Transcript
*Please note that you may experience errors in the below transcript, therefore, we recommend watching the video above for full context.
Delaina Hawkins Hello, everyone. On behalf of the entire Golden Helix Team, I would like to thank you for joining us today. My name is Delaina Hawkins and I am the Director of Marketing and Business Development at Golden Helix. And I would like to extend a very warm welcome to our presenter, Darby Kammeraad, who is the Field Application Scientist Manager here at Golden Helix. Darby, thank you for joining us today.
Darby Kammeraad Yeah. Thank you, Delaina, for the warm introduction. And thank you all for attending today's webcast.
Delaina Hawkins Yes, we are looking forward to your presentation. He will be discussing our recent VSWarehouse update to benefit our VSClinical users conducting somatic variant analysis. But before I pass things over to Darby, I would like to mention to all of our viewers that one of the main benefits of attending our live webcasts is the opportunity to ask our team of experts like Darby and any of your questions. So with that said, if you have any questions you would like to ask or you've kind of been thinking about previously. You can just enter those into the questions tab of your GoToWebinar panel. The screenshot shows where you will find that on your docket that's loaded on your screen. And then and at the end of Darby's presentation, we will be opening it up for some Q&A. I think that covers it for me now. I'll go ahead and pass things over to you.
Darby Kammeraad Yeah, thanks. Like Delaina said, basically what I'm going to be covering today is kind of a brief update on the upgrade that we've given to the warehouse instance to support the capture of the somatic variant classifications and interpretations using the AMP guideline tool and VSClinical. So in addition to that, before I actually jump in deep dive into the demonstration to the rest of the slides, I did want to give a thank you to the NIH. And we're actually very incredibly grateful for this research grant that we've gotten from them.
Darby Kammeraad The research reported in this publication was supported by the National Institute of General Medical Science of the National Institute of Health under these listed awards. And we've actually received one from the state of Montana as well. And so our PI, here is Andreas Scherer, Ph.D., who is also the CEO at Golden Helix. And of course, the content described today is solely the responsibility of us as the authors and does not necessarily officially represent the views of the NIH. So again, thank you for grants like this. It helps us maintain producing the top quality software that our customers come to rely on.
Darby Kammeraad And a little bit more overview of the company background itself. Golden Helix is a global bioinformatics software company founded in 1998. So over 20 years of experience with trying to provide software and analytics that basically help with research and clinical practices and analyzing large sets of genomic data. So since our kind of foundation, we've actually been based on pharmacogenomics work performed at GlaxoSmithKline. There's actually still a primary investor with our company to this day. And as in terms of software that we provide we have two flagship products: one is VarSeq, and one is SNP and Variation Suite or SVS.
Darby Kammeraad VarSeq is meant to be this tertiary analysis tool that is the kind of primary environment where you're gonna go through and annotate and filter your variants, but then also detect the CNVs and filter those as well and go through those ACMG and AMP guidelines in VSClinical. And the ultimate goal is to get down to a clinically relevant or clinically actionable variant hopefully that you can then put into a clinical report that can be customized to your specific use case. And now the focus of today's webcast, of course, is going to talk a little bit about this relationship between VarSeq and VSWarehouse. And so VSWarehouse is this genomic repository where all of this processed data in VarSeq ends up. And so it not only has the projects as well as these catalogs where you're storing these interpretations but also these reports. And it's a good centralized location to not only store the data but also query it and then have it accessible for anybody who wants to go in and view that data. Even collaborators who are simply just going to look at the results. And then alternatively SNP and Variation Suite is kind of our large scale research platform for these large-N population studies, methods like a genome-wide association studies, genomic prediction, imputation, as well as CNV analysis and some options for RNA-Seq as well. But a little bit more on the kind of the result of having been around for 20+ years. One of the benefits to that is that we've actually been cited in thousands of peer-reviewed publications over those years, and this includes reputable journals like Science, Nature and Nature, Genetics. And you know, this is just one testament to kind of our application and our use base for our customers. And of course, another testament to that is the customers themselves. And so we work with over 400 organizations all over the world. This is including top tier institutions, government organizations, clinics, genetic testing labs with well over 20,000 installs and 1,000s of unique users across this organization.
Darby Kammeraad So the main question is, why is all this relevant to you? Well, over the course of those 20 years and that large scale use base for all of our customers, we absorb a lot of that customer feedback and it helps us produce the refined top quality software that you guys all rely on. And in addition to that customer feedback, we always want to have our finger on the pulse of industry needs so that we can keep maintaining making that software as relevant as possible for even more users over time. The nice thing about this, too, is with the annual license subscription that you get to the software, we don't charge per sample, which I know is something that's kind of unique. So you could run as many samples into the VarSeq software, for example, as you would like during that subscription time, and we don't charge anything extra. Additionally, you have full access to the training team and support team. So I myself or any other FAS on the team are always willing to hop on a call with you or even answer questions via email to get in and help you troubleshoot any issues that you're having, any roadblocks or just simply giving some basic training and getting up to speed quickly. So we want you to spend more time doing analysis and less time having to worry about learning how to use a new software.
Darby Kammeraad And then one last kind of general higher view slide that I'm going to cover here is the capability that users have to not only do this tertiary analysis that we're talking about with VarSeq, but through our partnership with and users also have the option to go through and do the alignment and variant calling steps from the FASTQ files as output from their sequencer. So with this full stack capability, you can essentially start with FASTQ, produce that BAM and VCF, import that data into VarSeq, detect the CNVs based on the cover data in the BAM file, annotate and filter those variants, get down to a subset that you might want to pass through those ACMG and AMP guidelines and then get down to a final clinical report. And then lastly, being able to store all of this content in VSWarehouse, but then be able to navigate through it effectively, which is gonna be the focus of today's presentation with that kind of direct effort and how you can navigate through that content with the AMP guidelines.
Darby Kammeraad And last little general slide I'll have here in terms of VarSeq, I just want to talk a little bit about its versatility and its usability. One is it's a very scalable tool. So this is accepting everything from gene panel data to whole exome or whole genome data, single sample, hundreds of samples. It's incredibly scalable. And the nice thing about it, too, is you're not limited to just simply looking at a list of variants in a spreadsheet. There's a lot of good visualization capability with GenomeBrowse so that you can visually see what's going on with your variants at the same time that you're just processing them through the workflow. Additionally, one of the nicest, probably more powerful features of the VarSeq software, is that when you go through and you set up your workflow of how you want to prioritize these variants or filter these variants, that workflow can actually be saved as a project template.
Darby Kammeraad And that saves you the time so that you don't have to keep going back and building a project from scratch. You simply use the template that you've built up, import your data and it will automatically run through that workflow to give you a filtered set of variants that you might want to process through those guidelines, for example.
Darby Kammeraad Now, recently, we've actually provided an upgrade to VarSeq. So now we are currently at version 2.2.0. So if any of you are currently using the software and you haven't made this transition to 2.2.0, I definitely would recommend it. We've polished a lot of things, kind of fixed some issues that we just inherently thought could be improved. But we've also added a lot of new features. So some of those features include updated amino acid change displays so it's easier to kind of interpret what impact your variant's having. Sample import control options, so selecting which samples to keep or remove that of an imported set into the project. And then another powerful capability with the allele count algorithm is being able to say, "out of a shared set of variants that I have in a cohort project, which samples have that variant?" And I can very easily isolate which samples I might want to investigate maybe via guidelines, for example. And we've also added some kind of efficiently accessible plotting features and genome browser to get to some common databases that most people would probably want to visualize anyway. And one of the other powerful things that we added is all the pre-built project templates that we ship with the software natively came in a GRCh37 assembly format. But now we've built up the 38 format so that anybody who's using the GRCh38 variants, they can use those templates as well. And then lastly, it's gonna be the focus of today's conversation is looking at those variant interpretations from somatic guideline processing in the VSClinical tool with the AMP guidelines from the warehouse perspective.
Darby Kammeraad So before I jump into the details of all of that, I wanted to kind of take a moment to talk about the general key concepts and value points of VSWarehouse overall. So this VSWarehouse is a genomic repository that's going to store all of your genomic data and provide a simple but really powerful querying and leveraging of all that archive data or all of that archive content. So one easy way to really explain how VSWarehouse can be utilized is to kind of pose some questions, or ask some questions, that commonly occur within large scale tertiary analysis pipelines.
Darby Kammeraad So, a good example would be when you're trying to narrow the search to clinically relevant variants, a common question that might come up is have I seen this variant before? And if so, how did we classify it and how did we interpret it? And this question is really best answered when we take a look at those warehouse-based catalogs that are meant to capture those ACMG and AMP guideline interpretations that are processed through VSClinical. We'll take a look at that when we get into the VSWarehouse browser and the VarSeq project. Another common question can be how can I eliminate common variants that I might see against the cohort of samples that I have? So really, users benefit from a direct exchange of all that cohort data that's stored in VSWarehouse and that can be utilized from a VarSeq workflow perspective to set up an easy filter to say take out any variants that are in my cohort that might have highe allele frequencies or in other words, might be common. Another great question would be if I have minimal evidence available for the variant that I'm currently trying to classify, how can I keep track of that evolving evidence over time? And this is a critical issue that's conveniently handled not only from VSClinical's perspective, but also directly from the VSWarehouse browser. And I'm going to show you a great example of how easily you can look at that data. So VSWarehouse is capable of storing and querying through the complete set of all that sample variant data that's in the VarSeq project, but also storing and querying those variant classifications and interpretations that are captured in the catalogs and also containing those standardized clinical reports that might be built custom for each lab. So what we're gonna do now is talk a little bit more about the connectivity and the relationship between VarSeq and VSWarehouse. So while VSWarehouse is the location for storing all of this data, VarSeq is the primary environment for doing all the variant filtration and automation for these ACMG and AMP guidelines. Now the relationship between VSWarehouse and VarSeq is essentially a two way street. They both kind of get to benefit from each other's existence and I'm going to show you examples of how that interaction plays out. So from a VarSeq project perspective, from a VarSeq project, users can upload all that sample data or variant data up to the warehouse server. Now, this is a really powerful kind of application because what this is going to do is increase the number of allele counts and a number of allele frequencies to kind of keep growing or increasing this powerful collection of variant allele frequencies that you could use back in a VarSeq project. And that's the next example essentially is from Warehouse, users can go back into the warehouse server, download those allele frequencies and actually incorporate those allele frequencies from your sample cohort back into a filter chain from the VarSeq perspective. And that's a really great way to eliminate common variants that you're seeing across all your samples and just focus in on what would be either rare or novel variants that really matter. And then from Warehouse perspective, this is also where you're gonna go in and create those remote assessment catalogs and reports. So these catalogs are what you would consider your knowledge base for the variant classification and interpretations that are designed specifically for those ACMG and AMP guidelines in VSClinical. And the benefit of using warehouse assessment catalogs and reports is that every user is going to be simultaneously accessing that comprehensive set of those variant classifications submitted from all users who have access to that warehouse. And additionally, user accessibility is completely manageable in warehouse. If some users are meant to be given full admin privileges to get access to all warehouse content, or if you simply just want to set up a view access point for collaborators who are just going into a warehouse browser to look at results. So let's discuss that in a little bit more detail with some simple examples.
Darby Kammeraad So Warehouse is gonna be installed on your server behind your own network configuration. And from the warehouse browser, admin users can go in and can have complete control over who has access to what level. A good example would be one group let's call them Group A might have full read and write permissions to their own their own Project A. And likewise, Group B has full read/write permissions to Project B. And Group A and Group B can both leverage the results from each other's projects into their own workflow and keep kind of working off of each other's results. But they might not have full access to be able to go in and modify each other's projects. Alternatively, you might want to provide simple viewing access for those collaborators to go in through the browser. Just go look at those results without modifying anything. So these are really just meant to be some highlights of the warehouse usability and some value points. But let's refine the discussion now a little bit more to focus on those upgraded features with VSClinical's AMP guidelines.
Darby Kammeraad So we've had a number of recent webcasts that have given a lot of attention to this release of the AMP guidelines and VSClinical. And these webcasts provide both high level understanding of the guideline tools as well as the usability in more of like a workflow perspective. I would definitely recommend accessing our library of webcast on [00:15:53]this Golden Helix site [0.7s] to learn more as I'm just going to basically be covering an overview of the AMP guideline interface and how that information gets ported up into warehouse. But however, the overall goal with VSClinical is to essentially standardize and comprehensively capture all relevant criteria evidence for a variant to build the final classification and interpretation. VSClinical will support the review of multiple biomarker types, which includes CNVs, indels, copy number variants, and gene fusions, for example. And then from the back end, a long list of annotations with known clinical submissions, drug sensitivity and resistance, allele frequencies, insilico predictions. They all pool together to impact what would be that final interpretation and classification for a variant or biomarker that goes up into that warehouse catalog. And you can see also the ultimate goal is to be able to not only diagnose what the specific or be able to isolate what the specific biomarker would be, but then be able to supply those treatment options. And we'll take a look at an example of that when we get into VSClinical later.
Darby Kammeraad So this is meant to just be a simple illustration, listing those considerations for the levels of evidence that are going to impact what that final tier classification would be for any biomarker. For example, to reach Tier 1 level classification, biomarkers need level A or level B evidence showing either FDA approved treatments for that specific biomarker or well-powered studies that have consensus. So the assessment catalog retains the user submitted classification tier and interpretation, which will be based on all the evidence at that given moment in time. However, the struggle with this is that building these interpretations for that clinical evidence is essentially it's always limited to what is available the time. But these databases are always evolving. So how do you kind of stay on top of all of that and keep reviewing these variants if you need to, to get these updated interpretations or classifications? So the nice thing is, is that users benefit from the warehouse centralization of these cataloged interpretations so that when new evidence is available, the updated classification and interpretation is simple to capture and universally accessible for all users who are tied into that warehouse instance.
Darby Kammeraad So for today's demonstration, I'm going to start by exploring the warehouse browser. I've just got a few examples of demonstrating how you can access and kind of query and navigate through that data. We'll start with doing some querying through some project data. We'll also do some querying through the AMP based catalogs that are being supported now. And then I'm going to show you some highlights on how to keep track of some changing variant classifications as well, because I always know that that's a critical thing to utilize. And then after that, we're going to switch gears over into a VarSeq project where I'm going to showcase how we can filter out common variants using the warehouse cohorts. I'm going to show you how we can explore catalog variants visually through GenomeBrowse. And then finally at the show, you the access point where all those variant classifications and interpretations are being stored in that warehouse catalog. So before I jump over to the project or as I get things set up, I wanted to remind our users now's a great time as I transition over if you want to enter any questions that you have into the question pane. Feel free to do so. And I'm gonna go ahead and just drag our warehouse browser over to the screen.
Darby Kammeraad So here we go. So here is our warehouse browser. And just to take a second to kind of orient you guys to what we're looking at here. You can see we've got some summary reports of the total number of variants that we have in our server. And if I hover over this, you can see that that scales up to 63,000,000 variants. So pretty hefty sum of variants that we're going to be navigating through. Likewise, if I scroll down, this is where you're going to see all the stored project report and assessment catalog data as being accessed through that warehouse browser. So I figured we would actually start things off by looking at how to query through some of this data from a project level. So let's go ahead and open up this 1,000 whole genome project. And you can see here, I don't have any filters applied. I'm basically looking at the full set of 63,000,000 variants or so. But I want to do some queries to see what some list of variants that I might want to pull out for inquiry. So if I go to the query section here. You can see that the workflow that's stored in the project from the VarSeq perspective is also stored from the warehouse perspective as well. So it's really easy to go through and use any of these criteria to set up however sophisticated of the filter chain you'd like to isolate variants that meet any kind of criteria. And I'll just use a quick example here with ClinVar, where if I wanted to look at the 63,000,000 variants, how many of them do I see that have a pathogenesis classification? So if I click apply filter, within seconds, we'll narrow that search down to 1,300 variants that we might want to investigate. Right. So if I go to the results section here. I've got a list of all those variants here and I could browse through. Likewise, I could essentially go through and export all of this list of variants as a VCF file even against whatever samples or cohorts that I would want to select these variants from, export all of this data and even reimport into VarSeq if I wanted to reevaluate these variants following those guidelines, for example. So just a quick little snapshot way of how you can kind of do a quick little query, but also add to what level and scale of those number of variants that we're storing there. How quickly you can search through that. Go ahead and clear this and now we'll do the same thing, but we'll use it in the context of the somatic guideline catalogs that we're supporting now from the AMP guidelines in VSClinical. So if I go back to my warehouse browser and I scroll down to my assessment catalogs, you'll see here my somatic variants that I'm capturing.
Darby Kammeraad Let's open that up.
Darby Kammeraad All right. So we have some representation of some critical information that's gonna be captured in these catalogs for those guidelines. So not only the genomic coordinates for the biomarker, the interpretation name, whatever transcript it's in, but also, you know, what kind of specific cancer you're looking at as well. In addition to the full level of the interpretation that's being captured, any of the citations, and then that what that tier and list of available drug treatments are, as well as the author who had actually submitted these variants in this catalog. So the nice thing is, is you can get details for any of these submissions listed here, but you can also go out and search to see if any of these biomarkers that you have in your catalog are present in any other projects, reports or catalogs that you're capturing in warehouse. So in this case, there isn't much here for this specific biomarker. But another thing that we can do with this, if we ever wanted to go query on these catalogs, maybe specifically for a certain cancer type, let me show you how that's done.
Darby Kammeraad So just like what we did with the project level data, if I go to query. And I open up our catalog here and let's say I want to do a search for a specific cancer and let's just say it's for lung. Do anything that contains that. To apply that filter here and then I get those results of however many of those biomarkers that are in that catalog that overlap with, in this case, non small cell lung cancer, for example. So that querying capability on the project level is routinely the same of what you would do in the catalog level. And you can see very easily how you'd access or even search upon any of these criteria back at the whole warehouse kind of perspective or project level, searching for anything that's in the projects or catalogs or reports. So another thing that I had brought up that's always a critical thing to review, how do I keep track of changing clinical evidence for a variant? And then how can I easily go back and kind of navigate to a project or any of the content that I have on warehouse to reevaluate that variant? And I'll show you a really good example of this.
Darby Kammeraad If we scroll down past all of our project, report and catalog information, you'll see here ClinVar changes. And this is a really good example of how this could be done, where we basically show you the new variants in the variants that have changed that are either new or changed in ClinVar that already exist in your cohort. So a good example of some kind of extreme scenario is a variant that might have had a stale interpretation, beginning with conflicting or uncertain significance, has been updated to say, oh yeah, this is likely pathogenic based on what's in ClinVar. I need to be able to go back out to that project, see the variant and all the details that I have for it, but also be able to keep track of any of the samples that would have that variant and go back and reevaluate them maybe through the ACMG guidelines or AMP guidelines, depending on the nature of the variant that you're looking at and see what kind of classification or interpretation I come down to based on that updated clinical significance from ClinVar. So there's a lot of power and what you can get away with quick searching through cohort data from the warehouse browser itself. So now that we've touched on some highlights there, I'm going to transition now into a VarSeq project where we can look at how we can leverage some of the warehouse content from a workflow perspective. So here's our VarSeq project. And for anybody that maybe hasn't seen VarSeq if this is their first time exposure. I can just orient you really quickly with what we're looking at here as well. So in any VarSeq project, you're gonna be importing the list of all the variants that are in the VCF file. And you can see in this case we have a total summed value of 126,000 variants across four samples. Now, the nice thing is, like I said, will it be importing all of the data that's in the VCF file so that includes things like quality fields, like filters pass read depth, genotype quality, for example. But once all of that's into the project very easily, you can start annotating against all that data to get any kind of context of which genes you're in, if there's any clinical submissions with ClinVar, frequencies, how you want to leverage all of these individual data fields in your filter chain or in your table into the filter chain. And so the first example I have in this filter chain is filters pass where I've taken this field here. Right Clicked. And added it to the filter chain. So now as we kind of navigate down through this workflow, we can see what kind of impact each of these criteria are having and filtering down to the subset of 8 variants that I might want to evaluate. Right? So the first set of the total number of variants is a 126,000. Then the next criteria things filter pass gets us down to 58,000 within this specific sample. Beyond that, I'm looking at the quality fields again withread depth and genotype quality to say anything greater than 200 read depth or 90 for genotype quality to get us to 57,000. And then beyond that I've also actually included variant a little frequency to look for anything that's got low read depth counts for variants in this set, which can be somatic by nature. So I'm setting this criteria to say look for a variants to have a variant allele frequency of anything less than 10%. That gets us to a 157. And then beyond that. This value of a 152 is actually leveraging our first field from the warehouse project data, and I can show you where we got access to utilize this criteria in our filter chain. So if I click on this V-connect icon. This terminal is really the access point from VarSeq to warehouse, where you can upload project data up to the projects that you already have in the warehouse server or create new projects. Also access those standardize reports that are available from warehouse as well as those catalogs that you might have for the somatic or ACMG guidelines, for example. But then additionally you can take any of this data, whether it's project data, catalog data, reports... And you can annotate against it. And that's essentially what I've done here with this 1,000 whole genome project is I've selected it and then I added it as an annotation where that's out on the table here. If I scroll to the right.
Darby Kammeraad Sorry, GoToMeeting tends to use a lot of my memory at the same time the software does. There we go. So at this point now I can use this 1,000 whole genome project level field for allele frequencies. And I've added this to the filter chain to say I want to look for only variants that are essentially rare at less than 10% or novel missing from my project or missing from my cohort. So that gets us down to a 152. And then beyond that, I am basically just finding anything that's known to be in CiViC or COSMIC down to 25. Anything that matches this primary site of skin that I have here for this specific sample and that gets us down to 8, and then 8 of those of course are all confirmed somatic based on the COSCMI database. So if I scroll over here, you can see that I've got one variant flagged that I'm going to go through and do evaluation for following those AMP guidelines. But what I also wanted to show you, too, is how to visually access any of the not only overlapping biomarkers that we've interpreted and classified before that are in the catalog, but then also basically see if there's anything nearby. And that actually adds a lot of context for, you know, for example, maybe you're looking at a loss of function variant that you don't really know a lot about. But then you find out upstream of that variant, you find another biomarker that's loss of function that you labeled as pathogenic or oncogenic. That can be a really helpful context to look at that gene overall. So if I wanted to go through and plot this catalog, I can go back to my V-connect icon here and go to my somatic variants track here and plot that and GenomeBrowse. So now we have a really good representation of not only the allele accounts or the variant counts here at this site for the specific low allele frequency biomarker that we're going to be processing in the guidelines. But now we have this somatic variant catalog up above. So we can see I've already got this variant actually submitted into the catalog, but there were other submissions for different tumor types that were captured prior. And so if we go ahead and just close this, let's now switch gears into the AMP guidelines and focus on this specific biomarker's interpretation.
Darby Kammeraad So we'll go here to our AMP guideline tool and I'll shrink my filter chain. And maybe even zoom in a bit. So the beginning stage of the guideline tool is going to take you through this, filling out the sample and patient level information, which is all going to go into that final clinical report which is here at this last tab in this interface. But you also are going to set the trajectory for how you want to capture these interpretations, both on the biomarker and the gene level. So what is the tissue in the tumor type that you're working with? And in addition to that, any sample statistics or coverage statistics that you want to incorporate into that clinical report as well. And the nice thing about this is being able to label what would be any regions that failed to meet that coverage threshold. So once this is all set up, we go into the mutation profile section. This is where we go in and select the variants that we actually want to evaluate. And you can see here small sort of variants. This will be your SNVs, indels, which you can either bring in from the project or manually bring into the AMP interface. Additionally, we can do that the same thing with the CNVs as well as manually bringing in this gene fusions for interpretation also. So we'll stick with this BRAFV600E biomarker that I have here and go into the variant section to assess that oncogenicity. And so this is a great point in time to not only review lightly how we get to this final academic score, which is basically a summation of all these different criteria for all these relevant areas of databases or algorithms that we're using. So, for example, SC+3 somatic catalogs plus three. How frequently is this occurring in a somatic catalog? And you can see it's up to past 28,000 samples in COSMIC. So you go through and you assess each of these criteria obviously to get what that final oncogenic value would be. But you also want to build what would be your final interpretation and the scope. So, you know, whether you're looking at a general BRAF missense or anything that's specific to the biomarker and for what tissue type that you're a tumor type that you're looking at. Now, the nice thing about this is when you go through and you build up your interpretation, obvious things might change over time. You have to add new details to this and I'll just make a slight change here with adding some literature that might have popped out for a publication, if I copy that reference and add it in here. You can see where I get to go and review what I want to save into my assessment catalog. So for any of those changes that I would want to add to that now, whether where that catalog resides is actually from warehouse, right. So if I go click on this cogwheel here at the top, then we set up our kind of options for running the AMP guideline tool you'll see here. Not only do I have my warehouse space with this purple V icon somatic catalog, but I can also utilize an ACMG one for those germline variants which you can process both of these from that same terminal. So once that interpretation is captured and stored, I'm going to go ahead and just discard these changes and keep it as is the next section, after you review the opportunity and build up this interpretation for the biomarker is to go into the biomarker tab and not only do it for the biomarker level of interpretation, but also what you would want to do for the gene. So in this case, for saving an interpretation for BRAF specific to melanoma. If I scroll down further, we're also considering the alteration, frequency and outcomes for that BRAF gene for melanoma as well. And then lastly, what would be the biomarker interpretation for the specific biomarker that we're looking at here? And conveniently placed is a couple of things.
Darby Kammeraad We have the CancerKB database that we're supplying that is meant to be a hub for you to submit your variant interpretations or gene interpretations, up to you, but also just basically becomes this collaborative database where everyone could submit their interpretations with the ultimate goal that anytime you come across a gene or interpret or a biomarker for the first time, you can leverage what's being stored in the CancerKB catalog to help get you to that final interpretation and clinical report faster.
Darby Kammeraad And then beyond that, you can see that I've also got stored interpretations for my own personal catalog of what we're using in warehouse that was submitted from another user in my lab. And so I can leverage any of this information to go into the final interpretation for this biomarker. And then lastly, the final stage, of course, is being able to get down to the list of those available treatment options that you have for these biomarkers. So anything that in this case is specific to melanoma, for this specific biomarker that I'm looking at, what the drug sensitivity would be and if they're all FDA approved. Great. Tier 1 Level A criteria. And in that case, for this specific biomarker. And then I can go through and browse through all these different treatment options of what I would want to list and bring into that clinical report. So then that last stage for the clinical report is just to click on this tab, sign out and finalize the sample, which I can do here. And then go into my Microsoft Word rendering tool here and render this.
Darby Kammeraad And then I could open actually that site for where that report is here. And here is a final clinical report. So you get a sense of how quickly you can navigate through the data from a warehouse perspective. I know I overview to a lot of what you could go into deep detail for the AMP guidelines. It was really my goal just to kind of show you how this all connects up to that warehouse instance. So I'm going to switch back to the slides now. And I think Delaina wanted to add some additional content on our current presence at the AMP conference right now. But I just wanted to remind you guys to enter any questions that you have as well as address this. This acknowledgment for the NIH grant funding that we're, like I said, very appreciative for. So, yeah, with that Delaina, switch gears over to you so that you can update our viewers on our presence there.
Delaina Hawkins Great. Thanks, Darby. And as he mentioned, we'll get ready to take some Q and A so I'll cover some housekeeping items, then you can enter those in. So, yeah. As you mentioned, our team is currently in flight to the AMP 2019 conference in Baltimore, Maryland, starting tomorrow. And we're looking forward to seeing everyone who will be attending. If you personally will be there or if you have any colleagues, give us a shout or tell them to stop by. We will be in Booth 2856. We have a lot of great events happening at the conference. And as always, we will be doing our in booth demos throughout the entire conference. And for those of you who are not familiar with what those demos are, our team will be presenting a variety of five minute talks that cover different parts of our clinical suite. And then these demos are also a ticket to one of our infamous t shirts that you may have heard about or seen on social media. And then in addition to what is happening in the booth, Golden Helix president and CEO Dr. Andreas Sherer and our V.P. of Product and Engineering, Gabe Rudy will be giving two innovation spotlight talks at Stage 1 and the exhibit hall. I will pop you a link in the chat panel with all of the times and topics of our talks, including the in booth demos as well as these innovation spotlights. So if you're at the show, or again if your colleagues are there, we hope you'll join us for any of the events and or just stop by and say hello. But that's all I have for housekeeping items. So just go ahead and get into the Q and A Darby.
Darby Kammeraad Sure.
Delaina Hawkins Question one is how do you control user access in warehouse?
Darby Kammeraad Oh, sure. Yeah, I know I talked about that, but I didn't really show it. So let me go ahead and bring up that browser again.
Darby Kammeraad So from the warehouse browser main view, if you click on this manage icon up here in the top left corner, you can actually go in and manage the project level and change whatever aspects for the projects and permissions that you would want to. And this can literally come down to an individual field level view ability or accessibility. Likewise, this is also where you can go in and set the permissions for any users that would be accessing that warehouse browser. So the easiest way is once you get warehouse installed and you get the browser up to an active state, you can go in and manage all that stuff directly from the browser interface.
Delaina Hawkins Great, next question. Will VSWarehouse integrate with our existing LIMS system?
Darby Kammeraad Yes, it will, and I know that's something else I left out, unfortunately. So I know a lot of users have existing LIMS systems and they're concerned about bringing in a warehouse database or, you know, a solution like this. If there's not going to be any kind of compatibility, but we definitely do support that. So that is a regular thing that we tackle with the installation of the warehouse servers that it has full compatibility with their limb system. So, yes, absolutely.
Delaina Hawkins And then we have one more question. This is two parts. So how does this warehouse scale? And is there any limitation to the number of variants or samples we can store?
Darby Kammeraad Yeah, I mean, you saw when we were looking at the querying through that project that 63 million variants was a pretty hefty sum. It would take a while to get up there, possibly. But we can go well beyond that with hundreds of millions of variants being fully supported. And then on a sample level, I mean, if in terms of if we wanted to put it in the context of a cancer gene panel's kind of scenario, we can easily get up to the hundreds of thousands of samples that would be supported in that in that warehouse server as well. So, yeah, a pretty, pretty hefty sum amount of data that you can capture and retain in your warehouse server.
Delaina Hawkins Wonderful. Well, if that is it for questions from our viewers, then I think that will conclude today's presentation. As always, if you guys have any other questions that come up later on, please don't hesitate to reach out and we will be happy to talk with any of you individually. So thank you, Darby, for this great webcast, of course. And thank you to everyone else who joined us today. I hope you all have a great rest your day and hopefully see you guys at AMP.
Darby Kammeraad Goodbye, everyone.
