have it fully agnostic, but we don't know what products were there have been some studies where certain addicts like earlier here, I showed you this a trilomite has been shown to be a biomarker in human development of exposure to various generals and say people who work in all of our industry, and this is correlated with certain cancer so the idea is, can we sort of go back, find novel addicts that might be useful at biomarkers for different diseases. Any other questions. Thank you very much. Thank you. The last talk of this session introduction. I don't need introduction. Hello, everyone. I'm sitting on the galaxy for three weeks team at the University of Minnesota. Am I too low or just perfect. All right, thank you. So basically I'm not going to go over this long title. It's just saying that we dug up galaxy workflows that could get very specific peptides from clinical samples. So, COVID-19. I don't think I want to say anything about COVID-19 anymore everybody wants to get rid of it, and everybody here has a PhD on COVID-19 so I don't want to do it on it. We have two tests, basically, I mean more than two tests, but one of your base assets is antibody testing, antigen testing, you guys all have packets for that. But there's also mass spectrometry based testing for COVID-19.  We were like, okay, let's start with COVID-19 triomics and we decided to publish papers on COVID-19. The first was to basically take COVID-19 peptides and clinical samples and also create a peptide panel that could be easy for targeted COVID-19 detection. That was one of the papers and the other paper was to create meta-prediomics, to create workflows that could detect meta-prediomics COVID-19 analysis and to detect any potential co-infecting microorganisms in your COVID-19 samples. So, that means that there's any pathogen in COVID-19 patients or positive patients that could be fatal for them. So, that's what we were doing with our workflows. While this was going on, there were waves and waves of COVID-19 coming up with different variants and we thought we should like end up with two papers, but that was not it. We just decided, okay, if there are variants coming up, why not like develop workflows that can detect these different variants in COVID samples, clinical samples. But what are these variants? I mean, we are not trying to detect everything like all the panoramic ages, but we were just concerned about main causes of the waves, which is the alpha, beta, gamma, delta, omicron. So, we just thought like, can we detect like peptides from all these different waves? So, we had like a theory in mind, let's select databases from different timelines from 2020 to 2022 and across the globe, different demographics, different parts of the clinical sample, nasopharyngeal, oral samples, ball samples, urine samples can be detect COVID and then specifically the different variants, WL shows specified variants, and have a peptide target list or a peptide panel, a list of peptides that could be used for targeted essays later on. So, we ended up developing two workflows, discovery workflow as, sorry, discovery workflow and verification workflow, wherein first was to identify any COVID peptides present in the samples that we had. And the second was to verify whether the peptides that we detected were actually present as well as then allocate them into the various, the variants. So, let's get to the first workflow. The first workflow basically, as I mentioned, is identification. So, in our previously published papers, we just use search-free peptide shaker as our search engine. But in this workflow, we just added more because you would just be, you know, let's identify more. So, the basic input is the MSMS data, which is the mass spectrometry files, and then we used COVID-19 sequence database, contaminant database and protein sequence database, human protein sequence database as a combined database. The COVID-19 sequence database consists of not just the Wuhan type, which has all the proteins, but we also have COVID-19 structure proteins in the COVID-19 sequence database, everything we've got to say. And the contaminant database is usually for human errors and seeing if we have any human contaminants or, you know, experimental contaminants. And then because these were human samples, we have human protein database. So, we used search-free peptide shaker as well as MaxCon for searching, the output that we got where we had tons and tons of fabric output from these search engines, but we just took the peptide output from the search engines. And then, from all the modifications, we extracted only COVID-19-confident peptides. We filtered out the human peptides, the contaminants, as well as anything that matches with other known proteins, but we just extracted COVID-19-confident peptides. There's another output from this is MC2Sqlite output, which we were later used to look at the spectra from all these peptides and annotate them manually to know whether the spectra that we got from these peptides are of good quality or not. This is how the loyalty for one of the sample peptides looks like. So, if you can see if there are continuous blue and continuous red lines, then you say, okay, there's a good spectra, and all the peaks here should be three-fold more than the gray ones right there. So, that's what we're looking at the spectra, but that goes in the next workflow. So, when we ran the discovery workflow for all of our 12 datasets, we got about 103 peptides that were unique to COVID-19. And what we did was we added these 103 peptides to previously published 803 peptides that we had from our paper as well as other published papers, and then created a peptide panel of 906 peptides. Now, we used Pepquery tool, which is a validation tool, which tells you whether these peptides are present in your sample or not. So, we re-analyzed our 12 peptides and using these 906 peptides and got I think 114 peptides that validated. And these were, we made sure that these did not have any human as well as contaminant proteins in them. Once we had that, we looked at, and this is how Pepquery results look like. I mean, it's not very clear, but it has a peptide column and then it tells you which file it came from, plus it gives you a V-value associated with it and a confidence associated with it. So, that's how we filter out our confidence peptides. Once we did that, we also looked at the spectra like I showed you before, we had to manually go through all the hundred and 15 peptides and we got approximately 82 peptides, which were of good quality and we were confident that these are COVID peptides. Now, obviously, our next question was, which of these COVID peptides are really peptides? Yeah, we did BLAST-P. When I did first time BLAST-P on this galaxy, I just realized that the galaxy P, BLAST-NR was out of date, it was 2018, so it did not contain any COVID-19. So, I had to go outside of the galaxy, do the NR as well as BLAST-P searches and align them to the Wuhan time, so that I can find if there is any variance from the wild side, as well as I did BLAST-P NR to just to make sure that they did not come from any other virus. So, it was mostly to validate that these are indeed COVID-19 and variant peptides. So, we looked at multiple things. Once that was done and we were confident like, okay, these are maybe variant peptides because they didn't have 100% coverage or they didn't have 100% match to the wild side. We took these peptides and annotated them by looking at the peptide report that we got from our previous searches, as well as we have an operator whom we asked to write in our script who could take our peptides compared to the pangolinages and they gave us whatever pangolinages that peptides match to. So, there were some peptides that had like multiple pangolinages associated with it, but there are some peptides that are specific to a certain pangolinage. And then we looked at the pangolinage in the code tool lineage website, the org website and they had those mutations present in them from these WHO based variants. So, if you see here, you can see these are few of the peptides that we detected. The wild type is on the top and then the variant specific peptides on the bottom and the ones in red are showing the mutations from the compared to the wild type. And as you can see, we have like gamma, delta, amicron strains here, so from the different waves from different samples. So that kind of proved like, okay, our workflows did detect these variant peptides. So, few things that we did manually also we had to align these peptides and to the wild type strain and also see like given coverage, different coverage. These are the alpha, beta, gamma, delta, all these different waves that came and the color are coded like that. These all those strains that passed our confidence and probably there were being a lot of stringent. Most of them were nuclear capsicum peptide, we did not get a lot of spike proteins, because of the, maybe the digestion enzymes we used. And because they were clinical samples, we were not getting spike proteins, but we were getting nuclear capsule proteins and which had good mutations in the newer waves compared to the paid out because it was more concerned with the wild type. So those were a few of our results we got. And then here are the six peptides that we were confident about that came from different strains, delta, gamma, amicron. We do have alpha, but we didn't find any data in our samples, but probably because we didn't have many samples it was just 12 datasets, and these were like published datasets. So, if we have more we could do more research we're using our two workflows. So, with that I would like to conclude and say that our workflows are flexible enough to detect emerging strains from clinical samples, but hopefully we don't have to run it again and I hope this is the last thing that comes up. The second is that the peptide panels could be used to develop vaccines. The variant peptides could be, can be actually used for targeted assays, because now that we have these variant peptides, we could do PRMSRM assays to detect them. And the last thing is our workflows can be manipulated to detect other pathogenic organisms so it's not only confined to detect COVID-19, you can just change the database and the parameters and use it for other organisms too. So, I would like to thank all the collaborators and Galaxy PTU to have all of this project and if any questions I would like to answer. Questions? I wonder if wastewater is a possible source? So, the question is if base water data could be used for running these workflows for detect COVID-19 samples. Yeah, I mean we could, we haven't tried it because we haven't gotten the data yet, but yeah that could be used. The only issue is we will have a lot of data and a lot of microorganisms, other microorganisms because it's wastewater, so our database has to be, you know, it will be a large database to search for this, but yeah. So it seems like we can use this as an alternative to validate some of the variants which come from the sequence. For example, there is a question of if some of the in-dose observance has come to me, or did you do any comparison between what you get from your data and what's in the sequence base? So, the question is whether I compare it to the sequence that's already published. In congruent. So, like, I actually tried and compared it with whatever was already published, so people, like COPE2 linkages has like amino acid mutations already mentioned, so I have just done that, but I haven't compared it with the other variants. There are data somewhere. Yeah. Do you have the understanding for someone who's not a proteomics expert? I think COVID has something like 30 proteins, but you're finding that 1,000 peptides. Are those peptides being produced? 1,000 peptides are from 30 proteins during the degradation. Yeah. So sorry, the question was whether you're finding all these peptides, there are only 30 proteins in COVID-19 and we're finding thousands of peptides, and is it during degradation? Yeah, the peptides cut at different sites. It depends on the digestion enzymes I'm using, but using tryps and maybe so cuts the protein into different parts. And yeah, we have a lot more peptides. Any last questions before the break? Okay. Thank you very much.


What's the word, y'all? Man, it feels good to say that, y'all, that the NBA season being here today, first of all, my mental day of holiday bigger than Christmas in my eyes when basketball comes back. This is a very exciting day. And today I want to talk about the two games. I'm probably going to spend more on game number one, the game number two, because there's a lot more takeaways. We're talking about a brand new team, brand new players coming back off injuries and things like that. And the second game, it was just Clippers versus Lakers. Two of the top teams in the Western Conference. It could have gone either way. But of course, I will spend some time there. Be sure to leave a like on the video, subscribe if you're a new welcome to called game. Let's talk about the first game of the day, a complete blowout. But it was fun for the first quarter, definitely, to see Kari Irving coming back and dominate, to see Kevin Arrant come back and dominate is a beautiful sight. Because you know, as far as Kevin Arrant goes, coming off an Achilles injury at his age, there's question marks on how he was going to look. And then if you watched him at all, during the pre-season, you could tell that he is here and he is looking great, man. See him come out and drop 10 quick points in the first quarter was amazing. I'm excited about this team, man. Of course, I don't want to overreact to anything I saw on day number one because it is a very long season of 72 games and things can change. But I do want to give a lot of love to the Brooklyn Nets because they came out and that offense looked amazing. You have a guy that's heavy ISO and Kari Irving. You have a guy that's heavy ISO and Kevin Arrant. And yet the ball never felt like it was sticking. It never did. They were flowing. People were hitting shots. Kari's the first coming off the bench, looking to me. Like the offense look deadly. And if I was the other teams in the league, again, it is against a Warriors team that maybe is worse than what we expected. But they just came out and they dominated. And I would be afraid if I was the rest of Easter conference because, I mean, I guess you were counting on Kevin Arrant not looking as good as what he did in that first game for you to feel comfortable and confident. But that man just, that man was different. And Kari Irving, this ain't breaking news. We all know Kari Irving has the nicest handle ever. Yes, I said it, ever. And it's just everything he does is so silky smooth on the court. Love Kari Irving as a basketball player. It's going to be really fun to watch the Brooklyn Nests this season. But I want to spend a lot of time on the other team because this is a team that was, for me, super hard to project. I made a video at the beginning of the off season when they got the second overall pick before the draft, like right after the lottery. And I was basically saying that I was excited for Warrior's basketball again because Clay Thompson was back in the gym at the time. Steph Curry was healthy. And then they had the second overall pick. We kind of knew it was going to be Weisman, but we weren't completely sure. Weisman fits what they were trying to do. And I was, I think the video said that the Warriors are the most dangerous team in the NBA. Freezing cold take. But a lot of that is because Clay Thompson is injured, by the way. If Clay Thompson is there, we're having a different conversation on open night. I must admit. I mean, I saw some people on Twitter saying things like, with Draymond comes back, things will be better. And I'm like, Draymond ain't that big of a band day to cover up all the things that we saw in today's game. Again, like I said, I don't want to overreact to what I saw. But there is some gleaming issues with the team. And that's why I said it was so hard to project because we know Steph Curry is one of the best players in the league, the best point guard in the league. And he has this gravity to him on the basketball court. Of course. But what we have realized throughout the five games that he played last season and the first game of this season, again, that is a very small sample size. But if he doesn't have his shooters around him, it's way easier to guard Steph and Curry. Like Steph Curry and Clay Thompson need each other like love needs water. You know what I'm saying? It's just like they fit so perfectly. We have two of the best shooters of all time playing on the same team. And it's just hard to defend at the end of the day. And now with Steph Curry having to kick it out to Wiggins or Steph Curry kicking it out to Kelly Ubrey who look bad today, it is so much easier to be like, mm, Ubrey's cool. But we must try to double the guy that's the greatest three-point shooter of all time. Wiggins, we don't worry about that whatsoever. Eric Pascal, cool guy. We ain't worried about him. We're going to bring that double team. And there was a time in this game, and we're talking about third quarter at this point, so it was already 25, 30-point game. And there was a lineup out there of Steph Curry, Kelly Ubrey, Wiggs, Eric Pascal, and Kevon Looney. And I was looking at this lineup, and I was like, they should be double team as Steph Curry. And guess what they did? They double-teamed Steph Curry, right? One of the greatest parts about Steph Curry's game is not necessarily his ability to score, but the threat of him being a score. And a lot of that dies when he doesn't have teammates around him because it was hard to taste around Steph Curry when you also had Clay Thompson out there. Obviously, it's hard when you have Kevin Durant. And this is a season for Steph Curry. I don't think Steph Curry has anything to prove. I need to put that out there. I don't think he has anything to prove. He's a two-time MVP, multiple-time champion. He has nothing left to prove, but what I'm saying is that for this team to be successful, Steph Curry's going to have to try to bring more than what he did. It's weird to say, we're talking about a guy that is so, so elite on the offensive side of the ball, but you can only do so much as a guy that's not going to be super quick to get around as defender. Or on Twitter, it turned into, I mentioned it turned into a Dame versus Steph Curry debate. And I didn't want that to happen. Like, ranking players are so lame to me now in 2020. It was something I would do when I was younger and stuff, but I don't want to do that anymore. But they're as great as they are. They're so different player. They're just such different players. And this is one of those seasons where you want to see Steph Curry have the Damien Lille type year from last year. Damien Lille type year from last year got them to the bubble, into the playing game, into the HC and I think they took Game 1, yada, yada, yada. Steph Curry's going to have to have that type of season on his own. And throughout the course of his career, he hasn't really had to do that because Clay Thompson, I'm starting to think that the world is realizing this, but throughout the last four to five years, Clay Thompson has been severely underrated from the average basketball fans. Sure, he has a couple all-star appearances, but like he is similar to Steph Curry. We're like, you can't leave this man anywhere on the court. And when you have two of those guys out there, it's ridiculous. And then that opened things up for guys like HB before Durant got there and allowed Draymond Green to be Draymond Green. And then Draymond Green before the season started, like a week ago, he said like, this is a championship or bus season for them. And that is, I understand having the wanting to win the championship. That is the end goal for all 30 teams at the end of the day. But championship or bus is harsh when you have the players that you have. I think I mentioned his name earlier, but the way Wiggins plays, man, it frustrates me as a basketball fan. Because I know, and you know watching this video, all the potential that Wiggins has. And every year I go into the new season, like, okay, this could be the year that Wiggins does what Wiggins should be doing. This could be the year. And again, I don't wanna overreact based on game number one, who knows the 71 games that are remaining he could take off. But this game, it was just like, when he was on the court, I was like, please get him out. Please get him out. But the problem is the reason why this team was so hard to project, of course they had Clay Thompson going out of Andre Montgrain, I played well last season. But this is a team that lacks depth. And one thing that the Warriors have had to all of the titles run is they've had depth. They have Iggy, they have Sean Livingston, they have Vessels Azeely, they have players like that that'll come off the bench, the Brazilian Blur, remember? Barbosa was killing the game. They'd always had Blur when they were contending, they always had depth when they were a contending team and they just don't have that this year.  Juan Toscano? You know what Wiggins, you air ball in two shots is probably better than what Juan could put us on the offensive side of the ball. So this team lacks depth. They're missing their second best player. And it's so heavily like Steph Curry would have to put on the backpack bigger than any backpack I've seen in a long time. So because of that dream on, this is not a championship or bus season. It's not a contending team. And who knows, again, I don't want to overreact. I still don't want to project this team to be in the lottery this season, but it wasn't promising what I saw. So then to put up the, what, 85 points if that this game was kind of sad. Now I do want to say some of the shots that they got were good shots. It's just that they don't have good shooters. The shots that Wiggins were getting in that corner, those are good shots that you want to see people take, but it's a below average three point shooter taking it. You know what I'm saying? And the last thing about the Warriors before I move on to the second game of the day is that they are missing that second ball handler type guy. There were so many turnovers I saw where like him baseball was bringing a ball up the court and he was trying to make a simple pass to cut and then they just jumped the pass. It's just like they don't have a secondary ball handler whatsoever. And I think that comes in with Draymond is healthy and ready to play, but they desperately need somebody other than Steph Curry to play because again, Steph Curry is so deadly off screens and is so deadly without the ball in his hands, but nobody that played today could get him that ball. Again, shout out to the Brooklyn Nets. They looked amazing. The Warriors a little bit a little bit afraid of. Now, let's talk about the second game of the day. There's not really that much to talk about the second game of the day because we do have two of the best teams in the Western Conference goal in the entirely going at it. And it's going to go on either way. Of course, both teams have new pieces like Sergio Baca looked really good tonight. I'm already seeing Lakers fans really mad at Marcus Saul for the stinker of an opener, but it's Marcus Saul. You're going to get some good games. You're going to get some bad games. I think a lot of what Marcus Saul brings is hoping that it happens in the playoffs and kind of ignoring what he did in the bubble of last year. I keep telling people this whether it be on my podcast on Twitter or whatever that like I am taking the bubble performances from players with a grain of salt because it was such different circumstances than what they're normal to. It just is like the people that were averaging for you. I don't expect Jamal Murray to come out and average 30 this season. I do want him to be better, but I'm not going to look at the bubble. And if he comes in and averages 22 instead of the 30, I'm like, this is a disappointing season because it was so different. So I don't want to look at Marcus Saul's very bad play in the bubble in the playoffs of last year. I'd be like, that's who he is now. But in the first game of the season, it was pretty rough. One thing that was encouraging with the Lakers is that the mattress hero Anthony Davis minutes looked great offensively and defensively. It's always a pleasure to watch Anthony Davis defend wing players because he just can. And that's probably, that's why I have him as one of my top defensive player to year players this year is because like there were times he was guarding Kauai and did a very good job. Like there were also minutes where Anthony Davis and Cal Cousma were on the court together, but Cousma was guarding search while Anthony Davis was still guarding Kauai Leonard, which is like, just Anthony Davis is that guy. But shout out to Paul George at Kauai.  But everybody knows that on his best day, Paul George is the man. He just is. And today was one of his best days. Today's one of his best days. This is one of Kauai's best days too. I was very curious to see what type of play we get from Luke Knard because I'll be the first to admit it. When he was playing in Detroit, I didn't get to watch him much. So when he got that extension, I was like, wow, that's a lot of money. But I'm curious to see how he performs this season to see if he'll live up to the money that they gave for him.  And I hope that we finally this year get to see this in the series, whether it be in the second round and the conference finals, whatever, because these two teams match up so well together. They just do. And I did see the Clippers running a little bit of zone in there and that kind of hurt the Lakers. The offense in the first quarter for the Clippers was so fluid. And then the second quarter, everything got stagnant from them. But the first quarter was so nice. Sergi Baku was setting the screens and picking. And it was just really good play from both teams at times. Fourth quarter, the Lakers kind of fell out. And I kind of, even as I'm recording this video, the game is not completely over. But with four minutes left with no LeBron on the court, I'm just assuming they've thrown in the towel, which is fine. I'm very curious to see how much of this season we see of LeBron. Because this game particularly, it didn't feel like he was on the court for that much. He did have a spurt, so he was taken over. But it didn't feel like he was out there as much as you would want him to be out there on the open tonight. But again, it was what, 50 something days ago, they won their championship. So the shortest off season of his life, he probably wasn't able to spend that million of dollars that he do in the off season to keep his body right. So this will be a coasting year for LeBron, I'm guessing. But overall, a great game for the second one because again, the first game wasn't living up to it. I don't know how I'm gonna do these for the games like tomorrow, the nights like tomorrow when there's so many games. Maybe I'll watch as much as I can and try to talk about what I can. We'll see how this goes. Hope you enjoyed this. If you did, leave it a like. Oh, in the description, there's gonna be a link to, I've been using the app Flick and I have my own group. If you didn't know what it is, it's kind of like Discord type, but it's just for our community to talk about sports. So join that, let's talk about games. Not tonight though, because I'm going to bed, but like tomorrow, I feel like I'm gonna be live typing and chat to things, talking about things. So download Flick the app and join the group, man. Thank y'all so much. I'll see y'all tomorrow soon. Call the game.


Nobody's saying tired. I would have said tired if somebody asked me that question. So exhausted. It's been a great conference, but it's been long, and I am privileged to have the last speaking slot, which is a really funny joke to play on a guy with imposter syndrome. So whoever set the schedule, nice job. My name is Nicholas Means, but we are about to be really good friends, so please call me Nick. You can find me, hi. You can find me as In Means on Twitter if you're playing along on your laptop or your phone. I'm an engineer at WellMatch Health. I'm lucky enough to get to remote pair nearly 100% of the time from beautiful Austin, Texas with a lot of really smart people, fixing some of the hard problems that vex our healthcare system. I want to start by asking you to think a little bit. What is your biggest fear at work?  I can tell you mine. I've been a software developer for over a decade now. I did some PHP spaghetti for a while, moved into Ruby, been doing Ruby for, gosh, eight years now. I've worked as a solo developer. I've worked as a siloed developer where I was the only guy working on a product and a broader team. I've worked in a really good team environment, which is what I'm in now. But I've had this persistent fear the whole time I've been a software developer that I was about to be fired. Now, how many times do you think I've been fired in my 10-plus years of software development? Brandon, right, three, no, zero. I've never been fired, but it might happen any day now, or at least that's what my brain persists in telling me all the time. It's just a matter of time until my boss figures out that I really have no idea what I'm talking about, and I'm a terrible software developer, and all my commits are crap, and then I'm going to get the pink slip, which is not true, right, because I've convinced a bunch of people to give me a job, and I've shipped good code, and I've been through pull-request processes, my code's been reviewed, I pair now. So I'm an OK developer? This is the gist of imposter syndrome. It's a set of beliefs and behaviors that was first identified back in 1978 by doctors Pauline Rose Clantz and Suzanne Imes at Georgia State University. Dr. Clantz was on the psychological staff at Georgia State, and Dr. Imes was a researcher, a pre-doctoral researcher under her. And they did a lot of interviews with graduate students, female graduate students specifically. Now, these women had all gotten into very competitive graduate programs. They had all been published in very prestigious psychological journals, but they still didn't think very much of themselves. There's actually a really great definition of imposter syndrome in this abstract. Despite outstanding academic and professional accomplishments, women who experience the imposter phenomenon persist in believing that they are really not bright and have fooled anyone who thinks otherwise. So these women were all very prestigious, all very successful, but still thought that they had everybody around them fooled, and all the stuff that people told them about themselves was not true. So if you've experienced these kind of feelings where you think that people around you say nice things about you, they're not really true, let me tell you a little bit about yourself. Pretty good chance that you're an introvert. Pretty good chance you're a perfectionist.   You probably have a deep-seated and abiding fear of failure.  You probably feel less competent than your peers. If that resonates any, there's good news, you're not alone. As it turns out, imposter feelings are really pretty common. Dr. Clancy's research indicates that about 70% of people experience imposter syndrome at some point in their careers. Now that's not to say that everybody experiences imposter syndrome the same way. There's people like me who have experienced it throughout their entire career as a persistent feeling afraid that they were going to be fired any moment. There's other people that maybe it only happens when they start a new job or they emerge from college into the workforce. But no matter where you are on the scale, there's things that you can do to lessen this reaction that you feel. So there's two things I want to do in this talk.  Because there's a lot of stuff that goes on in your head that reinforces this imposterism and this inability to accept your success. And I want to tell you how to patch your brain. As it turns out, imposter syndrome in your head is sort of this infinite loop that goes round and round and round. And what we have to do is find places where we can hook into that loop and get out of that cycle. And if we can do that, then we can start functioning a little bit better. But before I do that, I want to tell you a story. It's about the first conference talk I ever gave.  Somebody decided it was a good idea to give me a speaking slot at RailsConf. I decided on a whim about an hour before the CFP closed that I was going to put in this talk proposal. And it's one that had already been rejected by a couple of conferences. So I tweaked it a little bit, I cleaned it up, I made it a little bit better, and submitted it. And the RailsConf submission process is like custom-tailored for someone with imposter syndrome. Because the first round is blind, so I don't have to worry about that I don't have this great reputation in the community and nobody knows my name. And during this process, there's the chance for people reviewing your proposal to give you feedback. So Sarah May, bless her heart, read my early rough draft and gave me some great feedback.  So I was hanging out with my wife and my three-year-old son when I got the email that my talk was accepted at RailsConf. And I gasped, and I was speechless, and my wife thought somebody important had died. Finally, I had the words out to tell her what was happening. And I was very excited. So when I put the proposal in, the whole time I'm waiting to see if I got in or not, I'm envisioning... Our team has a Friday happy hour, and I'm envisioning getting to tell my teammates that I got a talk accepted at RailsConf, and I got to do that, and it was glorious. But then reality sets in. I don't know if any of you have ever spoken at a user group or something, but as it turns out, you don't actually have to write the talk before it gets accepted. Once your talk is accepted, you have to start writing it. And it plunged me immediately into this worry, fear, panic, and dread. I sat down, I tried to make an initial outline, total creative block. I couldn't come up with anything. I couldn't organize my thoughts to save my life. The funniest thing was my brain played this great trick on me.  So I pushed through it, and I took an imposter syndrome test on which I scored very highly. And I'll share that test with you guys later. So after that, I jumped into full-on overwork. Now, normally, I am a huge procrastinator. Normally, I sit and wait till the very last minute to do something, but I guess my brain thought this was important, because it jumped into overwork from day one. Like, three days after I found out my talk was accepted and my family was headed on vacation. And, you know, as you do, I loaded my Kindle up with psychological journal articles and brought books. And I spent most of my vacation reading about imposter syndrome, which was not what I had planned to do. And then, hopefully, the next step in the cycle is success. Now, clearly, the jury is still out on this, but hopefully, when you're done, you won't feel like your time has been wasted in this room. After that, I'm going to feel some initial relief. A co-worker asked me... I had this long list of things that I hoped happened after the talk. And a co-worker asked me, okay, so what do you really want to happen after this talk? Really, all I want out of this talk is to help somebody. If I accomplish that, that's all I want out of this. So if that happens, I'll feel some initial relief. But then, as we do, I will move into this step where I am focusing on all the little failures in this talk. I'm going to focus on the jokes that I didn't get just right. I'm going to focus on the times I said, uh, and um, and stumbled over my points and the content areas that I wish I had covered better or phrased differently. And then I'm going to move into sort of the meat of imposter syndrome. I'm going to start denying my success. I'm going to start feeling like people saying nice things to me in the hallway are just doing it because they know I have imposter syndrome and need compliments. And that's what we do.  So then, when the next opportunity comes up, you cycle right back into this thing because you've never been successful in your life, at least in your own mind. This is the imposter cycle. This was identified in Dr. Clancy's research. And it's driven by the superstition that we have to go through this fear and doubt and overwork and panic to get anything done.  The reason you did okay is because you went through this big process of being all panicky and fearful and procrastinating and overworking.   There's a lot of stuff going on in your brain that causes this to happen. I alluded to some of these earlier. One of the first ones is introversion. There's probably a lot of introverts in this room. And the thing about introverts is we all spend a lot of time inside our own heads, right? We spend a lot of time replaying social interactions, wishing we hadn't been awkward, wishing we were better at small talk.  So we spend a lot of time in self-criticism. The next thing is perfectionism. The two most highly correlated personality attributes to imposter syndrome are introversion and perfectionism. So the thing about perfectionism is we have very high standards for ourselves, right? We want to get the gold standard. So what happens when we hit silver plus plus plus? We have failed. So you have this inability to ever do as well as you think you ought to do. You have this inability to ever pull anything off to your own perfect standard. And then it goes into something even more sinister than that. There's this need and perfectionism that emerges to always be the best. Not enough to be good. You have to be the shining star. And so you get into this, it can create some really weird things in your job. So when a co-worker, say, gets a lot of praise for pulling something off, I feel threatened by that.  I know intellectually that I shouldn't. I know that there's more than enough credit to go around. It's not a zero-sum game. But I still feel like somehow my co-worker getting praise is taking something away from my shiny gold star. And so it sets you up to always have to be perfect. It's terrible. And it feeds into workaholism, because it takes a lot of work to live up to that standard that we all think we need to live up to. A whole lot of work. Or when we get into a situation and we know we can't, we procrastinate. Now, I've been a world champion procrastinator for the bulk of my career. And it wasn't until maybe a year ago that I realized how intertwined it was with imposter syndrome.  It lets you get something out the door that might not be as perfect as you want it to be. It gives you a time box, and what you can produce in that time box has to be good enough. And it also gives you a way out of failure. Because, like I mentioned earlier, imposters often have a strong fear of failure.  It's not a reflection of you as a person, which is the perfect segue into the next thing going on in your head, which is fear of failure. As imposters, we are terrified for others to see us struggle. We are scared to death for anybody else to see that we might not be able to pull it off the first time we try. Because we feel like we're walking on eggshells. We have this very thin facade that if anybody can get through, anybody can see around, they're going to realize what a complete and total failure we are. So anytime there's a crack in that armor, we're afraid that it's all going to come tumbling down. So we developed this deep-seated fear of failure that makes it very hard to go and try to do new things. It makes it hard to do anything you think you might look foolish doing. A perfect example of that is the dance floor. And one thing you will not see on the dance floor is this guy. Much to the chagrin of my wife, by the way. She would really prefer me to get out on the dance floor with her.  I won't risk that part of my personality because somehow it's going to make people think less of me. I know it's kind of a contrived, trivial example, but it plays out on the rest of your professional life. It plays out when you're trying to learn something new or you're trying to fight through a hard problem. You're kind of afraid to admit that you don't know. This one is really the meat of Imposter Syndrome, this idea of denial of success. One thing we're all really good at as imposters is deflecting objective evidence that we're actually competent and successful. We're very good at pushing those things away. We deflect compliments. We write off promotions. We got new jobs because we're good interviewers, not because we're good developers. In fact, when I found out my talk was accepted, the first thought that came into my mind is, man, it's going to be such a letdown tomorrow when I get the email that their CFP system screwed up and emailed everybody a letter of acceptance.   It gets into attribution theory and psychology, and that's the idea of where you attribute success and failure. As imposters, we tend to attribute success to external factors. So it's luck and timing and knowing the right people, whereas we take failure and we internalize it. It speaks to who we are as a person. If we fail, it's completely a reflection on us, but if we succeed, it's not a reflection on us at all. It's a reflection of the situations we found ourselves in. We also have a little bit of fear of success going on. There's this fear that being successful might ostracize you from your peers. I know I felt this in school a lot. I was the kid that knew the answer every time, and by about third grade, I had developed a defense mechanism where I hardly ever raised my hand, because I didn't want to be the kid that knew all the answers, because that kid didn't have any friends. This is part of why people from marginalized groups suffer disproportionately more from imposter syndrome than the rest of us. The only marginalized group I'm a part of is guys without air. But I still have a very strong case of imposter syndrome. When you get into a marginalized group and you might be more successful than some of your peers, you're afraid it might cause you to be outcast from that peer group that's one of the only safe places you have, one of the only places you belong. It's a scary thing. There's a couple things that make imposter syndrome more prevalent in technology than it is in other industries. One of those things is the vastness of the knowledge involved in being a technology worker. Most of us have heard of the Dunning-Kruger Effect. It's this idea that unskilled people may not really be able to gauge their level of competence. They may feel like they're more competent than they are because they don't know their gaps. We have the diametric opposite problem of that. We know every last one of our gaps. We know all the things that we don't know. We know all the things that we need to improve about ourselves. And we know how much we don't know. DHH alluded to this in his keynote, actually, when he talked about the imposter plexus. We spend a lot of time wondering if we're doing it right in software. But the definition of right is always moving around. It's very hard to track. And so it sets us up for this feeling where we're never doing it right. We're always two steps behind. There's always something else we need to know to be able to be accepted on our field. The other thing is peer scrutiny. We have actors, scientists, college professors, musicians, artists all have a hard time with imposter syndrome. And the thing all those professions have in common with us is this idea of peer scrutiny.  And it's a scary place to be.  It's really helpful. The other thing we do is we compare the finished work of other people to our messy thought process. So you look at this library that you admire, you look at the code, you look at the structure, you look at the way it's organized. It's fantastic code. Then you look at your own code. But when you look at your own code, you have the backstory of that code. You know how it came to be. You know what a struggle it was. So you assume this guy just wrote this entire text file but you know how the sausage was made on your code and you know it wasn't a pretty process. So you're setting yourself up for failure in that comparison. You're never going to come out ahead when you compare your thought process to somebody else's finished code. So how does this work out in practice? There are a lot of things that imposters don't do because of imposter syndrome. We don't participate in conversations. We come to events like this, user groups, we tend to hide in the wings, right? Because if we hide, then we don't have to put ourselves out there. We don't have to let anybody see us and figure out that we don't know what we're talking about and shouldn't be where we are. We don't ask questions. It's hard for us to admit that we don't know something. We pretend like we know it, fake it till you make it, and then we come back later and frantically scramble to try to learn the things that we don't know. So we write crappy code, and we adhere to best practices. We don't ask questions that would allow us to become somebody with a trained eye for code that actually knows how to apply those best practices well. And we all know that if you just blindly apply best practices, you're going to end up with some code that's really pretty rough. But if you don't ask those questions, if you don't put yourself out there to admit that, yeah, you know what, I could learn to do this a little better, you don't grow. Imposters don't teach others. We don't give user group talks. And the community is worse off for this. Brandon Hayes in his talk this morning alluded to the fact that everybody sitting in the audience has a talk that they could give. There is something out there that you know an awful lot about, maybe more about than anybody else. But if you're afraid to get up in front of people and share that knowledge, the community is lacking that knowledge. The community is lacking your unique viewpoint. So Imposter Syndrome robs our community of some voices that would otherwise be very helpful and very insightful. Another thing Imposters don't do is contribute to open source.  It was something along the lines of WordPress isn't supported on Microsoft Stack or something. I don't know, something dumb. And I got my rear-end handed to me for it. And ever since that happened, I have been very hesitant to do anything of any significance in the open source world. And I have a feeling there's a lot of people in the audience that that's true for as well. Maybe you got bit one time, maybe you're just afraid of getting bit by trying to contribute to open source. But it's something that Imposters don't do because we're afraid of that rejection, we're afraid of that failure. The other thing Imposters don't do is they don't change jobs. I mentioned I was a solo developer for the first chunk of my career. I was in that job for five years. It was a bit of a cushy position. I'll admit that. But by being a solo developer, I deprived myself of the chance of learning from other developers. And by the time I left that job, I was not making anything close to a market salary. But as Imposters, we kind of feel lucky to have the job we have and to have the people that are around us fooled. It seems like an awful lot of work to go take a new job and have to fool a new group of people, right? So we just sit where we are. So clearly these are not things that we want to happen in our community. So how do we patch our brains to get out of this cycle? Like I mentioned earlier, Imposter Syndrome, that cycle, it's kind of an infallible loop in your head. And you just have to find the places that you can hook into it so that you can get out of it. But good news is, is that for starters, knowing is half the battle. If you just understand that this thing that you feel, this pervasive sense of incompetence that you feel, actually has a name. It's called Imposter Syndrome. You know how it's affecting your thoughts.  That's the first start of the road to recovery. I mentioned earlier I've got an imposter survey. I'll give you the link at the end of the talk. It's derived from research from Dr. Clance. And it'll give you a numeric score. It'll help you sort of sort through some of the thoughts in your head and figure out if it's something that you're dealing with. So once you know what Imposter Syndrome is, the next thing is mindfulness. You have to start paying attention. Because I know this is super cheesy, but I'm going to do it anyway. I want you to turn to somebody next to you in the audience and pay them a compliment. If it's somebody you know well, make it as specific a compliment as you can. I'll wait. All right, come on. Reeling you back in here. I wondered if this would happen on the last day of the conference, and it did. So think back to when the person sitting next to you paid you that compliment. How did you react? Was your initial instinct to accept that compliment and take it at face value as true? Or was it to try to write it off? To try to find some way to deflect it? Because that's what we do as imposters, right? If somebody comes up and says, hey, nice job. Oh, man, I was really lucky. It was an easy topic to talk about. That sort of thing. We tend to write it off. So you have to pay attention to how you accept compliments. You have to work to accept them gracefully. If you don't know how to do that, just say thank you. All you have to say. It's very hard work, and I am still terrible at it. The other thing you have to do that you can do to help you find how this is happening in your head is to listen to yourself talk. See if you do a lot of self-deprecation.  Or do you go, oh, I forgot a semicolon. Not that we ever do that because we're rubious and we don't write JavaScript or anything. The next thing you have to do is accept your success. Accept your success. Trust reality, not your feelings. Brandon Hayes, a friend of mine from Austin, said this probably as eloquently as I've heard it said. He was talking about leaving a previous job and coworkers paying him compliments and saying nice things about him. And he said, you know, at some point I had to stop the thought process in my head. And I had to try to find these things that people were saying about me inside myself. I had to find them as true. And it's hard. It's hard. You have to try to accept that compliments are true. You have to accept that nice things that people say about you are true rather than immediately writing them off. Another way to practice that is to give more compliments. I mentioned sort of this idea of a zero-sum game where you have to be the shining star and you're worried about what happens when people on your team get compliments. Well, give more compliments. Notice the genuineness with which you deliver a compliment. If you notice how genuine you're being, start assuming other people are being that genuine as well. And they're not just deluded into thinking you're something you're not. You have to own your success. And even if your success involved a lot of luck and timing, you still had to be smart enough to use that luck and timing to your benefit. So even if you try to write it off as luck and timing, you still had something to do with it. You also need to be kind to yourself. Forgive your mistakes. If you notice yourself being very self-critical, interrupt yourself. Notice what you did well in addition to what you feel like you need to improve on. My wife's a middle school English teacher. And in her classroom, she practices something that she calls the compliment sandwich. When she has to deliver some bad news to a student or tell them how they need to improve their writing or something like that, she tells them first something they did really well. Then she delivers the improvement and then she tells them something else they did really well. Well, if you start doing this inside your own head, what will happen is you'll have to find something you did well. Then you can indulge in that self-criticism that feels so good to us. And then you tell yourself something else you did well. And over time, you'll notice that you start to notice your success more than the things that you wish you'd done better. Another technique you can use is to ratchet your freak out. And I use this one a lot giving this talk. I mentioned that I moved into that fear and doubt and cycle pretty early on. I made an agreement with myself that I was not going to freak out until a week before the conference. And it actually worked pretty well. So normally when I do something like this, I would freak out a long time in advance. I would start worrying about it and feeling like I wasn't going to pull it off. But I made an agreement with myself that I wasn't going to do that. And I sat on it for a while. And the week before the conference, it actually worked. When it would come up, I would push it back down and say, you get to freak out the week before the conference. You don't get to freak out now. And then the week before the conference got here, and boy did I freak out. I stayed up till 2 in the morning most nights, not doing much of anything, but feeling better because I was doing something like I was making progress on my talk. It was very much a coping mechanism. I wasn't accomplishing anything. But instead of doing that for a whole month, I only did it for a week. And the magic of this is it lets you be successful without freaking out for a month. And so you start to break down that superstition that you have to do a lot of freaking out before you can be successful. You start to chink away at that bit by bit until finally you become convinced that it's not as necessary maybe as you believe it is. Another thing is embrace vulnerability. Allow yourself to admit you don't know things and ask questions. If you don't know who that is in the background of that slide, say Brown, if you haven't seen her TED talk The Power of Vulnerability, put that very high on your to-do list. I saw it probably two years ago and it was the start of my journey out of imposter syndrome. Because it was the first time I'd really ever embraced the message that it was okay if I wasn't perfect. It was okay if I didn't know everything all the time. When you start embracing vulnerability, it frees you up to be yourself and be with that to be okay with not knowing everything.  But because instead of trying to build on top of this shaky facade, you're building on who you actually are and what you actually know. In the process of embracing vulnerability, something you absolutely need to try is pair programming. I don't have a lot of time, but I will be more than happy to pair with any of you if I can find the time, just ping me on Twitter. I'm lucky enough to be on a team that does pair programming 100% of the time. And what you find, a co-worker said this to me right as I was walking in her to give this talk, what you find is that knowledge becomes collective, not something that you have to bring to the table individually. So it's okay if you don't know everything. Your pair probably does and there's no penalty for them knowing things in you not. But it also makes you be vulnerable enough to work with somebody, because if you're pair programming with somebody, there is no hiding. And it's really the first dose of having an accurate basis of comparison for my work that I ever had. Working with somebody, seeing how that worked, seeing how I worked, seeing how we did a lot of the same things and approach problems the same ways convinced me that maybe I wasn't that bad a developer after all. But most importantly, you got to keep going. You have to keep going. The hell of imposter syndrome is that you were never fully recovered from it. This pattern of thought is so deeply ingrained in your head that it is very hard to escape from. So what you're working towards is becoming what I like to call a high-functioning imposter. I've alluded to the fact that I've been my own walking case study in the development of this talk. So clearly I am not out of the woods. Most of these things on a very regular basis. The difference is, I can work through it enough to be able to get up and give a talk or to ship code and know it's actually decent code. And everybody can do this. It's not that hard. You just have to start working through it and continue working through it and not giving up when it's hard and continue pushing against it. The last thing I want to leave you with is this. I talked about Dunning Kruger earlier. You need to remember about imposter syndrome. You have a lot of very smart people around you saying very nice things about you.  They're not good enough at reading code to tell that your code is terrible. And these are also the people that serve as your basis of comparison. So you're looking around at all these people that are very smart, and you're saying, well, you're saying these people that you consider to be smarter than you are not smart enough to see through this facade. They are. And they're not because there's no facade there. So the fact that you experience imposter syndrome pretty much means that you have no reason to experience imposter syndrome. It means you know enough about your skill and your craft to see your shortcomings. And you just need to focus more on the things that you know and the value that you bring to the table. There's no credit for that. And focus less on the things that you still have to learn because you have a long career ahead of you. You don't have to know it all now. Thanks.


This week in Hyrule, Nintendo is throwing some new E3 news at us, and I might finally have a reason to buy some new shoes. Your news recap starts now. Welcome once again, folks, to this week in Hyrule being brought to you from a shiny new location. Now, to kick things off, let's dive into all this E3 craziness. Recently, Nintendo gave us some insight into what exactly they have planned at this year's E3. Here's the basic rundown. Their show starts at 9am pacific time on Tuesday, June 14th. Nintendo plans to stream the Legend of Zelda gameplay, and you'll be able to view the stream on Nintendo's YouTube and Twitch accounts, as well as on E3.Nintendo.com. Scott Moffitt of Nintendo of America said, at E3 this year, we're treating people to an immersive Zelda experience. And series producer Ejiya Numa has said that this is going to be a departure from some of the conventions of previous titles. The game will have less boundaries than other titles, and will be introducing new gameplay. And if that wasn't enough, Nintendo's E3 press site might have given us some small hints into what the story of the new Zelda game is going to be focused on. When you visit E3.Nintendo.com, you'll be greeted by a lone blue Sheikah symbol against a black background. This image then fades to show Link and Epona standing in a big open world. Now, could this mean that the Sheikah are going to play a large role in the story? Possibly, but right now it's all speculation. Okay, that was a lot of info, and honestly I'm really excited to see how much gameplay we're actually going to get to see at E3. But for now, let's talk about shoes. Nintendo is teaming up with shoe company Vans to bring us some super awesome super nerdy shoes. This line of shoes is based on the NES, and many of its games including Legend of Zelda, Donkey Kong, Mario, and more. No official release date or price, however the European sneaker store 43 in Yen Halb has them listed for a release sometime in June. To any German viewers out there, please forget my butchering of your language. Now, let's get to the last new story of the day. After over 30 years of Zelda games, the original Legend of Zelda is being inducted into the World Video Game Hall of Fame along with five other games. The strong National Museum of Play recognized the game for its popularization of non-linear exploration and for being the first console game to include an internal save battery. All right, that's it for news, so let's check out those contests. For this week's caption contest, we had you guys speculating as to why poor purple Link was left out from Triforce Heroes. Gotta say, my favorite absolutely goes to Kune Mang. I can hear the angry comments from here. For the caption contest, we wanted a piece featuring the Ladies of Zelda. I definitely have to give this to Link584. I love this movie and this crossover. Would actually be pretty hilarious. But let's not have Lindsay Lohan play Illya. All right, that about does it for me. If you want any more information on the stories discussed in this video, you can check out the links in the description. Keep it locked to Zelda Informer for all your Zelda news. I'm Matt and this was your week in Hyrule.