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Similaire à TargetInsights: A New Method to Rapidly Access 'Specificity” of Selected Proteins' - webinar March 20 2013
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TargetInsights: A New Method to Rapidly Access 'Specificity” of Selected Proteins' - webinar March 20 2013
- 1. Welcome to Elsevier Life Science webinar on Sandhoff Disease! We will begin soon. Your host: Ann-Marie Your presenter: Dr. Thomas van Himbergen 1 1
- 2. Elsevier’s Life Science Solutions 2 2
- 3. Useful tips Webinar control panel: • ‘chat’ or ‘ask a question’ for question and comments • Option for full screen view Q&A after presentation and demo 3 3
- 4. A New Method to Rapidly Access “Specificity” of Selected Proteins Wednesday, March 20, 2013 Thomas van Himbergen -- Project Manager T.vanHimbergen@elsevier.com
- 5. Profile Multiple proteins against Multiple diseases
- 6. Today’s Presentation • General Introduction • Target Discovery • TargetInsights • Use case: Sandhoff Disease • Basic Search: filtering searches • Advanced Search: relationship search • Multiple Search: disease protein profile • Q&A and Demo
- 7. Pharmaceutical Target Discovery The First , Most Critical & Challenging Step Good target selection is dependent on understanding disease & disease progression. Early Discovery Target Target Lead Lead Tox Safety PI/II PoC PIII/ ID Val PIV ID Val Find new drug targets by: What • Building & verifying disease models and proteins or disease pathways gene are What is the associated function of • Profiling proteins & genes against diseases with disease this protein and tissues and other biological properties of interest? target ? to understand interactions Is this target • Understanding functional relationships specific for disease between entities and concepts to support x? Where else is target selection and disease mechanism this protein hypotheses. expressed?
- 8. Pharmaceutical Target Discovery The First , Most Critical & Challenging Step Good target selection is dependent on understanding disease & disease progression. Early Discovery Target Target Lead Lead PIII/ Tox Safety PI/II PoC ID Val ID Val PIV Validate drug targets by: • Finding evidence/ information on What types of proteins/genes to support experimental experimental data has findings & hypotheses been published, siRNA, What assay or genomic, knockout animal models • Uncovering information gaps – where mice etc? are best? data/ information is scarce or non- existing – to guide research Can this target be • Examining new information and how this modulated or affects selected projects to further alerted? If so what is best approach? support target selection decisions
- 9. TargetInsights Search powerfully. Advance intelligently. Critical questions addressed! Early Discovery Target Target Lead Lead PIII/ Tox Safety PI/II PoC ID Val ID Val PIV What What assay or proteins or animal models gene are What is the are best? associated function of with disease this protein of interest? target ? Can this target be Is this target modulated or specific for disease alerted? If so what is x? Where else is best approach? this protein expressed?
- 10. TargetInsights Search powerfully. Advance intelligently. Full text relationship searching of biomedical literature, which is linked though a comprehensive taxonomy (thesaurus). Full text annotations of the Elsevier and other publishers journals (approx. 600.000 articles/y in +5000 journals) Additional annotations of Medline Unique Abstracts and Conference Abstracts (approx. 400.000/y) Article in Press updates Fully automated annotation. Focus on genes/proteins, drugs and diseases.
- 11. Deep Indexing of Full-text Articles Taxonomies and indexing 9 target discovery relevant Taxonomy categories Millions of terms aligned to taxonomies
- 13. Sandhoff Disease? • Rare genetic lipid storage disorder • Beta-hexosaminidase enzyme deficiency • Accumulation of lipids in the brain and other organs of the body. • Progressive decline of the central nervous system. • Classic Infantile Sandhoff is the most common form: characterized by little to no Hexosaminidase-A (Hex-A) and Hexosaminidase-B (Hex-B) enzyme activity. • Search Question: What other proteins may play a role in the progression of this disease and may be useful drug targets to manage the disease symptoms?
- 14. Streamline Search Strategy From disease to potential drug target What other genes or proteins may play a role in the progression of Sandhoff disease and may be useful drug targets? 1. Basic Search: one concept at the document level ‘Sandhoff Disease’ 1a. Select Known Biological functions- search 1b. Select top protein(s) associated with biological functions- set up co-occurrence 2. Advanced Search: co-occurrence search between concepts, sent level Sandhoff Disease & cathepsin d 3. Multiple search – to determine if cathepsin d is specific to Sandhoff disease, profile multiple diseases against protein class cathepsin.
- 15. Basic Search at the Document Level
- 16. Search Results: 324 Hits
- 17. A Quick Comparison to PubMed: About 5 Times More Hits using TI… 63 hits versus 324. There is a lot of information overlooked in Title-Abstract searches!
- 18. Filter on Biological Function
- 19. Search Results: 182 Hits
- 20. Filter on Specific Genes or Proteins Categories populated with each new search Top 25 indexed terms within the search results are presented: Scale research intensity: higher in list more research Narrow and refine search fast Examine relationships that may have been overlooked SD Enzymes Protein of interest?
- 21. Results: Filter on Specific Genes or Proteins
- 22. Display: Abstract and Index Terms Cathepsin D and Sandhoff Disease not mentioned in Abstract
- 24. Co-occurrence Search: Doc, Paragraph, Sentence
- 25. Results: Doc, Paragraph, Sentence
- 26. Display: Abstract and Index Terms Cathepsin D is not mentioned in Title or Abstract. Review Paper: Sandhoff disease and Cathepsin D co-occur in the same sentence: … over expression of Cathepsin D (CTSD) has been reported in the brain of murine models of several other lysosomal diseases such as Gaucher’s disease, Sandhoff disease, GM1 gangliosidoses, Neimann-Pick A …
- 27. Search Method: Multiple Co-occurrence Search
- 28. Browse the Dictionary or Search Direct Search Direct Browse
- 29. Profile Proteins against Disease • Assemble neurological diseases along one axis • Assemble Cathepsin protein family members along another axis • Build knowledge profile Small body of evidence established discovery still probable Potential emerging area of research, new hypotheses?
- 30. Matrix Search Results: Review Results and Read Papers
- 31. Summary of Findings • Basic Search for „Sandhoff Disease‟ • Filtered results based on biological function: ‘Lysosomal Enzyme’ and ‘Beta-hexosaminidase’ • Reviewed the list of proteins found in the search results: Cathepsin D • Advanced Search: Co-Occurrence • Retrieved two papers describing a relationship between ‘Sandhoff Disease’ and ‘Cathepsin D’ based on sentence level co-occurrence) • Set up multiple search: • Profile of the Cathepsin protein family against other neurodegenerative diseases.
- 32. User Poll • Which search method to further explore in the live demonstration? • Basic Search • Co-Occurrence search • Multiple search matrix
- 33. DEMO: www.targetinsights.com Based on the results from the Poll: • Basic Search • Co-Occurrence • Multiple search
- 34. Product Summary • TargetInsights is a powerful search tool that informs target discovery & validation approaches: • Leverages Elsevier’s world class full-text journals as well as multiple publishers to create a best-in-class and unique content database • Allows researchers to dig deep into full text articles for new & existing information to guide discovery & validation approaches • Provides unique search formats that quickly summaries information to aid researchers in generating, refining and verifying target research hypotheses
- 35. Questions?
Notes de l'éditeur
- Welcome to this Elsevier webex. My name is Thomas van Himbergen and I am a project manager at Elsevier.I was closely involved in the creation of this new product called TargetInsights which is a new literature search tool aimed at supporting the drug discovery process. In drug discovery, a lot of energy is spent on researching disease specific proteins which may be suitable targets for drug intervention.TargetInsights helps you to dig out all the information which is hidden beneath the title and the abstract. Furthermore, Target Insights is a tool that helps you explore biological relationships in literature between concepts of interest… …and in the next 30 minutes I will be demonstrating how this is done.
- Before we start with the talk I would like to show you this table. This table was generated using our Multiple Search tool and displays multiple relationships between proteins- on the y axis and neurodegenerative disease on the x axis. Instantly you can review the volume of published literature relating to the intersecting terms, for example, Alzheimer disease and cathespin d. From here you can determine which interaction you would want to further explore, read articles and determine if this information impacts your current disease hypotheses… or it may even point to new hypotheses.The rest of this discussion will go over the steps we took to define this table.
- Selecting the right targets is dependent on a solid understanding of disease biology. Note that the activities described involve both experimental and literature-based research.
- Selecting the right targets is dependent on a solid understanding of disease biology. Note that the activities described involve both experimental and literature-based research.
- So, in the early phases of the drug discovery process, there are many open questions that need to be addressed. TargetInsights is a powerful literature search tool that helps researches with their literature based target discovery & validation activitiesand can help address many of these important questions and guide research decision that will impact downstream success.
- This slide illustrates the indexing and taxonomies that finds precise information and that can be used as powerful filters.When this search is completed each of these 9 tax categories are populated with \\the top 100 index terms aligned to the articles found. Each category has a pull down list that allows you to select index terms as additional filters. You can select terms form 1 or all 9 categories and when a new search is initiated these categories become re-populated with new terms. If your search terms is not found in the title or abstract it is likely in the full-text article. To verify simple click on the “View Index” tab and all the terms identified for this article will be shown. Note that “Sandhoff disease is located.
- It should be noted that this use case was defined from a perspective of little working knowledge of Sandhoff disease.
- It should be noted that this use case was defined from a perspective of little working knowledge of Sandhoff disease.Reading a review article we learnt that SD is a rare genetic lipid storage disorder caused by Beta-hexosaminidaseenzymedeficiency. Low levels and activity of this enzyme lead to accumulation of lipids in the brain and other organs of the body. Which in its turn cause a progressive decline of the central nervous system. Classic Infantile Sandhoff is the most common form of SD and is characterized by little to no Hexosaminidase-A (Hex-A) and Hexosaminidase-B (Hex-B) enzyme activity. This is the starting point for our search and the search question is: What other proteins may play a role in the progression of this disease and may be useful drug targets to manage the disease symptoms?
- This slide illustrates how TI can be used to address specific questions that target discovery scientist need to answer and applying it to the Sandhoff disease use case. We will take what is know and look to see of additional proteins can be found that may be good drug targets that could relieve some of the symptoms associated with the disease.First we start with finding out what is known about Sandoff through basis search step1.This leads to a possible new protein cathepsin d (filtered to the top of proteins tax category step 1b )Next we look to see how much is known about SD AND cathepsin d by doing a sentence level co-occurrence search- found 2 documents.Looks promising but need to verify if the protein is specific only to SD. Set up multiple search with protein family cathepsin against neurodegenerative diseases. Resulting matrix is shown.Next step would be to complete search (highlighted in green and read papers to determine if this could be a possible new target.This process simplifies the literature search significantly and could as we have demonstrated take minutes to capturing information that may impact research direction.
- Our first search is for SD at the document level. Notice that TargetInsights has an autocomplete function and spelling variations of SD all lead to the same search and the same search results. On the bottom there is the option to define co-occurrences, since this option is only relevant for when you are searching for more than 2 concepts or search terms, so we leave it at the document level.
- Hitting ‘search’ gives us the following set of results, 324 hits. These are papers which have been indexed and have one or more mentions of SD in the title, abstract or the body text. This is important to highlight, TI does an annotation of the full text of a paper, not just the title or the abstract and there for you will find papers that you will not find through a regular Pubmed search.
- Going to Pubmed and running the same search, gives us about 5 times less results that using TI over a comparable period of time (which is about 5 years of content). This difference is cause by the fact that Target Insights searched annotations derived from the full text of the article, not just the title and the abstract and the MeSH terms as Pubmed does. This make a big difference and is one of the main strengths of TI.
- Next step is to make our search more specific. There are 343 articles form the our original search but the chance is that there are a number of results in there that are not specific for SD, there might be a mention of SB in the text but the article focusses on something else. For example, clinical papers might mention the disease but have nothing on the underlying mechanism and we are interested in that, we want to focus on the mechanism. To do this, we go to this menu and select Biological Functions. There we see a list of biological functions associated with our search results (I will explain the dynamics of this search function in the next slide), what we do here is pick functions we know are important for SD, we know that the issues are caused by defects in Lysosomal Enzymes, so we select that. We also select the specific enzymes causing the disease from the list, remember that was beta hexosaminidase, so we select those as well. If we now apply these filters we get results for SD andLysosomal Enzyme or Beta Hexosaminidase. By adding the biological function filter specifies the search to those papers really dealing with SD and the underlying mechanism.
- Results of our biological function filter is that we go from 324 to 182 hits and have most probably filtered out the papers not directly dealing with the mechanism or paper with a single mention of the diease. Next thing I’ll do is show you an other way you can use these filters here on the right hand side, and that is to look at the Gene’s and Proteins filter and review the top 25 Gene’s and Proteins in this filtered set of search results. For this, it is important to understand how these filters work in detail.
- We are going to review the top genes and proteins identified in our search results using the filters. Here you see the filters based on the 9 taxonomies we use to annotate. Each category is populated with each new search and the top 25 index terms are listed. Note the higher they are in the list the more frequent they occur in the search results. This option can be used to narrow and refine your search, as I have shown you in the previous slide, but also to examine relationships that have been overlooked, and that is what we are doing here:Reviewing the list of genes and proteins here we see the familiar ones causing SD, remember the Beta-hexosaminidase, HEX-A and HEX-B. Going passed the established ones, we notice Cathepsin D here, which may be an interesting option to look into. Note that in addition to the established proteins we are looking for additional proteins may play a role in the progression of this disease and may be useful drug targets to manage the disease symptoms.
- The results of the Cathepsin D filter are presented here. We end up with 19 papers which are about SD and Cathepsin D at the document level. 19 papers is a manageable amount to read and review. If we pick one we are taken to the result page.
- The result page consists of the Title and Abstract, similar to Pubmed. Also listed here are the relevant index terms found in this particular article, these terms are grouped by the taxonomy. Notice that both SD and Cat D are not present in the title or the abstract, but if we review the index terms we see that both concepts are present in the full text.
- Next search method is the co-occurrence search. This method is particularly useful when you are interested in finding papers which deal with two concepts in relation to each other.
- In our example we are interested the relationship SD and Cat D and we would like to find relevant papers describing that relationship. This can be done using the co-occurrence setting. There are three options: doc, para and sentence. These can be used to set the limit on how close the occurrence of these two terms should be: in the same document, paragraph or sentence. The strongest semantic relation is of course when they both occur in the same sentence. The opposite is also true, when they occur in the same document, they might not be directly related. These radio buttons give the user the control to define how strong the semantic relationship needs to be. Generally, one would be interested in the strongest relationship, that is the sentence co-occurrence. But when there is limited evidence available and no results are returned for the sentence search, one may relax the setting to the paragraph or even the document level to retrieve papers that might be of interest.
- I ran three different searches here at the doc, para and sentence level, returning 25, 5 and 2 hits respectively. Thus, down here are the two titles which have sentences in them with both SD and Cat D in the same sentence. We’ll pick one to review the results.
- Here, SD is in the abstract but Cat D is not. Cat D is in the indexed terms and when you review the actual paper, you find the sentence displaying the co-occurrence of SD and Cat D.
- The next few slides are on the multiple search function in TI.
- The multiple search function allows the use to create a search matrix and explore interactions. For this you need to add search terms to the x-axis and the y-axis. There are two methods to do that: by browsing the taxonomy and adding terms and by a direct search. Her on the left you see the search function based on taxonomy browsing. This method is especially useful when you would like to pick a series of related terms and would like the taxonomy to guide you in your selection. In this example, I have been searching for neurodegenerative diseases and variants of SD. Here I picked ‘Tay Sachs Disease’ which similar to SD, they are both variants of a GM2 Gangliosidosis i.e. disorders that result from a deficiency of the enzyme Beta-Hexosaminidase, so I selected that search term as well. On the right is the Direct Search function. This is useful for a search when you already know what you are looking for. In this example, I searched for cathepsin and got a list of all the members of the Cathepsin protein family. Going through the list I can select the variants I would like to include in my search.
- This table is generated using our Multiple Search tool and assess multiple relationships between proteins from the Cathepsin family on the y axis and neurodegenerative disease on the x axis. Instantly you can check the volume of published literature relating to the intersecting terms, for example, Alzheimer disease and cathespin D or Cat B. There appears to be relatively less published data for Cat B-SD compared to Cat D- SD. From here you can determine which sell to completed a search, read articles and determine if this information impacts your current disease hypotheses, can help verify a current hypothesis, or maybe even point to new possible mechanistic hypotheses.
- Again, clicking any search result in the matrix takes you to the list of papers where you can continue to read and review the results.
- Just to summarize what we did….
- Basic Search: Sandhoff disease and filtering. Taxonomy. Co-Occurrence. Sandhoff disease and Cathepsin D. Review resultsMultiple search. Sandhoff disease and cathepsin d. 5 per axis.