Training

GeneGo Systems Biology Training (1 hour)

Free on line training are available every first Monday of the month.

United States MetaCore training at 10am PST/1pm EST

European MetaCore training at 10am GMT (London, UK)/11am CET/6pm Beijing, China time

To receive invitations please email training@genego.com.

• November 3, 2008 - Knowledge mining GeneGo content, EZ Search and MetaSearch

  Tired of spending hours searching the public domain building your research objectives? GeneGo now provides a new Google-like interface to search your favorite gene, protein, disease or compound with just one click. In this training session you will learn how to search your favorite gene(s)/protein(s) and obtain a detailed list of references, associated diseases and drugs. We will also demonstrate how to use your search results to further build your hypothesis using network building and gene list exporting functions. GeneGo’s EZ searchable database also includes over 700 prebuilt canonical pathway maps and 842 pre built knowledge networks to facilitate browsing how your proteins interact in key signaling pathways. This session will also take search capabilities to another level where you will learn how to create specific search queries using MetaSearch to generate gene lists for further analysis. Learn how to resolve the following questions:

  1. What are the diseases associated with my gene?
  2. I don’t have a gene list, can I still use MetaCore?
  3. I know my favorite gene is involved in a particular process, but what is the established signaling?
  4. I have a particular gene list and I want to determine if it is representative of a particular pathway or process approach
  5. How do I obtain in list of genes to fit my specific queries: ie: What are the enzymes associated with a particular disease?
  6. What are drugs available to activate or inhibit my protein?
  7. What genes and diseases are targeted with drug?
  8. I have a new compound, are there any similar drugs already available?

  
  
  

• December 1, 2008 - How to choose the right network building algorithm to test and expand your hypothesis

  One aspect of systems biology is to integrate complex interactions of biological systems. GeneGo provides a highly annotated and dense interaction database with over ten different network building algorithms. Here we demonstrate the strength of these tools in the ability to visualize signaling interaction networks and expand on your hypotheses outside of the realm of your core research areas. This tutorial describes each network building algorithm and modeling workflows including building with canonical pathway interactions, with examples of when to use each. In this session we also highlight how to optimize the visualization of your interactions of interest on a network we will build. We will show tools such as how to add/ hide/show objects and how to manipulate visualizations of pathways using post-filters such as disease, tissue, orthologs or gene otology processes.
  
  

• January 5, 2009 - Getting started with MetaCore: Getting the most from your “omics” analysis

  The ability to generate massive amounts of data with “omics” analysis begs the need for a tool to analyze and prioritize the biological relevance of this information. GeneGo provides a solution for using “omics” gene lists to generate and prioritize hypotheses with MetaCore. This tutorial highlights how to work with different types of data (genomics, proteomics, metabolomics and interaction data) beginning with how to upload gene lists and expression data (if available). Here we demonstrate data manager capabilities including how to upload, batch upload, store, share and check data properties and signal distribution. We then focus on how MetaCore uses your gene list to extract functional relevance by determining the most enriched processes across several ontologies. This entails a detailed lesson on how to prioritize your hypothesis using the statistically significance enrichment histograms and associate highly interactive GeneGo Maps and pre-built networks. We further emphasize the role of expression data in your analysis and the ability to visually predict experimental results, associated disease and possible drug targets. Lastly we highlight the benefits of using MetaCore workflows to compare data sets and work with experiment intersections.
  
  

• February 2, 2009 - Knowledge mining GeneGo content, EZ Search and MetaSearch

  Tired of spending hours searching the public domain building your research objectives? GeneGo now provides a new Google-like interface to search your favorite gene, protein, disease or compound with just one click. In this training session you will learn how to search your favorite gene(s)/protein(s) and obtain a detailed list of references, associated diseases and drugs. We will also demonstrate how to use your search results to further build your hypothesis using network building and gene list exporting functions. GeneGo’s EZ searchable database also includes over 700 prebuilt canonical pathway maps and 842 pre built knowledge networks to facilitate browsing how your proteins interact in key signaling pathways. This session will also take search capabilities to another level where you will learn how to create specific search queries using MetaSearch to generate gene lists for further analysis. Learn how to resolve the following questions:

  1. What are the diseases associated with my gene?
  2. I don’t have a gene list, can I still use MetaCore?
  3. I know my favorite gene is involved in a particular process, but what is the established signaling?
  4. I have a particular gene list and I want to determine if it is representative of a particular pathway or process approach
  5. How do I obtain in list of genes to fit my specific queries: ie: What are the enzymes associated with a particular disease?
  6. What are drugs available to activate or inhibit my protein?
  7. What genes and diseases are targeted with drug?
  8. I have a new compound, are there any similar drugs already available?

  
  
  

• March 2, 2009 - How to choose the right network building algorithm to test and expand your hypothesis

  One aspect of systems biology is to integrate complex interactions of biological systems. GeneGo provides a highly annotated and dense interaction database with over ten different network building algorithms. Here we demonstrate the strength of these tools in the ability to visualize signaling interaction networks and expand on your hypotheses outside of the realm of your core research areas. This tutorial describes each network building algorithm and modeling workflows including building with canonical pathway interactions, with examples of when to use each. In this session we also highlight how to optimize the visualization of your interactions of interest on a network we will build. We will show tools such as how to add/ hide/show objects and how to manipulate visualizations of pathways using post-filters such as disease, tissue, orthologs or gene otology processes.
  
  

• April 7, 2009 - Getting Started with MetaDrug: Comprehensive Drug Target Assessment.

  Leverage MetaDrug’s best-in-class molecular interaction database that now includes over 700,000 compounds and their targets. Using MetaDrug we take systems biology to another level to integrate chemistry, biology and build drug target networks and further explore critical biological functions and disease associations of the intended (and unintended) target. This tutorial demonstrates how to search your compound of interest or upload chemical structures to purge for predicted metabolites, similar compounds and their targets. MetaDrug takes your chemistry/biology analysis to the next level by using drug target lists to provide enriched biological relevance of these targets and identity potential biomarkers of toxic effects. Learn how to use our intuitive MetaDrug workflows to compare compounds and predict metabolite activity.
  
  

• May 6, 2009 - Become a Chemist! Understanding and creating your own QSAR models

  GeneGo’s MetaDrug™ tools allow for a complete chemical analysis of compounds. In this tutorial we highlight QSAR modeling methods that relate structural features of molecules to biological activity in quantitative terms. Learn how to assess your compound activity according to GeneGo’s pre-built QSAR tests using Amiodarone as an example. We will highlight how to asses and uses QSAR values for the compound in questions. Learn how to create your own QSAR model as we demonstrate how to generate a training test set with corresponding activities and assign thresholds for a model. Next, we will highlight how to upload your QSAR predictive tools and evaluate the newly created model.
  
  

• June 1, 2009 - Moving Beyond Pathway Analysis: Leveraging MetaBase content and catering your data mining

  Answer you data mining needs with access to MetaBase. Learn how to write structured query language (SQL) defining data mining requests catered to your disease focus. In this tutorial we will review MetaBase content, how the content is organized into tables and relationship between levels of organization.
  
  

• July 6 2009 - Knowledge mining GeneGo content, EZ Search and MetaSearch

  Tired of spending hours searching the public domain building your research objectives? GeneGo now provides a new Google-like interface to search your favorite gene, protein, disease or compound with just one click. In this training session you will learn how to search your favorite gene(s)/protein(s) and obtain a detailed list of references, associated diseases and drugs. We will also demonstrate how to use your search results to further build your hypothesis using network building and gene list exporting functions. GeneGo’s EZ searchable database also includes over 700 prebuilt canonical pathway maps and 842 pre built knowledge networks to facilitate browsing how your proteins interact in key signaling pathways. This session will also take search capabilities to another level where you will learn how to create specific search queries using MetaSearch to generate gene lists for further analysis. Learn how to resolve the following questions:

  1. What are the diseases associated with my gene?
  2. I don’t have a gene list, can I still use MetaCore?
  3. I know my favorite gene is involved in a particular process, but what is the established signaling?
  4. I have a particular gene list and I want to determine if it is representative of a particular pathway or process approach
  5. How do I obtain in list of genes to fit my specific queries: ie: What are the enzymes associated with a particular disease?
  6. What are drugs available to activate or inhibit my protein?
  7. What genes and diseases are targeted with drug?
  8. I have a new compound, are there any similar drugs already available?

  
  
  

• August 3, 2009 - Getting started with MetaCore: Getting the most from your “omics” analysis

  The ability to generate massive amounts of data with “omics” analysis begs the need for a tool to analyze and prioritize the biological relevance of this information. GeneGo provides a solution for using “omics” gene lists to generate and prioritize hypotheses with MetaCore. This tutorial highlights how to work with different types of data (genomics, proteomics, metabolomics and interaction data) beginning with how to upload gene lists and expression data (if available). Here we demonstrate data manager capabilities including how to upload, batch upload, store, share and check data properties and signal distribution. We then focus on how MetaCore uses your gene list to extract functional relevance by determining the most enriched processes across several ontologies. This entails a detailed lesson on how to prioritize your hypothesis using the statistically significance enrichment histograms and associate highly interactive GeneGo Maps and pre-built networks. We further emphasize the role of expression data in your analysis and the ability to visually predict experimental results, associated disease and possible drug targets. Lastly we highlight the benefits of using MetaCore workflows to compare data sets and work with experiment intersections.
  
  

• September 7, 2009 - How to choose the right network building algorithm to test and expand your hypothesis

  One aspect of systems biology is to integrate complex interactions of biological systems. GeneGo provides a highly annotated and dense interaction database with over ten different network building algorithms. Here we demonstrate the strength of these tools in the ability to visualize signaling interaction networks and expand on your hypotheses outside of the realm of your core research areas. This tutorial describes each network building algorithm and modeling workflows including building with canonical pathway interactions, with examples of when to use each. In this session we also highlight how to optimize the visualization of your interactions of interest on a network we will build. We will show tools such as how to add/ hide/show objects and how to manipulate visualizations of pathways using post-filters such as disease, tissue, orthologs or gene otology processes.
  
  

• October 5, 2009 - Getting Started with MetaDrug: Comprehensive Drug Target Assessment

  Leverage MetaDrug’s best-in-class molecular interaction database that now includes over 700,000 compounds and their targets. Using MetaDrug we take systems biology to another level to integrate chemistry, biology and build drug target networks and further explore critical biological functions and disease associations of the intended (and unintended) target. This tutorial demonstrates how to search your compound of interest or upload chemical structures to purge for predicted metabolites, similar compounds and their targets. MetaDrug takes your chemistry/biology analysis to the next level by using drug target lists to provide enriched biological relevance of these targets and identity potential biomarkers of toxic effects. Learn how to use our intuitive MetaDrug workflows to compare compounds and predict metabolite activity.
  
  

• November 2, 2009 - Become a Chemist! Understanding and creating your own QSAR models

  GeneGo’s MetaDrug™ tools allow for a complete chemical analysis of compounds. In this tutorial we highlight QSAR modeling methods that relate structural features of molecules to biological activity in quantitative terms. Learn how to assess your compound activity according to GeneGo’s pre-built QSAR tests using Amiodarone as an example. We will highlight how to asses and uses QSAR values for the compound in questions. Learn how to create your own QSAR model as we demonstrate how to generate a training test set with corresponding activities and assign thresholds for a model. Next, we will highlight how to upload your QSAR predictive tools and evaluate the newly created model.
  
  

• December 7, 2009 - Moving Beyond Pathway Analysis: Leveraging MetaBase content and catering your data mining

  Answer you data mining needs with access to MetaBase. Learn how to write structured query language (SQL) defining data mining requests catered to your disease focus. In this tutorial we will review MetaBase content, how the content is organized into tables and relationship between levels of organization.
  
  

Bench to Bedside Discovery Systems Biology

Free on line training are available every third Monday of the month.

United States MetaCore training at 10am PST/1pm EST

European MetaCore training at 10am GMT (London, UK)/11am CET/6pm Beijing, China time

To receive invitations please email training@genego.com.

• November 17, 2008 - Identify tissue specific pathology and therapies using novel interactions using MetaLink

  Often diseases of an organ, such as the neurological disease, Parkinson’s are caused by manifestation in several tissue regions. Here we demonstrate how novel interactions from different tissues can be used to assign different molecular symptoms to different regions of the brain. We specifically expand on the work of Suzuki et al who obtained expression data on novel Parkin (a gene associated with early onset of Parkinson’s) interaction partners from different regions of the brain known to be affected in Parkinsons. To introduce custom/novel interactions between Parkin and its binding partners from Suzuki et al, we demonstrate how to generate a MetaLink™ file which allows for automatic visual representation of the custom interactions on networks with known interactions from the database. We then demonstrate how enrichment analysis using MetaCore allows for the specific identification of where each Parkin interacting partner canonical maps. Here we also exploit the visualization properties of GeneGo canonical pathway maps to animate between pathways affected by the loss or gain of Parkin expression identify tissue specific trends of functionality.
  
  

• December 15, 2008 - Predicting downstream effects of gene regulation on core metabolism

  GeneGo’s system biology platform incorporates a wide range of cellular signaling components including metabolic reactions. Learn how to visualize signaling interactions between hormones to receptors and downstream cascades to transcriptional regulation, including biochemical reactions, endogenous and exogenous metabolites (i.e. nutritional components). Using diabetes as an example, we will demonstrate how to data to show effects of insulin receptor regulation on glucose and lipid metabolism metabolism through directional networks of key regulators nutritional uptake, storage and breakdown. To complete the analysis we will demonstrate how illustrate drug treatment effects on insulin signaling to facilitate altered metabolism.
  
  

• January 19, 2009 - Finding drug targets from your “omics” MetaCore analysis

  Ever wonder if your favorite gene is the latest target in drug development? MetaBase now includes descriptive drug detail pages of over 700,000 compounds that are accessible through MetaCore. In this tutorial you will learn how to leverage drug and compound information from several analysis outlets using MetaCore. Learn how to use your gene list for drug target look-up, or to search the data base for drugs associated with your favorite gene/ disease or network/enrichment analysis. We will also demonstrate how to search for specific drug classes using our Boolean search tool, MetaSearch (included in MetaCore).
  
  

• February 15, 2009 - Mechanistic toxicogenomic data interpretation using MetaDrug

  Log on to exploit MetaDrug specific ontologies with over 400 pre-built toxicology specific and drug target networks. In this tutorial, learn how to interpret complex toxicogenomic outputs starting with multiple data types including chemical structures. We will demonstrate the power of our statistical tools to investigate your experiment’s impact on specific biological processes, toxicity-related pathways and networks for both human and rodent specific interactions to facilitate the transition of rodent trials to human trials. We will also identify perturbed drug targets and disease-related networks for a compound and identify discriminating adverse effects of several compounds simultaneously. Learn how to use intuitive workflows with automated report generation to provide rapid actionable results and putative toxic biomarker analysis.
  
  

• March 16, 2009 - Discovering biomarkers: using comparative analysis and filtering data sets and genome wide queries

  Disease diagnosis is highly dependent on identifying biomarkers that can be accurately measured to provide a reliable signal indicating the presence or absence of a disease and/or dissociate a disease symptom from drug toxicity. Current requirements for the validation of a biomarker include powerful assays, complete data/population set and solid statistical analysis. Toxicity biomarkers ideally should appear early enough during the administration course of a compound to be stopped before any adverse effects manifest. Furthermore, it is no longer sufficient to have one biomarker per disease and clinicians are focus on a biomarker profile for diagnosis. In this tutorial learn how to use several data sets to identify common or unique biomarkers for cancer therapy or dissect enriched biomarker pathway. Using a similar workflow, learn how to identify bookmarks of toxicity from comparing compounds used to treat cancer, as an example. We will also demonstrate how to use pre filters to generate lists of potential biomarkers specific to a tissue, fluid or disease. Join the trend and win at the race for identifying measurable biomarkers!
  
  

• April 21, 2009 - The solution to disease heterogeneity and developing personalized drug therapy

  An emerging cause in the complexity of cancer is gene heterogeneity. Gene expression profiling is key to defining molecular cancer subtypes, which can be used for diagnostic classification or more specific drug targeting approaches. Sorlie et al sought to better define the subpopulations of breast cancer by applying gene clustering analysis to microarray data and correlating them with clinical outcomes. To further define functional biological profiles of gene heterogeneity between subtypes across patient groups we use MetaCore to report the pathway and network analysis of two highly metastatic subtypes of breast cancer, ERBB2+ and Basal. We further use MetaDrug to predict biological effects of two cancer treating drugs, Iressa (gefitinib) and Tarceva (erlotinib) starting with molecular structures of the compounds and elucidating them. We further overlay the Sorlie gene analysis over the drug target networks of each compound to assign optimal drug-to patient-therapy.
  
  

• May 20, 2009 - How to expand biological space beyond the limits of direct interactions determined with newly found interactions using microRNA expression data and MetaLink

  MicroRNAs (miRNAs) regulate gene expression by directly binding to messenger RNA sequences leading to the repression of protein translation. It is has been hypothesized that identifying patterns of miRNA expression in disease states and elucidating the processes dictated by their targets will be key defining mechanisms of disease progression or classification. Here we use MetaCore in combination with MetaLink to visualize and characterize the biological functions of miRNA (from breast cancer subtypes) and their novel, known or predicted targets. Learn how to create interaction file types to be used in conjunction with expression analysis to first identify sets of miRNA for each cancer subtype then exploit our network building algorithms to build expanded networks for each set of microRNA. In this session you will also learn how to expand beyond the direct interactions of miRNA and their targets to export gene lists of associated biological functions for further enrichment analysis and comparison of function between cancer subtypes defined by miRNA trends. Also exploit how to use your miRNA target network as your own ontology for enrichment of a list of new targets. Lastly, learn how to overlay gene expression data from aggressive tumor samples to demonstrate a causative relationship with changes miRNA expression and target expression.
  
  

• June 15, 1009 - How to validate your hypothesis using different data types: from microRNA-> to RNA/DNA-> Protein

  With the advances in microarray analysis scientist are now able to obtain genetic fingerprints of RNA information. One caveat of RNA/ DNA analysis is that gene expression at this level is not always reflected in protein translation and validating gene expression across gene regulation stages is essential. MetaCore provides a mechanism to allow simultaneous analysis of genes at the microRNA, RNA/DNA and protein levels. In this tutorial learn how to upload and visualize different types of data, and use all forms of expression to characterize disease subsets or patient profiles, using breast cancer as an example. Learn how to exploit the ability to visualize gene to protein maturity and to use this information to formulate more accurate hypotheses.
  
  

• July 20, 2009 - Identify tissue specific pathology and therapies using novel interactions using MetaLink

  Often diseases of an organ, such as the neurological disease, Parkinson’s are caused by manifestation in several tissue regions. Here we demonstrate how novel interactions from different tissues can be used to assign different molecular symptoms to different regions of the brain. We specifically expand on the work of Suzuki et al who obtained expression data on novel Parkin (a gene associated with early onset of Parkinson’s) interaction partners from different regions of the brain known to be affected in Parkinsons. To introduce custom/novel interactions between Parkin and its binding partners from Suzuki et al, we demonstrate how to generate a MetaLink™ file which allows for automatic visual representation of the custom interactions on networks with known interactions from the database. We then demonstrate how enrichment analysis using MetaCore allows for the specific identification of where each Parkin interacting partner canonical maps. Here we also exploit the visualization properties of GeneGo canonical pathway maps to animate between pathways affected by the loss or gain of Parkin expression identify tissue specific trends of functionality.
  
  

• August 17, 2009 - Interactome Analysis - global data analysis with both functional and mechanistic insight

  GeneGo now provides the ability to perform complete interactome analysis according to topology, transcription factors or protein class. Learn how to use GeneGo’s interactome workflows to obtain mechanistic insight beyond enrichment analysis and network building. Whether you are starting with gene/protein expression or with gene id list of amplicons and mutations, learn how to obtain inter- and intra-connectivity information to confirm the relevance of your networks hubs. We then highlight how to determine if central network hubs are truly significant for the entire data set, avoiding literature bias to drive hypotheses further.
  
  

• September 21, 2009 - How to model your own pathway to create concise directional or causative networks

  The MetaCore platform enables further understanding of biological and chemical networks and the molecular characteristics of a disease associated with a particular gene or gene list. MetaCore also provides several advanced tools to further exploit different characteristics of a complex data set. In this training we will highlight tools such as filtering a data set or network according to different ontologies (according to disease or processes), tissues, orthologs, localizations, interactions and bodily fluids.
  
  Unique to MetaCore, is the ability to choose from ten different network building algorithms to accommodate different ranges of network spaces and biological interactions that can be drawn. This tutorial highlight when and how to use all ten network building algorithms from the newly created workflows and exploit the advanced options of assigning the signaling direction, specify types of interactions, size of the network and number of interaction steps. We also highlight how to save your created networks as netshot (frozen in time) or network (updatable with lasts content ) portray your interactions of interest on a built network. Tools such as how to add/ hide/show objects will be exploited. MetaCore also has the ability to merge networks and create networks exclusively from canonical pathway interactions. We will end by demonstrating how to customized saved network with Map Editor to document your hypothesis.
  
  

• October 19, 2009 - Predicting downstream effects of gene regulation on core metabolism

  GeneGo’s system biology platform incorporates a wide range of cellular signaling components including metabolic reactions. Learn how to visualize signaling interactions between hormones to receptors and downstream cascades to transcriptional regulation, including biochemical reactions, endogenous and exogenous metabolites (ie nutritional components). Using diabetes as an example, we will demonstrate how to data to show effects of insulin receptor regulation on glucose and lipid metabolism metabolism through directional networks of key regulators nutritional uptake, storage and breakdown. To complete the analysis we will demonstrate how illustrate drug treatment effects on insulin signaling to facilitate altered metabolism.
  
  

• November 16, 2009 - Verify your compound targets activity in MetaDrug with expression data from drug treated or siRNA treated experiments

  Ever wonder if your compound targets trigger similar pathways as those triggered by siRNA inhibition? Take your MetaDrug analysis further by combining compound reports with gene expression verification. Using velcade, a proteosomal inhibitor, as an example, this tutorial will emphasize how to search and upload velcade, with its metabolites and their respective targets to obtain functional enrichments. In addition, we will upload gene expression lists from cell lines treated with velcade or siRNA targeting a proteosomal subunit to overlay on target networks and compare inhibitory effects of each type of treatment.
  
  

• December 21, 2009 - Creating and using gene lists to dig deeper into your hypothesis

  Don’t limit your analysis to enrichment or network building! MetaCore allows you to derive new lists with the List Operation tool. First, learn how to convert your experiment data to a workable gene list or create a gene list from a network, enrichment analysis or EZ search result. Then use the Logical Operations tool to calculate intersections, unions, subtractions and other “derivative” subsets of genes & objects in activated datasets or gene lists from the data manager tool. You can also create lists based on how many times the genes appear in the original gene lists. Ultimately this tool allows you to recreate subsets of genes from your experimental data (such as different conditions, samples, times points) that may be of particular interest for further analysis.
  
  

On site training is available for $2,000 per day plus expenses.

Please contact training@genego.com to schedule dates.