Databasing Expression with Integrative Biochip Informatics

Transcription

1 Databasing Expression with Integrative Biochip Informatics Ju Han Kim, M.D., Ph.D., M.S. SNUBiomedical Informatics Seoul Nat t Univ. School of Medicine Databasing Gene Expression Bio-databases Microarray basics Do we really need databases for expression? An example for pharmacogenomics approach Public repositories for expression Relational vs. Object Oriented Models OM-MAGEML The reality Integrative biochip informatics, coming soon

2 Bio-databases PIR: bio-sequences in the 60 s by M. Dayhoff NAR review: major 500 bio-databases Primary Secondary -. Standardization Integration Intelligence Paradigm Shift - Clinical Knowledge Management - Clinician-directed Resource-directed Dr. Faughnan Dr. Elson Dr. Abraham Dr. Abraham Dr. Dandy Informatics Dr. Belsky Dr. connelly

3 The Central Dogma of Life DNA RNA Protein

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5 Bio-databases, what are the problems Heterogeneity: data types and forms Complexity Only loosely connected Noise & quality Level of granularity Curation Is modelling even possible? In-silico experiment is only possible with models Clinical knowledge engineering: 3-D Visualization of Gene Expression Streicher J, et al., Nature Genetics 2000;25:147-52

6 Clinical knowledge engineering: Future queries Database Structure Streicher J, et al., Nature Genetics 2000;25: Biochip basics Bioinformatics pipeline

7 Biochip, Core competency They are the genes! We have the map! Natural measure of quantification. Literally, INFINITE # of states Dynamic series on time & space Don t need to extract bio-molecules. Now systemic perturbations! Streamlining & automation of the process put abstraction barrier Do biology in silico! *********************** *********************** Say NO to lab bench! A Functional Genomics Strategy Interesting Patients Interesting Animals Interesting Cell Lines Make Biochip Appropriate Tissue Appropriate Conditions Extract RNA Hybridize Biochip Access Significance Functional Clustering Data Preprocessing Scan Biochip Biological Validation Post-cluster Analysis & Integration Informatical Validation???

8 Biochip informatics: clustering A 11 A 21 A 31 A 41 A 51 A 61 A 71 A 81 A 91 A 12 A 22 A 32 A 42 A 52 A 62 A 72 A 82 A 92 A 13 A 23 A 33 A 43 A 53 A 63 A 73 A 83 A 93 A 14 A 24 A 34 A 44 A 54 A 64 A 74 A 84 A 94 A 15 A 25 A 35 A 45 A 55 A 65 A 75 A 85 A 95 A 16 A 26 A 36 A 46 A 56 A 66 A 76 A 86 A 96 time Biochip informatics: clustering clustering

9 Hierarchical & Partitional Clustering Clinical relevance of Biochip informatics Dx. Discovery Tx. Px.

10 Really need databases for expression? Standard practice for gene sequences is.. How do we know the data integrity without data? Are authors interpretations sufficient? It s a whole new type of data. Observational vs. experimental Enormous potential for the good of public Where to house the data? In what format? and.. what s next? With a single format for gene expression data, databases should be able to 'talk' to one another and exchange data. The existence of a standard language should also spur development of software tools to query the databases, and to manage and display gene expression data.

11 An example for pharmacogenomics

12 An example for pharmacogenomics Expression data repository projects Public repositories in making: GEO : NCBI GeneX : NCGR ArrayExpress : EBI In-house databases : Stanford, MIT, U. Pennsylvania Organism specific databases: Mouse in Jackson

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14 Database Model: Relational vs. Object-Oriented (or Frame-based) MIAME Model RAD Model Stanford Microarray Database (SMD) Relational Ontology for samples Gene index for genes Annotation for Exp.

15 ArrayDB

16 MGED participants including Affymetrix Berkeley DDBJ DKFZ EMBL Gene Logic Incyte Max Plank Institute NCBI NCGR NHGRI Sanger Centre Stanford Uni Pennsylvania Uni Washington Whitehead Institute Reporting a Microarray Experiment Experimental Data Image Files Data Files Experimental Description Purpose of Study Experimental Details Standard Needed to Describe Microarray Experiment

17 Goals of a Microarray Data Standard Encapsulate Data and Experiment Description Rationale for Experimental Study Experimental Details Experimental Data Standard Data Access and Exchange Format Upload and Retrieval from Public Repositories Data Exchange Between Portals Widespread Industry Support Microarray Standards MIAME Minimum Information About a Microarray Experiment Experimental Design, Array Design, Hybridization, Samples, Measurements and Normalization MAGE-ML XML Implementation of the MIAME Standard Formed Via Merge of MAML and GEML Standards De Facto Widespread Industry Support

18 Conceptual view of gene expression data.

19 Three parts of gene expression DB Gene annotation may be given as links to gene sequence databases Sample annotation there currently are no public external databases (except the species taxonomy) Gene expression matrix each position contains information characterizing the expression of a particular gene in a particular sample. What are the measurement units for gene expression levels? General principles of MIAME design The recorded information about each experiment should be sufficient to interpret the experiment and should be detailed enough to enable comparisons to similar experiments and permit replication of experiments The information should be structured in a way that enables useful querying as well as automated data analysis and mining

20 MIAME structure I. Array design II. Experiment design 1. Experimental design 2. Samples used, extract preparation and labeling 3. Hybridization procedures and parameters 4. Measurement data and specifications of data processing Microarray Information to be Captured

21 Six components of microarray experiment. Three levels of microarray gene expression data processing.

22 MAGE-OM Framework for Developing MAGE-ML OMG specifications are developed in UML MAGE-OM represents a data driven model of microarray experiments This model can be used to automatically generate an XML DTD UML (Unified Modeling Language) Standard object-oriented design language Methods for showing relationships between data objects Objects are boxes (things) Association between objects are indicated by lines

23 Main Packages Biosequence Quantitation Type ArrayDesign DesignElement Array BioMaterial BioAssay BioAssayData Experiment HigherLevelAnalysis Protocol Description Audit and Security Measurement BioEvent

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25 Mapping from MAGE-OM to MAGE-ML Generated DTD

26 ArrayDesign 의 Class Diagram MAGE-ML : Microarray Data Exchange Formats Platform for moving data between data generators and shared databases International format to communicate data from DB to third part application Support MIAME compliant data Information Available online Current DTD (Document Type Definition) A few sample data sets

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28 MAML(Microarray Markup Language) standard / vocabulary communication / msg ontology

29 RDB implementation of MAGEML Web Server XML INTERNET Client XML RDBMS 관계형데이터베이스에서 XML로변환후연결한다. XML이라는 object는데이터베이스와응용프로그램간에데이터교환을위한표준기술의역할을한다. 데이터베이스에직접의존하는부분이적어지며갱신의부담이적어진다. 플랫폼이나기타언어등에제약을받지않고 XML의자원을전세계적으로이용할수있다. Middleware Implementation in Database Client Middleware Database <Overview of data transfer structure> XML File JSP http DOM 을처리하는 Servlet JDBC DATABASE (MySQL)

30 Transforming the cancer center Goals of National Center for Toxicogenomics

31 Minimum information to be recorded about toxicogenomics experiments Experimental design parameters, animal husbandry information or cell line and culture information, exposure parameters, dosing regimen, dose groups, and in-life observations. Microarray data, specifying the number and details of replicate array bioassays associated with particular samples, and including PCR transcript analysis if available. Numerical biological endpoint data, including necropsy weights or cell counts and doubling times, clinical chemistry and enzyme assays, hematology, urinalysis, other. Textual endpoint information such as gross observations, pathology and microscopy findings. ArrayTrack Commercial Tools Public In-house Microarray Database Functional components INTERFACE GeneLib ProteinLib PathwayLib ToxicantLib User

32 Development of a Toxicogenomic Supportive Database: dbzach Lyle D. Burgoon* Department of Pharmacology & Toxicology Institute For Environmental Toxicology and The National Food Safety & Toxicology Center Michigan State University tel: (517) fax: (517) burgoonl@msu.edu Research Program Supported by: National Institutes of Health P42 ES , *T32 ES07255 IBM/Mayo Clinic Collaboration Applied Genomics Data Analysis Genomic data (DNA) GeneChip array data (RNA) Protein data Clinical Data Signs Symptoms Laboratory Radiology Etc. Phase I Databases Genome Proteome Disease Tumors Drugs Optimized, individualized healthcare

33 PsyBase 년서울대학교병원신경정신과에서사용되기시작된국내최초의전자의무기록 PsyBase 1.0.

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35 Streamlining the process Miniaturization & Streamlining the process Image analysis Array fabrication Cluster analysis Data mining Pathway/network analysis Data management layer clone data Cell data Exp. data Inhouse data Outside data Slide data Hyb. data scan data Integrated biochip informatics CIS Literature and Factual Database mining Integrated biochip informatics Image analysis Array fabrication Cluster analysis Data mining Pathway/network analysis Data management layer Rosetta transcriptomics Ideker, 2001, Science Chemoinformatics clone data Outside data Slide data Cell data Hyb. data Exp. data scan data Inhouse data Communication Ontology

36 Xperanto: Expressionist s Esperanto in XML Xperanto: Expressionist s Esperanto in XML

37 Xperanto: Expressionist s Esperanto in XML Xperanto: Expressionist s Esperanto in XML

38 Xperanto: Expressionist s Esperanto in XML MGED Ontology 1. Input control 2. R-DB construction MAGE-OM Relational DB 인터넷 RDB implementation Translation to/from XML MAGEstk (Java API) DTD or Schema (generated from MAGE-OM) MAGE-ML Data Entry by HTML XML Validation XML Expression Repository 임상정보모델링 Open Source & XML 발현정보모델링 서열정보모델링 MGED Ontology MAGE-OM DTD or Schema (generated from MAGE-OM) XML Validation Relational DB RDB implementation MAGEstk (Java API) Data Entry by HTML MAGE-ML 인터넷 XML Expression Repository

39 Thank you!