Training Account. Account: ~ Password: ingenuity123. Sample & Assay Technologies

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1 Training Account Account: ~ Password: ingenuity123 1

2 IPA Introductory Training Course Academia Sinica 2014 September Chris (Yu-Lun Kuo) 2

3 About me Chris (Yu-Lun Kuo) Senior Researcher in Genesis Genetics Asia Corp. Ph.D. in Computer Science & Information Engineering, NTU 8-year research experience in bioinformatics and system biology Microarray Analysis NGS Analysis Biomedical Science 3

4 Agenda A. Introduction and Getting Started in IPA B. Searching and Accessing the Knowledge Base Hands-on Exercises C. Building a Pathway D. Editing a Pathway for Publication Hands-on Exercises E. Q & A 4

5 Agenda A. Introduction and Getting Started in IPA B. Searching and Accessing the Knowledge Base Hands-on Exercises C. Building a Pathway D. Editing a Pathway for Publication Hands-on Exercises E. Q & A 5

6 IPA IPA is an All-in-one, web-based software application Enables researchers to model, analyze, and understand the complex biological and chemical systems at the core of life science research 6

7 IPA Applications: Disease Mechanisms Target Identification and Variation Biomarker Discovery Drug Mechanism of Action Experimental Platform Supported : Gene Expression: (mrna, mirna, microarray platform, Next-gen sequencing, qpcr) Proteomics Genotyping Metabolomics Identifiers Drug Mechanism of Toxicity 7

8 Peer-reviewed publications citing Ingenuity apps >10,000 publications that used IPA -- and growing! thru Feb

9 A n a l y s i s a n d I n t e g r a t i o n o f M u l t i p l e T e c h n o l o g i e s Sample & Assay Technologies Proven Value Throughout Research and Discovery >10,000 publications that used IPA -- and growing! Discovery Biomarkers Gene Expression Metabolomics Toxicology Proteomics Mechanism of Action Genotyping Disease Models Epigenetics Pharmacogenomics mirna 9

10 Third Party Database Synonyms, Protein Family, Domains GO, Entrez Gene, Pfam Tissue and Biofluid Expression & Location GNF, Plasma Proteome Molecular Interactions BIND, DIP, MIPS, IntAct, Biogrid, MINT, Cognia, etc. mirna/mrna target databases TarBase, TargetScan, mirecords Gene to Disease Associations OMIM, GWAS databases Exploratory Clinical Biomarkers Clinical Trial information ClinicalTrials.gov 10

11 Ingenuity Expert Findings From full text, contextual detail, experimentally demonstrated Original sentence from publication nnos overexpression mice showed reduced myocardial contractility. Francisella organisms efficiently induce IL-1beta processing and release. Ingenuity Expert Findings Transgenic nnos in myocardium from mouse heart decreases the contractility of myocardium in left ventricle from mouse heart. Francisella tularensis subsp. novicida U112 increases (in a timedependent manner) release of human IL1B protein from human monocytes. Contextual details: Manual curation process captures relevant details Experimentally demonstrated: Findings are from full text articles includes tables and figures Structured: Supports computation and answering in-depth biological questions in the relevant context High quality: QC d to ensure accuracy Timely information: Weekly updates so up to date information is captured 11

12 Ingenuity Findings More than four million IPA Findings Species Synonyms Experimental method Site of post-translational modification Direction of change Tissue context Cell line context Original source 12

13 Ingenuity Platform: 15-years, massive investment Highest-quality interpretation of genomics and sequencing data Manually curated scientific literature Pathway and systems models Public databases Experimental data sets Customer proprietary data

14 Mutation Content Biological Models Sample & Assay Technologies Unprecedented Access to Knowledge LITERATURE & DATABASES FDA LABELS, GUIDELINES Unified Ontology Disease models PRIVATE VARIANT DB Pathways CURATION THE INGENUITY KNOWLEDGE BASE Biomarkers Causal Networks Regulatory Hereditary Experimental Somatic Leverages field s deepest functional knowledge base, with rigorously curated published findings, well structured ontology, and smart interaction modeling Mouse Ortholog Models Associations Copy number PGx & Clinically Validated

15 Explore the Ingenuity Knowledge Base Ingenuity Expert Findings THE INGENUITY From the KNOWLEDGE full text BASE Contextual details Timely High-quality Ingenuity ExpertAssist Findings High coverage (abstracts) Timely High-quality Ingenuity Expert Knowledge Ingenuity Supported Third Party Information Extensive: Leverages knowledge in one place - Largest scientific knowledge base of its kind with modeled relationships between proteins, genes, complexes, cells, tissues, drugs, pathways and diseases Structured: Captures relevant details - Scientific statements are modeled into Findings (often causal) using the Ingenuity Ontology Expert Review Process: Checked for accuracy - Findings go through extensive QC process Timely: Frequent updates and up-to-date knowledge - Findings are added weekly 15

16 IPA Application Integration Partnerships IPA 16

17 Supported Identifiers for Data Upload Vendor IDs Affymetrix Agilent Life Tech (ABI) Codelink Illumina Ingenuity Gene Protein Transcript microrna SNP Chemical Entrez Gene (LocusLink) GenBank Symbolhuman (HUGO/ HGNC, EG) Symbolmouse (EG) Symbol- rat (EG) GI Number GenPept International Protein Index (IPI) UniProt/ Swiss-Prot Accession Ensembl RefSeq UCSC (hg18) UCSC (hg19) mirbase (mature) mirbase (stemloop) Affy SNP IDs dbsnp CAS Registry Number HMDB KEGG PubChem CID UniGene

18 Registering and Downloading/Starting IPA Then click login button 選擇 IPA Logging into IPA with personal account and password First, please setting JAVA environment 18

19 IPA Interface Search box Project manager Quick start menu 19

20 On-Line Help Resources Get help from the on-line help documents and Legend 20

21 Agenda A. Introduction and Getting Started in IPA B. Searching and Accessing the Knowledge Base Hands-on Exercises C. Building a Pathway D. Editing a Pathway for Publication Hands-on Exercises E. Q & A 21

22 Searching Searching Basics Gene/chemical search and results Function/Disease search and results Drug target search and results Advanced search: Limiting results to a molecule type, family or subcellular location 22

23 Key Terminology Finding: A single piece of evidence from a literature source or database in the Ingenuity Knowledge Base Includes context of the fact such as experiment type, species, tissue/cell location, etc. Canonical Pathway (Signaling and Metabolic) Are generated prior to data input, based on the literature Do NOT change upon data input Do have directionality (proceed from A to Z ) 23

24 Live Demo 24

25 搜尋 Gene 與 Chemical 資訊 Example A Gene: ERBB2 Disease: diabetes Pathway: cell cycle: G1/S checkpoint Regulation Example B Gene: EGFR Chemical: Iressa (gefitinib) & Tarceva (erlotinib) Disease: Lung cancer Pathway: apoptosis / cancer / 25

26 Search Basics Enter a gene or protein name in the search box Enter a drug or chemical name in the search box Can use for exact phrase or * for a wild card 26

27 Search Results Search results return as a list of entities that have a name or synonym that closely matches the search term. Single-click the name to follow the link to the corresponding Gene View Click here to open Reagent View or Interaction network 27

28 Gene View Page Species specific information Summary tab Name and physical characteristics from public domain Links to IPA Canonical Pathways Link to full content Summary of Ingenuity curated findings Gene Ontology information from public domain 28

29 Chem View Page Name and physical characteristics from public domain Links to IPA Canonical Pathways, if available Link to full content Summary of Ingenuity curated findings 29

30 Chem View Drug Information If the chemical is a drug, additional information such as manufacturer, clinical trial status, target(s), and action were added. 30

31 Reagent View Vendors & Reagent Categories 31

32 Function/Disease Search One can also find molecules (gene/chemicals) associated with a biological process or disease. Results are listed in a tree or list format. Type in your keyword here Results are listed in a tree or list format Molecules (gene/chemicals) associated with a biological function or disease 32

33 Pathway/Tox List Search The Pathways and Tox Lists search enables you to search through the Ingenuity Canonical Pathways and Toxicity Lists Library. View report or open pathway from here 33

34 Advanced Search The Advanced Search feature allows you to perform more sophisticated searches. To open Advanced Search, click the Advanced Search link. Advanced Search enables you to search for molecule symbols or Identifiers, Chemicals or Drug Names, Functions and Diseases, Drugs, Protein Families, and Subcellular Locations, or any combination of these categories. 34

35 Interaction Network 35

36 Interaction Network 36

37 Hands-on Exercises 1. Find all genes/proteins/complexes that start with BCR, how many are there? 2. What disease is BCR (the kinase) associated with? Please list one of them. 3. How many categorized literature findings are in Ingenuitys knowledge base on BCR (the kinase)? 4. What FDA approved drugs interact with BCR (the kinase)? Advanced: 1. Search for genes associated with the function antigen presentation in IPA. How many genes are associated with the function activation of antigen presenting cells? 37

38 Agenda A. Introduction and Getting Started in IPA B. Searching and Accessing the Knowledge Base Hands-on Exercises C. Building a Pathway D. Editing a Pathway for Publication Hands-on Exercises E. Q & A 38

39 Build and Grow Networks of Molecules Grow Upstream from AKT1 to kinases and phosphatases 39

40 Building Pathways Introduction to Pathway Building Key Terminology Adding Molecules to a New Pathway General pathway navigating Using the Build Tools Understanding the legend Using the Overlay tools Saving work for future analyses How to build pathway 40

41 Key Terminology Networks: Generated de novo based upon input data Do NOT have directionality Canonical Pathways (Signaling and Metabolic): Are pre-built and generated prior to data input, based on the literature Do NOT change upon data input Do have directionality (proceed from A to Z ) My Pathways and Path Designer Pathways: Custom built pathways manually created based on user input Relationship: An interaction between two molecules in IPA (seen as a line) Direct (physical contact) and Indirect (do NOT require physical contact) 41

42 Live Demo 42

43 Adding Molecules to a New Pathway After finding GPCRs associated with attention deficit : 1. Put a check in the box next to the function 2. Click on Add To Pathway and choose New Pathway Click on Add To Pathway and choose New Pathway 2 1 Put a check in the box next to the function 43

44 Results of Adding Molecules to a Pathway Tool Bar in Pathway/Network view that contains a variety of functions Molecules added to a new pathway 3 44

45 Build Tool > Grow 2 Grow :Adds new molecules and their relationships given the criteria that the user specified. 3 Filtering with specific contexts

46 Build Tool > Path Explorer 2 Path Explorer: Calculates the Shortest Path between 2 molecules or 2 sets of molecules 3 Filtering with specific contexts

47 Overlay Tools Analyzed Dataset: Expression/data values that have been uploaded into IPA Drug: Known drugs that target the molecules on the pathways Function & Disease: Functions and Diseases that overlap My List: User created lists saved within IPA that overlap Canonical Pathway: Canonical Pathways that overlap My Pathway: User created pathways saved within IPA that overlap Ingenuity Tox List: Ingenuity created toxicity related lists that overlap Highlight: Outline molecules that match specified criteria 47

48 Overlay Canonical Pathways 3 2 Select the pathways you wish to view by checking the boxes next to them. 1 Canonical Pathways that overlap your pathway 48

49 Saving a Pathway Once a pathway is complete, make sure to save it Using the save icon in the tool bar if you are saving a new one Using File>Save or File>Save As if you edit an existing one

50 Agenda A. Introduction and Getting Started in IPA B. Searching and Accessing the Knowledge Base Hands-on Exercises C. Building a Pathway D. Editing a Pathway for Publication Hands-on Exercises E. Q & A 50

51 Path Designer Transform networks and pathways in IPA into publication quality pathway graphics rich with: Color Customized text and fonts Biological icons Organelles Custom backdrops 51

52 Path Designer All of the features available in My Pathways and Networks (Build, Overlay, Zoom, etc.) are also available in Path Designer pathways, except for Subcellular Layout To include the subcellular layout in your Path Designer pathway, select this option before you convert the My Pathway to a Path Designer pathway 52

53 Live Demo 53

54 Converting to Path Designer Pathways 54

55 Path Designer Tools 55

56 Export Features Export Image: Export an image of your network up to 600 dpi Export Data: Export a list of molecules with associated identifiers, expression values and gene details Send an interactive pathway to a colleague Print: Generates a hard copy of the network/pathway diagram Exporting the Analysis Summary Page: Export the Analysis Summary page as a PDF file Export Reference: Export a file containing reference information 56

57 Hands-on Exercises II 1. Search for the following genes in IPA and add them to a pathway: A2M, APOE, APP, HFE2, LRP1, PSEN1, PSEN2, SLC40A1, TRF2 2. What are the connections between these molecules that occur in nervous system tissues and CNS cell lines (with the relaxed filter)? 3. Are these molecules involved in any Canonical Pathways? 4. Add at least one organelle or other cellular structure to your pathway and move the molecules on your pathway to the appropriate location. 57

58 Q&A 58

59 歡迎與我們聯絡 Office: #1636 Fax: EXT 1022 My MSC Support:

60 IF:

61 Best Practices for Expression Data Analysis Analysis Ready Molecules For the best results, you should have approximately

62 How Path Explorer Works Calculates the Shortest Path between 2 molecules or 2 sets of molecules If 2 molecules/sets don t have specific connections in IPA, Path Explorer will find how many and which molecules can be added to this pathway to create the shortest path Shortest Path (n) Shortest Path + 1 (n+1) n+1 A n B 62

63 Direct Versus Indirect Interactions Direct interactions require that two molecules make direct physical contact with each other. There is NO intermediate step. Indirect interactions do NOT require that there is physical contact between the two molecules. An example of an indirect interaction would be a growth factor that causes a change in expression of another protein. This could be due to a signaling cascade instead of the two molecules making physical contact with each other. Indirect interactions are never inferred. They have to be explicitly stated in the literature. 63

64 What Does the p-value Mean? The smaller the p-value, the less likely an observed proportion of genes mapping to a function or pathway is a result of chance. We then infer that there is a biological effect. A p-value of 0.05 indicates that you should expect to observe the gene proportion mapping to a function or pathway by chance in one out of 20 (5 out of 100) repeated experiments of the same size 64

65 What is the Fisher s Exact Test The Fisher s exact test is a statistical test, similar to the chi-square test, used for categorical data that result from classifying objects in two different ways It is used to examine the significance of the association (contingency) between the two kinds of classification In IPA, the two categories are the uploaded dataset and a particular pathway or function A Fisher s exact test is used instead of the chi-square test because the number of genes that map to a function or pathway can be less than five and would cause the chi-square test to be inaccurate 65

66 How the Fisher s Exact Test is Calculated The null hypothesis: The overlap (association) between the dataset and the function/pathway is due to chance. In other words, they are independent of each other. If the proportions mapping to a function or pathway are similar between the sample and the reference, there is not likely to be a biological effect 66

67 Determining Significance of Your Data to IPA Is proportion of overlap the same? Your Data Set Specific Pathway or Function Reference Set Specific Pathway or Function 67

68 Important note: Statistics in Biology The Fisher s Exact Test Given a list of differentially expressed genes in a dataset, what is the probability that the overlap with the set of genes on a particular pathway is by random chance? The null hypothesis is that the association occurs purely by chance. The Fisher s Exact Test is a way to test for significant associations The test looks at the number of genes a. That match between pathway and dataset b. That are in pathway but did not match dataset c. That are in dataset but did not match pathway d. That were possible to assay in the experiment but are not in the pathway or dataset (this is usually called the reference set and is ~the set of all genes on the array platform) The calculation returns a p-value: From 0-1, where values <0.05 are generally considered significant) 68

69 Statistics in Biology: Fisher s Exact, continued If you had this situation: Dataset (significantly differentially expressed) of 286 genes Pathway of 81 genes Where 5 of the dataset genes overlap those in the pathway And the platform measured about 12,000 genes What is the significance of that overlap? p-value = (a+b)!(c+d)!(a+c)!(b+d)! (a+b+c+d)!a!b!c!d! ( )! p = (5+76)! ( )! (5+281)! ( )! 5! 76! 281! 11715! =0.043 Note:! is the factorial operator, where for example 3! = 3 x 2 x 1 = 6 69

70 Mapping Colorectal Cancer Expression Data to the Function Neoplasia 13,101 genes on chip Filter for genes that change expression 260 Map to neoplasia Sample 260/747= IPA 747 expressionsignificant 487 genes 3005 genes map to neoplasia 3005/13,101= Are the proportions that map to neoplasia significantly different between the chip (reference set) and the sample? Do not map to neoplasia 487/747=

71 Calculating the Fisher s Exact Test For IPA, a 2x2 contingency table is created based on the total population, the sample, and how many genes map to the function/pathway. This table is used to calculate the Fisher s exact test. Neoplasia Not Neoplasia In Sample k n - k n Not in Sample m - k N + k - n - m N - n m N - m N m= Total that map to function/pathway N= Total k= Number that map to function/pathway in sample n= Total sample 71

72 Calculating the Fisher s Exact Test Numbers based on the colorectal cancer data mapping to neoplasia Neoplasia Not Neoplasia In Sample Not in Sample = Total that map to neoplasia on chip = Total on chip 260 = Number that map to neoplasia in sample 747 = Total sample right-tailed Fisher s exact test p-value = 2.13 E-14 72