Transcript
*Please note that you may experience errors in the below transcript, therefore, we recommend watching the video above for full context.
Hello, everyone, and welcome to this Golden Helix webcast series. On behalf of the entire Golden Helix team, we hope you and your families are staying safe and healthy and in good spirits. I'd like to thank you for taking the time to join us today. My name is Delaina Hawkins, Director of Marketing and Business Development, here at Golden Helix, and I am pleased to be the moderator for today's presentation. I would also like to thank Julia Love, who is our presenter - thank you for joining us. She is one of our Field Application Scientists. Julia, thank you so much for joining us.
Hi, Delaina. Yeah. Happy to be here. And and just echo you a bit, I hope everyone's staying healthy during this time.
Definitely, before I pass things off to you as a reminder, we will be on hand to ask any questions you might have at the end of Julia's presentation. So please enter those into the questions pane of your control panel demonstrated on the screen here. That will be it for me now. I'll be back later to talk about some recent events from Golden Helix, including some COVID-19 assistance resources we have for you. So stay tuned for that. And Julia, I'll pass things off to you.
OK, great. Yeah. Hello, everyone, and thanks for joining us today. Truth be told, I had a lot of fun putting this webcast together. While researching various disease and cancer workflows was interesting, what I enjoyed most is that much of the webcast content primarily comes from my experience working with customers. So something I hear all the time is, "Julia, I really like VarSeq, but how can I select variants based on phenotype? Or how can I find common variants between my samples?" And so customers are asking me these questions quite often, even if they're just getting started and becoming acquainted with the software. And we do ship basic templates with VarSeq to get users started, but I'm excited to introduce these new advanced templates with more content that addresses many of the questions that we receive and will give our customers an even better starting point.
But before I get started with the demonstration, we would like to give some recognition to our grant funding from the NIH, which we are incredibly grateful for. The research reported in this publication was supported by the National Institute of General Medical Sciences of the National Institutes of Health under these listed awards. Additionally, we are also grateful for receiving local grant funding from the state of Montana. Our PI is Dr. Andreas Scherer, who is also the CEO at Golden Helix. And the content described today is the responsibility of the authors and does not officially represent the views of the NIH. Again, we are thankful for grants such as these which provide huge momentum in developing the quality software we provide.
So now let's learn a little bit more about Golden Helix as a company. Golden Helix is a global bioinformatics software and analytics company that enables research and clinical practices to analyze large genomic datasets. We were originally founded in 1998 based on pharmacogenomics work performed at GlaxoSmithKline, who is still a primary investor in our company. We currently have two flagship products VarSeq and SNP and Variation Suite, or SVS.VarSeq serves as a clinical tertiary analysis tool tailored for basic variant annotation and filtration. But additionally, users have access to automated AMP or ACMG variant guidelines. VarSeq has the capability to detect copy number variations scaling from single exon to large aneuploidy level events. Additionally, the finalization of variant interpretation and classification is further optimized with the VSClinical reporting capability. So users can integrate all of the features into a standardized workflow which can be automated further with batch runs via VSPipeline. Paired with VarSeq is VSWarehouse, which serves as a repository for this large amount of genomic data that is being generated warehouse not only solves the issue of data storage for the ever-increasing genomic content, but also is fully queryable with the ability to define user access by project managers or collaborators. Lastly, our research platform, SVS, enables researchers to perform complex analysis and visualizations on genotypic and phenotypic data. SVS has a wide range of tools to perform GWAS, genomic prediction RNA-seq analysis as well as the ability to process CNVs.
So our software has been very well received by the industry. We have been cited in thousands of peer reviewed publications, which is a testament to our customer base. We work with over 400 organizations all over the world. Some are top tier institutions, government organizations, clinics and genetic testing labs for prevention genetics and ancestry. We now have well over 20,000 installs of our products and 1,000s of unique users. So why does all of this matter to you? This means that over the course of 20 years, our products have received a lot of user feedback, which we immediately incorporate into developing and releasing newer versions of our products. We receive active research grants to support the advancement of our software capability and is typically directed from our user feedback and awareness of the industry needs. We also stay relevant in the community by regularly attending conferences and providing useful product information via eBooks, tutorials, and blog posts.
Your access to the software is a simple subscription-based model where we don't charge per-sample nor per-version. You also maintain full access to our support and training staff to get you up to speed quickly with your analysis. Overall, the Golden Helix stack provides the capability to start with an initial FASTQ file all the way down to a clinical report. This is achievable through our partnership with Sentieon who provides the alignment and variant calling steps to produce the VCF and BAM files. These output files serve as the basis for CNV detection and data import for your tertiary analysis in VarSeq. If you do happen to be performing NGS-based CNV analysis, Golden Helix is the market leader supported by studies like Robarts Research Institute showing 100% concordance with MLPA. Additionally, the imported variants in your VarSeq project can be run through VSClinicals automated ACMG and AMP guidelines. Once secondary and tertiary processing is completed, all analysis can be rendered into a clinical report which can be stored in this VSWarehouse, allowing researchers and clinicians to access this information and to view any previous findings.
VarSeq itself is a powerful, flexible and scalable variant annotation, filtering, and interpretation tool. The commercial-grade software is designed to be a local desktop application that is installed directly onto your computer. In VarSeq, you can do more than just look at a simple list of variants in a spreadsheet format. You can also explore your variants visually with plotting capabilities available in GenomeBrowse. And one of the more prominent features of VarSeq is that you can build and run repeatable workflows. So once you have developed the workflow and decided the type of filtering you like to do and which annotation sources you would like to include, you can save that workflow as a template and apply it later with new data, which can really streamline your analysis time. So with that, let's talk more about project templates.
So Golden Helix ships a variety of templates with VarSeq that are designed to provide a starting point for users to evaluate variants. They contain prebuilt filtering logic, much like the one seen here in the image on the left. They're created by leveraging fields from the annotation sources and algorithms. And naturally, as users to become more familiar with the software in these shipped templates, there is a desire and a necessity to tailor the template design to accommodate more thorough variant analysis. And these template customizations include using several algorithms and annotation sources available in VarSeq that many users may not know about. And this webcast will explore the tools that are available in more advanced templates and provide ideas for users to create templates that are more inclusive for your variant analysis. And so we will discuss the template design in the context of two workflows, cancer and germline diseases. This reminds me, Delaina, I wanted to take a quick poll to get an idea of who is working strictly with germline diseases, germline or somatic cancers or maybe both.
Perfect. We will go ahead and launch this poll.
Julia, I know there's a little bit of a delay, so we'll just have to give everyone a moment to answer.
OK, awesome.
All right. It's like the answers are coming in, so I'll go ahead and close this poll.
All right, so looks like a lot of germline diseases and cancer, but a little bit a mix of everything which is perfect. So this webcast will pretty much cover the workflows that a lot of you are working with. That's great. All right.
So let's start by looking at the tools available within VarSeq that can be used to generate our customized template. So first, annotations. Golden Helix spends hours curating high-quality annotation sources according to strict specifications. Unfortunately, we won't be able to cover all the annotations available in VarSeq during this webcast. As you can see here in the image on the right, there are several to choose from. However, I did want to introduce a few annotations that are extremely useful and will be incorporated into the templates that we discuss later. So let's start with GnomAD.
So GnomAD is a resource that aggregates whole exome and whole genome sequencing data and provides detailed information on variant counts and frequencies across different populations.
One of the metrics available within GnomAD that I want to mention is the alternate allele frequency, which we will see used later in the templates to narrow the search for variants to those that are rare across populations.
Next is OMIM, which is a catalog of human genes, genetic disorders and traits focusing on the gene-phenotype relationship. Though you can utilize individual fields from home to search for variants based on disorder or phenotype, I will discuss in more detail later how OMIM phenotypic ontologies can be used within a phenotypic prioritization algorithm called phorank. Next, we have COSMIC and CIViC. These are two of our many cancer databases available in VarSeq. COSMIS is a catalog of somatic mutations and CIViC houses clinical interpretations of variants in cancer. In the example template we'll be looking at later, COSMIC will allow us to verify if the variant has been submitted as a somatic mutation according to scientific literature, but also resequencing data from the Cancer Genome Project. And with CIViC, we'll be taking advantage of the metric wherein you can confirm if the variant is in a region in which clinical evidence has been submitted. And finally, dbNSFP - this annotation source is a database that provides functional predictions and scores from functional prediction algorithms for nonsynonymous human SNPs. The algorithms included are SIFT, Polyphen2, Mutation Taster and Assessor and FATHMM. I will be using the predictions and outputs of these algorithms to select variants that are predicted to have a damaging effect on the gene.
And the second set of tools that are available in VarSeq are the algorithms. So like the annotation sources, there are several different algorithms that can be applied to your project and you can see them listed here. But there are a handful of algorithms that we will be seeing later in the advanced template designs. So let's go ahead and familiarize ourselves with those now. First up is phorank, which is a phenotypic prioritization algorithm that ranks genes on a scale of 0 to 1 based on their relevance to a user-specified phenotype. The phenotypes are defined by the gene ontologies and human phenotype ontologies. And this algorithm is helpful for those workflows in which there is a need to select variants that are associated with a specific phenotype or disorder. And we will be leveraging the OMIM annotation source that I talked about earlier to enhance the search for phenotypes. In the image on the right, I entered in disorders associated with cancer predispositions such as Cowden's Syndrome and Lynch syndrome. And these variants that are on the edge here around .8 to 1 are the most relevant genes for these phenotypes or disorders. The next algorithm is the count alleles algorithm, which provides alternate allele counts, genotype counts and allele frequencies across multiple samples. Count alleles is useful in cohort analysis when you want to know how many samples and in which samples have a given variant in common.
Then we have the match genes list algorithm, which will specify whether variants are in genes that match a gene list specified by the user in the template. Later, I will show how the match genes list can be used to set up gene panels for different gene sets associated with various conditions. In the example shown here, we can see hereditary anemia, spastic paraplegia and hereditary cancer lists were applied. Finally, we have the ACMG classifier algorithm that calculates classifications for each variant based on the 33 criteria that make up the ACMG guidelines. You'll be using variant classifications to selectively analyze variants that are not benign or likely benign, according to the ACMG guidelines. In the image, you can see that there are several outputs of the ACMG classifier, also including the inheritance model, which we will talk about later as well.
So let's keep in mind that the goal of the shipped project templates like the ones seen here are to minimize the amount of effort by the user upfront by providing a good starting point to begin variant evaluation. But also the templates help to maintain consistency with evaluation as future samples are obtained. So the templates achieve this because they automatically load in your specific annotation sources and algorithms like the ones I described earlier into the project even before your data has been added. In just a moment, I will transition over to the VarSeq demonstration, where I will discuss how to expand on some of the more basic templates and generate templates that are more relevant to how our customers select variants for evaluation. In each case, we will start out with a more basic template and discuss the limitations. And then I will show the more advanced template that will go into more depth for filtering and evaluation.
[00:17:02] So before I open up VarSeq, Delaina, let's take another quick poll. So I understand that there are many clinicians and researchers with us today for the webcast. And some of you are working with gene panels, some with whole exomes and some with whole genomes. So I'm curious to know what size data everyone is working with so I can include some helpful insights based on data size as we go forward.
That poll is live. It looks like people are submitting their answers will give you a few more moments to select which one you're working with. All right. Julia, it looks like we collected most responses, so we'll go ahead and share those results. Awesome. OK.
So. OK. Quite a mix, which I guess I'm not surprised, but that's great. So we'll be able to address each of these. A lot of whole exomes, that's pretty cool.
OK. So now let's go ahead and I will open up the software. All right. So when we open up VarSeq, I first want to show where you can access the project templates. So if you go to create a new project, you can see the template options available here. And so whether they are the shipped VarSeq templates or the ones that you create on your own, they're all going to be listed here as choices when you're creating a new project. As you can see here, these are the two more advanced templates that we're going to be discussing. But I'm actually going to start out talking about this top one, which is the ACMG guidelines gene panel template that comes shipped with VarSeq. And so I'll go ahead and open that up now.
All right, so I want to spend a moment orienting you all to the VarSeq interface, as some of you may be seeing this for the first time. So in the top right hand corner, we can see the number of samples, which is four. And the number of variants that have been imported to the project from the VCR files, which is around 57,000. We can see those variants listed here on a per row basis in the variant table. Also in the variant table are the annotations and algorithms that were loaded into the project when we selected our project template. And on the left hand side of the screen, we can also see the prebuilt filtering logic for the ACMG guidlines gene panel template.
And so at first glance, we can tell that this filtering logic is very basic and ultimately doesn't narrow our variant search much considering we still have 100s of variants to analyze with the ACMG guidelines. This demonstrates well the issue of having a basic template that lacks details from annotation sources. And actually when we take a closer look at the filtering logic, we notice that in the first several filters are based on just variant quality. It's not a bad thing as we do want to ensure that we're looking at quality variants. So that would be those that pass the quality filter from the VCF file have sufficient read depth with a threshold set at 50, has sufficient genotype qualities so we can have confidence that the genotypes assigned are correct, and it ensures that we are looking at heterozygous and homozygous calls and not reference calls. We do see a huge impact of the last filter card, which is the ACMG classifier algorithm. So we're keeping those variants that are classified as likely pathogenic, pathogenic or weakly pathogenic. And we can see that this filter chain narrows our search for variants from 57,000 to 678. Now, I don't know about all of you, but 678 variants is still a lot of variants to analyze with the ACMG guidelines. Also, our dataset is four exome samples. So you can imagine perhaps that if you have a larger dataset with millions of variants or more like those of you who are working with whole genomes, that you would likely still have 100s, maybe 1,000s of variants after applying only these filters. And so like I mentioned earlier, this filter chain focuses greatly on quality, and it isn't until we incorporate an algorithm that we begin to see our scope truly narrow.
And this template really isn't taking into account the numerous annotations and algorithms that are available in VarSeq. So now I want to transition over to the advanced template that will not only significantly limit the number of variants to investigate, but we will also be able to leverage more annotation sources and algorithms so we will have a more detailed understanding of our clinically relevant variants.
So I'll go ahead and open up. OK, so we can see that this template has more filtering steps and it successfully narrows the 57,000 variants down to 10 variants that we could evaluate further with the ACMG guidelines. So let's see what we can learn about these variants by examining the filtering logic associated with this more advanced template.
So notice that we do have the same quality filters as the previous template. However, they are grouped in what is called a filter container. And so in this filter container I have selected the and logic. So the variants will pass through each filter card in sequence and filter containers are particularly useful and complex workflows to group, to organize, sorry, to organize the filter cards. But it also makes manipulating the filter chain much easier. For example, I can move the entire filter container and not have to with all of the individual filter cards in there instead of having to move each filter card individually. Next, we have another filter container for our population frequencies. So not only are we using GnomAD to leverage alternately low frequency, but also one 1KG Phase 3 and ExAC. And in each filter card, the threshold is set to less than or equal to 1% or missing from the database. So we are looking at those variants that are rare or completely novel in the population. I also want to point out that we are using the OR logic instead of the AND logic. So instead of the variants being filtered through all three population tracks in sequence, you can see the filtering result of each population database individually. So for example, if we look here at the filtering results of GnomAD, we have 4,940 variants, but 1KG phase 3 leaves us with 4,015 variants. But the number at the bottom of this filter container reflects the total number of variants that passed any one of these filters, leaving us with 6,064 variants that are both rare in the population and of high quality.
And so the template next includes some specificity with gene panels and phenotypic prioritization. These three gene panels, hereditary anemia, spastic paraplegia and hereditary cancers were produced using the match genes list algorithm. And so each panel contains a list of genes that are relevant for the corresponding phenotype. Also included is a phenotypic prioritization for cancer predisposition phenotypes using the PhoRank algorithm. And I have that threshold set at .9 and higher to include those genes that are most directly related to the cancer predisposition phenotypes that I entered. As a side note here, with any of these templates, you can change these gene lists or phenotypic ontologies so they can be customized to your workflow. But if I scroll over in the variant table to the output of my gene lists and my phenotypic prioritization, we can see the list of genes that were used to produce each one of our gene lists. And then for our PhoRank calculation, we can see all of the phenotypic ontologies that we're included as well. And so after adding these gene lists and the phenotypic prioritization that significantly brought down our search to only 241 variants. And so the next filter card allows us to search variants across all four samples in the project with the count alleles algorithm. So in this case, perhaps, all four of my samples are individuals with Cowden's Syndrome and I want to look at variants that are in common among these individuals.
So to do that, I have entered three in here to find variants in common at least three of my four samples. And in doing so, we're left with 76 variants. Next is dbNSFP, and it was used to look at SNPs that are predicted to have a damaging effect by four or more functional prediction algorithms. And I have also selected missing in case there are not functional predictions for a certain SNP. And then also if you wanted to capture loss of function and variants, for example, selecting missing would allow those variants to be included as well.
And so the last two filters come from the ACMG classifier algorithm. The first is that we want to include variants that follow a dominant inheritance model. And then the final filter card filters variants based on classification.
So in order to capture interpretation on variants that are anything but benign, I have inverted the filter card so that benign and likely benign variants are excluded. And so using the more advanced template design has left us with 10 variants that are: of high quality, rare in the population within my phenotypes of interest, are common in at least three of my samples in the cohort, are predicted damaging by functional prediction algorithms, and are dominantly inherited and are not benign. So through all of this variant annotation built into the template, we now know a lot of details about these variants before even starting evaluation with ACMG guidelines.
So at this point, let's switch gears a bit and take a look at the cancer gene panel templates. I also want to remind everyone to enter in any questions that you have into the chat panel. Here is our cancer gene panel template that comes shipped with VarSeq. So of course, we start with the quality fields that we know and love, the pass filter from the VCF file and read depth. But we are also including the variant allele frequency, which sets a minimum threshold for the number of reads detected for somatic variants. And then last, we include variants that are known somatic mutations in the COSMIC database, and we see that in just applying these filters, we still have 55 variants and we don't have a lot of information about these variants. This again is a very basic filtering logic wherein three out of the four filters come from your VCF file and not algorithms or annotation sources.
So let's go ahead and check out the advanced cancer template and see how different annotations and algorithms are applied, and if we can't get a fewer number of variants to analyze and hopefully acquire more information about the variants.
All right. So, again, after filtering on quality, we return to our population databases. However, instead of looking at the alternate allele frequency across all populations, this time we're looking at subpopulations to find specific variants, variants that are specific to East Asian, African and European populations individually. I have set a threshold of 0.1% to capture those somatic variants in which we expect to be very rare across population databases, if not missing entirely. And next, I have my cancer gene panels that were produced from the match genes list algorithm for breast cancer, colorectal cancer and prostate cancer.
And this, again, significantly reduces our number of variants all the way down to 11 for further analysis.
Moving on, we notice the ACMG classifier algorithm again, excluding benign variants. However, in this case we are looking at the variant classifier instead of the sample classifier, and this distinction is important in cancer analysis as the variant side classifier does not consider genotype of the sample. So we are now removing those variants, whether there are germline or somatic that do not have a function like intronic variants for example or are common in population catalogs. And so lastly, this template wraps up by leveraging fields from cancer databases, CIViC and COSMIC. So depending on your use case, you may utilize the cancer databases a little bit differently than we see them used in this template. But in this case, I'm searching for those tried and true variants that are known cancer mutations. This is shown with these three variants being known mutations in COSMIC. And then these second filters are from CIViC.
The first one is looking at variants in clinically relevant regions, meaning these variants may not be submitted with clinical interpretations into CIViC, but they are located in a region that has been implicated to be clinically relevant, even without those variants submissions in which we see are all of our five variants are within these regions. And then we can see that three of these variants are in genes that have clinical interpretations submitted to CIViC.
And so we successfully narrowed our search from 127,000 variants down to five that we can now investigate further with the AMP guidelines. And I wish I had more time to time today to evaluate a variant or two with the AMP guidelines with all of you. But I do want to make sure that we leave enough time so that we can open up the discussion for questions. But this would be the final step for variant evaluation.
But we do have several other webcasts available on our Golden Helix Web site that cover both pathogenicity and oncogenicity scoring for the AMP or the ACMG and AMP guidelines respectively. And I encourage you to take a look at those webcasts as they would cover variant analysis all the way to generating a clinical report. But hopefully, these templates have shown how there are several different ways to include VarSeq annotations in algorithms in your workflows and you can narrow your search more effectively by using them.
Oh, one more thing that I wanted to show everyone is I wanted to let you know that these two templates that we went through today can be accessed through our template repository site. So we go ahead and pull that up.
So you can download these templates and customize them to your own VarSeq projects. And as we come across new workflow ideas, we will be adding new templates into this repository. And then additionally, in the spirit of sharing best practices, if you have a template that your lab uses that you think may be a helpful guide for other labs, you can submit the templates to us and we can share them in the repository as well. And so let me pull up that site for your reference.
Then you go. Sorry. And like I said, I was really excited about these templates because I really do think that users will be off to a better start with using some of the annotations and algorithms that are available and will give users the opportunity for a more complete workflow.
And so with that, I would also like to mention again how grateful we are for our funding that we received from the NIH as it allows us to continue developing the quality software that we provide.
And just one more quick reminder. Be sure to enter in any last questions now. But with that, I will turn things back over to Delaina as she is going to talk about some marketing content before we start answering some questions.
Great, thank you. Julia, Like she said we had a lot of great questions come in throughout the presentation, but if you have any last minute questions to pop in there, go ahead.
And as we wait, I will highlight some recent updates, starting with our latest eBook titled Genetic Testing for the COVID-19 Virus and Other Pathogens. This eBook was written by Golden Helix President and CEO Dr. Andreas Scherer. And in this he summarizes to the best of his knowledge our current understanding of COVID-19 and how Next-Gen Sequencing can deliver significant insights in this quickly evolving area.
And then secondly, I would just like to inform our audience about Golden Helix's COVID-19 Assistants Resources, which includes this eBook I just mentioned, but then also we have developed our remote assistance program, which is intended to help any of our customers who are temporarily working remotely and might not have access to their machines in the office. So if you're currently working from home and need an additional license, you can reach out to our team and we'll be happy to help you with this and get you back up and running.
And then this week we also announced that we will be offering all of our software solutions at an extremely discounted price with extended 18 month licenses for anyone who is researching or working on projects related to COVID-19. So please let us know if you're working in this area and are interested in discussing this further with our team. We're more than happy to assist you with this and I'll go ahead and throw a link into the chat panel here. That'll take you to our site and lists all of these assistance resources and further details. So you can check those out and request assistance on the site there. All right, Julia. It looks like we have - oh, one more. So in light of all of this, obviously conferences have been canceled. And so we have put together our virtual booth for various conferences we were attending: ACMG, AMP Europe, ESHG... So if you head to our blog and check out our virtual booth and watch all four of the short five-minute demos, you'll be entered to win an iPad mini, so definitely go check that out.
All right, moving to questions.
Julia, we'll go ahead and get started. Sounds great.
First question is, how can I save my own project as a template?
Oh, yes, sure. Let me show you how you can do that. So let me open up our project. So once you create a project and load in all your annotations and algorithms, you know, and build up the filtering logic, you can go to the file menu and go to save it project as template. And then you can go ahead and name the project template up here, and then the next time you have new samples, just go ahead and select that template from the list and you should be ready to go.
Next question is, how can I visualize my variants with the GenomeBrowse?
Sure. I can show you that. So if we go ahead and with this little plus icon or this little like plus tab next to your other table tabs, you can open up GenomeBrowse. And so not only can you plot your variants, but we can see that we also have the RefSeq genes annotation source included as well. And the reference sequence. I even have the BAM file plotted here for this sample. And then also you can plot different annotation sources too. So here I have ClinVar classifications plotted. And so when we click on a variant from our variant table, click on maybe this guy and we'll go over and it takes us right to that variant and we can sort of get an idea of the classifications of those neighboring variants as well. So but this does bring up a good point when you do save a project template, you also save this project but the layout of the project as well. So any windows that you want to have open how you want these arranged, anything like that will all be saved into the project template when you save that.
I am working with GRCh38 data, and I'm wondering if there are templates available also in GRCh38?
Yes. So all of the shipped templates are available in GRCh38. So when I go and click create new project, just to be sure here to select GRCh38 instead. And so when I do that you can see that those templates are there. And actually I just realized that this question was asked. It reminds me that I need to make these advanced templates available for GRCh38 as well in that in the template repository because at this moment are only built for 37. So I will make those available as soon as possible.
Thank you, Julia. Next question, when an annotation source is updated, does the new version automatically get incorporated into the template?
Great question. So the templates do not automatically incorporate newer versions of annotation sources. However, there is a new feature in VarSeq that allows you to change these settings when you design the template. So if I go to file and save project as template. If we click on annotation options here, so right now, all of these are selected to be blocked. However, if I deselected RefSeq, for example, every time this template or any project that's using this template will update RefSeq each time it has an update available. It will automatically incorporate that, so. Yeah. Doesn't automatically do it, but it can.
All right, next question. Do you have any automated features for variant analysis that I can use the projects template?
Yes. Yes, we do. This can be done using a VSPipeline. So if you have a large batch of samples, you can use your template and automatically and automate the import and analysis process and then you can even export those resulting variants after all the filtering for reporting or you can go back into that project and do some continued evaluation with VSClinical ACMG or AMP.
Lots of great questions today. And we'll do one more. And does the count alleles algorithm search for variants across all previous projects?
On a project level, no count alleles will search through samples and variants within the project that you're working in. However, earlier I did mention VSWarehouse where in the count alleles algorithm is integral. So you can use count alleles to search across all the samples and projects that you have uploaded to VSWarehouse. But you can also use the count alleles as an annotation source to prioritize variants that say have like a low allele frequency across your data from all of your projects. So yeah, with this VSWarehouse you could, but on a project level, not so much.
Like I said we have tons of questions so coming in, so we'll go ahead and conclude this presentation. And then Julia or one of our account managers will be reaching out to answer these questions that we didn't get to. But, Julia, thank you so much for your presentation and answering all of those questions. Keep an eye out for a link to this recording. If you want to review anything or ask any questions further, we'll look forward to seeing you all on our next webcast. But for now, thank you for everyone who joined. Have a wonderful rest of your day. And thank you, Julia.
Thanks. Have a great day, everyone.
Bye - bye.
