Mavatar Discovery

1\ what is mavatar discovery? mavatar discovery is a licensed, self service data driven research platform designed for pharmaceutical companies, researchers, cros, and academia through mavatar discovery, users access an extensive catalogue of integrated bioinformatic resources each resource is trained on up to hundreds of datasets, enabling powerful insights into tissue centric disease pathogenesis, patient stratification and treatment mechanisms of action read more about the mavatar discovery platform here docid\ zthcc1i0pljrwnbcmbhfe 2\ what kind of data does mavatar discovery provide access to? the platform contains an extensive catalogue of dina (deep integrated network analysis) resources from a wid e spectrum of tissues, and disease specific curated resources from transcriptomics data microarray, bulk and single cell rna seq networks represent strongly preserved gene gene interactions, with readily available detailed functional characterization, based on a database of curated meta data 3\ describe how the data process works? at the core of mavatar discovery is dina (deep integrated network analysis), our proprietary framework built on 20+ years of scientific expertise dina integrates and analyzes thousands of transcriptomic datasets to model disease mechanisms at a systems level—delivering gene level networks by tissue and disease functional enrichment tools with full traceability back to raw data overlapping networks across diseases for broader insights you get curated, structured results—not just raw data or pre packaged database queries new features and capabilities are added continuously as we shape the future of research our approach also ensures high reproducibility, enables more accurate translation between animal and human studies, allows us to compare different diseases affecting the same tissue, and how the same disease is present in different tissues, etc 4\ can i download the figures and in which formats are the data driven insights and reports provided? yes, right click on any figure you want to download, and select the image format you prefer you can choose either png or vectorized svg format all images are exported in high quality, making them suitable for presentations, publications, posters, and other types of sharing you can also customize pre set color theme – to do so go to options tab visible in the left upper corner of mavatar discovery and select theme 5\ can i upload and integrate my own data? we currently support the secure upload of gene lists and their regulation status (e g , upregulated or downregulated labeled genes) for generating graphs learn how to upload a gene search file in our video tutorial docid\ ksbo8wa ixektw7mkinvw or 15 how do i search using a list of genes? docid\ vzvydlul syajo1u6exkh 6\ how fast can users get insights? depending on the complexity of the query, results can be generated in mere seconds, significantly reducing the time required for traditional analysis 7\ how do i generate a new graph (visualize a sub graph)? conduct a new search by clicking the “generate graph” button in the top left hand corner of the canvas, selecting a tissue, general or disease specific network model submit your gene names, choose from our suggested gene sets, search by drug, or search for top interactions in the selected model 8\ i cannot find my desired tissue type or condition how do i request a new network? is the tissue / disease you are interested in not precalculated in mavatar discovery yet? contact us and we will be happy to arrange a project together for a custom data survey and network construction 9\ what is the canvas in mavatar discovery? the space where your network is generated is called the canvas – this is where you will explore & discover new connections between genes 10\ what is a “card” in mavatar discovery? floating on top of the canvas mavatar discovery presents cards with various categories of information a card will always contain information about the network generated in the canvas – eg about the genes, edges or other information regarding what you see in the canvas 11\ how can i explore the highest normalized t values of a full network without selecting a gene? at the top of the generate graph window, click "top interactions" and choose the number of strongest edges you want to generate the network for the graph will then be constructed ased on the edges with the highest t values, which represent how reproduceable the gene co expression was found across included datasets (the higher t value the more consistent co expression was observed) 12\ how does the normalized t value distribution and filtering feature work? after an initial network has been drawn, a histogram of the edge weights among the edges drawn can be seen in the filter card adjust the sliders to limit the edges further within this edge weight span 13\ where can i find a list of all gses available in the platform, and how does it work? we provide a description of datasets contributing to each network further information on datasets contributing to specific edges can be found in the dina functional annotation card connected to the graph 14\ can i search by drug treatment, and how does it work? to explore drug targets, click generate graph and search for your drug of interest using the drugs search bar all drugs and their associated targets listed in chembl are currently available for querying, with plans to expand the drug library in future updates 15\ how do i search using a list of genes? you can upload a gene list as a csv, txt, or excel ( xlsx) file a template is available for download from the file search bar or through this downloadable archive https //discovery demo mavatar com/genefiletemplates zip open the template and modify it to include your genes of interest for details on input format and other specifications, refer to the readme file included in the archive once your file is ready, head back to mavatar discovery and upload your gene list the graph will update just as it does when entering individual genes, generating a network view that reflects the regulation status of your queried genes 16\ explain the gene information card detailed descriptions of genes? when clicking a gene in the drawn network, this card provides a description and some synonyms of this gene, along with the gene type as defined by ensembl 17\ can i search for genes by different tagging standards (such as symbol or ensembl id)? yes mavatar discovery supports both symbol and ensembl id 18\ how can i save my search or filter settings for later? in the top right hand corner of your screen, just above the canvas you have the option to save the current generated network to find your saved searches, simply click the dropdown of saved searches in the top right hand corner of the canvas 19\ what is the patient stratification card and what does it explain? the patient stratification heatmap displays normalized and scaled expression levels from high throughput sequencing experiments contributing to the present network only genes appearing in the drawn network are included in the heatmap in the drop down menus, you can select which patient groups to include in the heatmap, as well as your preferred sample clustering approach genes, and by unsupervised clustering samples, are clustered by upgma method using euclidean distance and samples are annotated based on our curated metadata 20\ what is the cell type explorer card and what does it explain? the cell type explorer displays coordinates in umap space, of cells from an example single cell rna sequencing dataset from the same tissue as the tissue of the current network cells can be coloured by a gene’s expression level by clicking that gene node in the drawn network 21\ how can i compare two overlapping or combined networks? drawing two networks at once (toggle “secondary” in the generate graph modal) draws the edges from both networks, in different colours this provides an overview of their overlap in addition, any single network drawn will be compared to our other networks (using mantel tests on the node node distances) – and the results visualised in the card “dina network similarity” this provides a measure of topology overlap for this gene set in pairs of networks 22\ what is the gene type filter? the gene type filter gives you the option to filter your analysis by gene/transcript biotype classification provided by ensembl ( 1 docid\ vzvydlul syajo1u6exkh ) 23\ can i use mavatar's resources to create my own custom dina resource? yes, you can we employ our pipeline to create a custom dina resource for any combination of public domain, or user provided datasets this will allow a user with a novel dataset from any disease domain to be integrated and compared to existing datasets from the same disease domain or tissue context the most seamless custom dina resource integration requires rna sequencing data (bulk or single cell), but our pipeline is compatible with proteomic or any quantitative data contact sales https //www mavatar com/contact for more information 24\ can i change the graph structure? the graph structure generated will depend on the number of genes and clusters but can be modified manually by selecting and pulling the nodes in your desired directions (click and drag) if you wish to select and move a full cluster, right click on one gene in the cluster and “select local cluster” you can also select multiple nodes by holding the control key (mac) or alt key (windows) and clicking and dragging, or by selecting each node individually one at a time 25\ how can i avoid two unconnected clusters from overlapping within the network? the network structure is automatically generated, but you can identify and move a full cluster by right clicking one gene within that cluster and "select local cluster" then pull the selected cluster away from the other 26\ what does the edge thickness represent? the edge thickness represents the normalized t value, where a higher correlation t value gives a thicker edge thicker edges therefore indicate greater confidence in the reproducibility of gene co expression across the datasets used to train the network 27\ what are the query genes? the query genes are the input genes from which your graph is generated they can be a specifically defined set of genes, drug targets, or genes associated with go terms, diseases, or included in other mavatar curated gene lists within the graph, these genes are highlighted in grey using default theme settings and tagged as queried gene within the gene information card references ensembl biotypes (available at https //www ensembl org/info/genome/genebuild/biotypes html https //www ensembl org/info/genome/genebuild/biotypes html )