GeneGo Systems Biology Training (1 hour)

This seminar series is available every first Monday of the month and is designed to approach the basics. If you are just getting started or need to become reacquainted with new features, this is the series for you. Each seminar is platform centered. For example the MetaCore webinar is best for learning how to upload and analyze biological data while the MetaDrug webinar is best for starting with chemical structures. Click on the title of each topic for a detailed description of what will be covered.

Free online training is available every first Monday of the month.

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

To receive invitations please email training@genego.com.

• April 5, 2010 - 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 3, 2010 - Become a Chemist! Understanding and creating your own QSAR model

  
  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 7, 2010 - How to identify microRNA targets, work with microRNA data and use predicted information

  
  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.
  
  

• July 6, 2010 - 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.
  
  

• August 2, 2010 - How to choose the right network building or interactome algorithm to test and expand your hypothesis

  
  One aspect of systems biology is to integrate complex interactions of biological systems and address connectivity. GeneGo provides a highly annotated and dense interaction database with over ten different network building algorithms and 6 different interactome tools for this purpose. 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 and interactome algorithm and highlights how to optimize the visualization of your interactions of interest on a network we will build or to determine central hugs of regulation. From the network view, 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. The interactome options will include learning how to obtain inter- and intra-connectivity information to confirm the relevance of your networks hubs according to topology or protein class. 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 7, 2010 - Eureka! Knowledge mining GeneGo content, EZ Search and MetaSearch - data mining to create lists

  
  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:
  

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

  
  
  

• October 4, 2010 - 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 1, 2010 - 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 6, 2010 - Moving Beyond Pathway Analysis: Leveraging MetaBase content, API’s 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

This series of webinars are designed to push your hypothesis to the next step: from experimental discovery to drug targeting and clinical validation. The GeneGo platform is more than network building and enrichments and we expand on the usability here. These topics chosen are based on situations you may encounter during your analysis. They also demonstrate how to use a combination of GeneGo tools (MetaDiscovery) such as MetaCore with MetaLink or MetaDrug, Metasearch Data-mining and gene list functions, network building and interactome combinations. Attending the basic sessions is helpful but not mandatory.

Free online training is available every third Monday of the month.

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

To receive invitations please email training@genego.com.

• March 15, 2010 - Identifying sub-type specific breast cancers using microRNA and gene expression profiles

  
  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. Learn how to use MetaCore to search miRNAs and their KNOWN experimentally validated targets, their disease association and role in biological functions. Also learn how to upload miRNA micro arrays and identify trends for further target analysis/ HWe also demonstrate how MetaCore can be used in combination with MetaLink to map/visualize predicted miRNA targets on top of what is known to expand on biological roles of this small sequences. 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.
  
  

• April 19, 2010 - Comparison of side effects of Amiodarone and Dronedarone

  
  MetaDrug™ is a powerful tool for prediction of compound pharmacological activity, ADME (Adsorption, Distribution, Metabolism, Excretion) properties and investigation of potential toxicological effects. It can be used to analyze known compounds as well as novel compounds under development. Here we will compare Amiodarone and Dronedarone. Both are anti-arrythmic drugs, yet Amiodarone high iodine content leads to pulmonary fibrosis, thyroid disease and liver disease, while Dronedarone has no iodine and is less toxic. Enrichment analysis of the drug structures using MetaDrug™ Compare Compounds workflow for both compounds predicts antiarrhythmic activity from influencing ion transport and the thyroid side effects. Also it suggests that amiodarone unlike dronedarone can affect liver metabolism leading to higher toxicity. These predictions are in good agreement with the experimental findings.
  
  

• May 17, 2010 - Integrated analysis of genomic and proteomic data reveals key role of growth factor signaling network in prostate cancer cells

  
  Prostate cancer is one of the most commonly diagnosed cancers and the second leading cause of cancer-related death in North American men. While androgen withdrawal therapy is often effective initially, most cases progress to the much more aggressive androgen-independent phenotype. Despite significant research efforts, the mechanisms underlying tumor progression are poorly understood. Roles for several signaling pathways have been established, but not a systemic picture. In the present study we have investigated response of LNCaP prostate cancer cells to treatment with synthetic androgen (R1881), as a well-studied model system for prostate cancer progression. We have performed concurrent measurements of gene expression and protein levels following the treatment using microarrays and iTRAQ proteomics. Sets of up-regulated genes and proteins were analyzed using our novel concept of “topological significance”. This method combines high-throughput molecular data with the global network of protein interactions to identify nodes which occupy significant network positions with respect to differentially expressed genes or proteins. Our analysis identified the network of growth factor regulation of cell cycle as the main response module for androgen treatment in LNCap cells. We show that the majority of signaling nodes in this network occupy significant positions with respect to the observed gene expression and proteomic profiles elicited by androgen stimulus. Our results further indicate that growth factor signaling probably represents a “second phase” response, not directly dependent on the initial androgen stimulus. We conclude that in prostate cancer cells the proliferative signals are likely to be transmitted from multiple growth factor receptors by a multitude of signaling pathways converging on several key regulators of cell proliferation such as c-Myc, Cyclin D and CREB1. Furthermore, these pathways are not isolated but constitute an interconnected network module containing many alternative routes from inputs to outputs. If the whole network is involved, a precisely formulated combination therapy may be required to fight the tumor growth effectively. Moreover, our results show that the predictions made from microarray and proteomics profiles converge on the same set of regulatory proteins despite significant paucity in the experimental data. Thus our approach could be instrumental in translating high-throughput datasets generated by vastly different technologies into consistent predictions of activity of underlying signaling pathways and key regulatory proteins.
  
  

• June 21, 2010 - Hemoinformatics comparison of Lopac, InterBioScreen and Spectrum chemical libraries with umbilical cord blood mesenchymal stem cells: Characteristics of “proliferatome” and “apoptome”

  
  MetaDrug™ is a systems pharmacology platform designed for discovery of novel pathways. Here we use MetaDrug™ for pathway analysis of the results of screening of three chemical libraries with proliferation readout against ex vivo umbilical cord blood mesenchymal stem cells (MSCs). This work was done in collaboration with Pauli Ojala et al and Finnish Red Cross Blood Service, Research and Development. As a result of screening, dozens of proliferators and toxins had been identified that were clustered into several unique hubs by similar scaffolds from superimposing shape and charge. MetaDrug™ identified that proliferators including mostly steroids and other molecules target pathways consisting from androgen-, serotonin-, progesterone-, estrogen-, histamine receptors, PARP1 and others proteins, that would constitute the “proliferatome” of the MSCs. Mesenchytoxins on the other hand include some steroid-type alkaloids and target ATPase complex, MDR1, EGFR, AHR and other proteins that would constitute the MSC “apoptome”.
  
  

• July 19, 2010 - Understanding Insulin-Resistant Diabetes Through Pathway Analysis
• August 16, 2010 - Investigating mechanisms of chemical toxicity using MetaCore
• September 20, 2010 - Identifying tissue specific pathologies in Pakinsons via Parkin

  
  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 and identify tissue specific trends of functionality.
  
  

• October 18, 2010 - Pathway Analysis of Serological Metabolite Profiles in Rheumatoid Arthritis
• November 15, 2010 - Combining Agile Text Mining with Expert Manually Curated Data to Provide Enhanced Understanding of Liver Fibrosis Predicting downstream effects of gene regulation on core metabolism
• December 20, 2010 - Evaluation of pharmacological and side effects of Vioxx® (Rofecoxib) with MetaDrug.

  
  Recent findings of multitargeted kinase inhibitors have stimulated research to find drugs hitting more than one target (pathway) at once. This is where the tools capable of assessing drugs polypharmacology come into play. For your compounds MetaDrug allows you to see what other novel pathways they affect and what diseases they impact. Here we will show how MetaDrug™ can be used to predict potential side effects and new therapeutic activities for the rofecoxib (Vioxx), a COX-2 inhibitor withdraw from the market. Using enrichment analysis of rofecoxib possible targets MetaDrug™ suggests its involvement in platelet aggregation and cell adhesion, the processes that can explain its cardiotoxicity. MetaDrug™ also indicates that it can be repositioned as an anticancer drug.
  
  

In addition to having the option of attending full day courses off-site, you can arrange for a course on-site catered to your analysis and group needs. Options include any on or a combination of the following:

1. 3 hour basic MetaCore:
   • Uploading data : expression profiles of genes, proteins, siRNAs, miRNAs etc.
   • EZ start interface
   • EZ Search: one-stop-shop data mining tool for access to your favorite gene, disease, protein, drug, map, network or article
   • Enrichment Analysis: determine the most representative process for your data across nine GeneGo ontologies and three Gene Ontology categories
2. 3 hour advanced MetaCore
   • Network algorithms: Learn which algorithm out of the 10 best suits your analysis needs
   • Interactome: determine categorical connectivity for your geneg list or your favorite protein
   • MetaSearch: extract lists of genes, proteins, compounds, biomarkers using Boolean query searches for seamless analysis with MetaCore
3. 3 hour MetaDrug
   • Uploading structures
   • Predicting metabolites and similar structures
   • Predicting activity with our extensive QSAR list or create your own
   • Identify targets for your compound and predicted metabolites
   • Pathway and enrichment analysis of targets
4. 3 hour MetaCore/MetaDrug combination
   • Dual approach analysis:
     • start with genes and identify targets then determine potential target pathways and toxicity
     • start with compounds and map target pathways to overlay expression data to validate or rule out drug effects and toxicity predictions
5. Scheduled 1 on 1 project consultations
6. Diseases or application specific seminar presentations (similar to Bench to bedside Webinars )
7. GeneGo Module oriented
   • Enrichment and comparative analysis (1 hr)
   • Interactome and Network building (1 hr)
   • Data mining Gene list function (1 hr)
   • Biomarker assessment (1 hr)
   • Networks and MapEditor

Cost: $2,500 per day plus expenses.

Includes training materials (here).