Databases for Microarrays

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Databases for Microarrays

• Vidhya Jagannathan
• SIB, Lausanne

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Overview

• Microarray data in a nutshell
• Why databases?
• What data to represent?
• What is a database?
• Different data models
• E-R modelling
• Microarray Databases
• Standards being developed

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Microarray Experiment
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Microarray Data in a Nutshell

• Lots of data to be managed before and after the
  experiment.
• Data to be stored before the experiment .
• Description of the array and the sample.
• Direct access to all the cDNA and gene sequences,
  annotations, and physical DNA resources.
• Data to be stored after the experiment
• Raw Data - scanned images.
• Gene Expression Matrix - Relative expression
  levels observed on various sites on the array.
• Hence we can see that database software capable
  of dealing with larger volumes of numeric and
  image data is required.

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Why Databases?

• Tailored to datatype
• Tailored to the Scientists
• Intuitive ways to query the data
• Diagrams, forms, point and click, text etc.
• Support for efficient answering of queries.
• Query optimisation, indexes, compact physical
  storage.

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Data Representation

• Goal Represent data in an intuitive and
  convenient manner
• Without unnecessary replication of information
• Making it easy to write queries to find required
  information
• Supporting efficient retrieval of required
  information

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What is a Database?

• A database is an organised collection of pieces
  of structured electronic information.
• Example 1 Libraires use a database system to
  keep track of library inventory and loans.
• Example 2 All airlines use database system to
  manage their flights and reservations.
• The collection of records kept for a common
  purpose such as these is known as a database.
• The records of the database normally reside on a
  hard disk and the records are retrieved into
  computer memory only when they are accessed.
• So the reasons are obvious why we need to discuss
  about a Microarray database.

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Data Models

• Describes a container for data and methods to
  store and retrieve data from that container.
• Abstract math algorithms and concepts.
• Cannot touch a data model.
• Very useful

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Types of Data Models

• Ad-hoc file formats (not really data models!)
• Relational data model
• Object-relational data model
• Object-oriented data model
• XML (Extensible Markup Language)

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Ad-hoc File Formats

• The various 'ad-hoc' file formats in use for
  microarray data are
• Flat file formats.
• Spread sheet formats.
• Not the least - Even MS-Word documents !!!
• Very rudimentary method to store data .
• Sometimes contains redundant information.
• Extremely inefficient for retrieval of particular
  subsets of the results.

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Relational Data Model

• Most prevalent and used in many databases
  developed today.

• The collection of related information is
  represented as a set of tables.

• Data value is stored in the intersection of row
  and column

• Column values are of the same kind. A Simple
  data validation.

• Rows are unique. So no data redundancy and every
  row is meaningful and can be identified by the
  unique key.

• Utilises Structured Query Language (SQL) for data
  storage, retrival and manipulation.

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Example
Table
Row or Record
Field or Column
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Example
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Advantages of Relational Model

• Allows information to be broken up into logical
  units and stored in tables.
• Allows combining data from different tables in
  different ways to derive useful information.
• Great for queries involving information from
  multiple original sources.
• Can easily gather related information.
• e.g. information about a particular gene from
  multiple datasets/experiments

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Object Oriented Model

• Object Oriented Model allows real world data to
  be represented as objects.
• Objects encapsulate the data and provide methods
  to access or manipulate it.
• Objects with specific structure and set of
  methods are said to belong to the object class.
• Allows new classes to be created by extending
  the description of the parent class.
• Child classes inherit the data and methods of the
  parent class.

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Example
OODBMS
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Example - ArrayExpress
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Database Design Entity-Relationship Concept
Relationship
Entity A
Entity B
Examples
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Entities

• are real world objects
• ex gene
• contain attributes
• ex gene_id, sequence
• are drawn as rectangle boxes that holds the name
  of the entity and attribute in two different
  notations as there is no standard!

Gene
notation 2
notation 1
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Relationship

• Relationships provide connections between two or
  more entities
• ex Which genes were used in which experiment
• When two entities are involved in a relationship,
  it is known as binary relationship.
• When three entities are invoved in a
  relationship, it is called as ternary
  relationship.
• When more than three entities are involved in a
  relationship, it is usually broken in to one or
  more binary or ternary relationships.
• are drawn as a line linking the involved entities
  as

used_in
Gene
Experiment
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Example E-R Diagram
Expt-Exptr
Expt-Sample
Multivalued attribute
Notation
Expt-Array
Many-to-one
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Object relational data model

• Improved relational model by adding some features
  from object data models.
• Information is represented as in relational
  models but column values not restricted to one
  mutliple values are allowed.
• Example (sample table in previous slides)

sampe-id organism celltype drug_id
d1 d2
s001 ecoli c123 ac1 nm ac3 nm
s002 ecoli c123 ac1 nm ac3 nm
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Queries, queries, queries!!

• Given a collection of microarray generated gene
  expression data, what kind of questions the users
  wish to pose.
• Constructing an extensive list of possible
  interesting queries and data mining problems that
  has to be supported by the database will
  facilitate the design process.

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Queries, queries, queries!!

• Query to the data
• Which genes are linked ?
• Which genes are expressed similarly to my gene
  XYZ?
• Which genes have a changed the expression in a
  second condition ?
• Which genes are co-expressed in differing
  conditions ?
• classification (of tumors, diseased tissues
  etc.) which patterns are characteristic for a
  certain class of samples, which genes are
  involved?

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More Queries !!!

• Queries that add a link in additional knowledge
• functional classification of genes Are changes
  clustered in particular classes?
• metabolic pathway information Is a certain
  pathway/route in a pathway affected?
• disease information clinical follow up
  correlation to expression patterns.
• phenotype information for mutants Are there
  correlations between particular phenotypes and
  expression patterns?

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More Queries !!!

• in what region is the interesting gene located in
  the genome?
• is there synteny in this region with other
  species?
• is there a known trait that maps to this region?

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Query Language

• Language in which user requests information from
  the database.
• SQL
• Data definition helps you implement your model
  and data manipulation helps you modify and
  retrive data
• Advantages
• Can specify query declaratively and let database
  system figure out best way of finding answers
• Supports queries of medium complexity
• Specialized languages
• SQL language statements are not abstract but very
  close to spoken language.

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Basic SQL Queries

• Find the image for experiment number 1345
• select image from experiment where
  experiment-id 1345
• Find the experiment-id and image of all
  experiments involving e-coli
• select experiment-id, image from experiment,
  sample where experiment.sample-id
  sample.sample-id and sample.organism e.coli
• All combinations of rows from the relations in
  the from clause are considered, and those that
  satisfy the where conditions are output

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Interfacing

• SQL queries are carried out on terminal screen
  which is not very useful and user friendly for an
  end user, so applications are created to
  interface more friendly staments with the SQL
  statements
• A web form is a typical example of interface for
  SQL
• Applications for data loading.
• More complex queries (e.g. data mining such as
  classification and clustering) are very
  imporatant part of the Microarray Analyis
  Protocol
• It is very important to interface the various
  applications we use to analyse the retrieved data
  with database.

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Gene Expression Databases Require Integration

• There are many different types of data presenting
  numerous relationships.
• There are a number of Databases with lots of
  information.
• Experiments need to be compared because the
  experiments are very difficult to perform and
  very expensive.
• Solution Make all the databases talk the same
  language.
• XML was the choice of data interchange format.

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Why XML?

• Why XML ? XML provides the method for defining
  the meaning or semantics of data.
• Example A XML file of the earlier table we
  defined

ltgene_featuresgt ltgene_idgtGBVN32lt/gene_idgt
ltcontig_idgtNT_010651lt/contig_idgt
ltcontig_startgt2354807lt/contig_startgt
ltcontig_endgt2360778lt/contig_endgt
ltcontig_strandgtComplementlt/contig_strandgt lt/gene_f
eaturesgt
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Mapping XML to Relational Database

• The Data Structure in XML is defined in Document
  Type Descrciptor as follows
• lt!ELEMENT gene_id (PCDATA)gt
• lt!ELEMENT contig_id (PCDATA)gt
• lt!ELEMENT contig_start (PCDATA)gt
• lt!ELEMENT contig_end (PCDATA)gt
• lt!ELEMENT contig_sequence (PCDATA)gt
• This kind of DTD also helps us to have control
  over the vocabulary used.
• SQL
• create table gene (
• gene_id varchar(5) primary key,
• contig_id varchar(10) not null,
• contig_start integer not null,
• contig_end integer not null,
• contig_sequence text not null)
• So the DTD can be directly mapped into a
  relational database.

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MAGE-ML As Data Interchage Format
Expression Data
Converter (program)
MAGE-ML
Databases
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Existing Microarray Databases

• Several gene expression databases existBoth
  commercial and non-commercial.
• Most focus on either a particular technolgy or a
  particular organism or both.
• Commercial databases
• Rosetta Inpharmatics and Genelogic, the specifics
  of their internal structure is not available for
  internal scrutiny due to their proprietary
  nature.
• Some non-commercial efforts to design more
  general databases merit particular mention.
• We will discuss few of the most promising ones
• ArrayExpress - EBI
• The Gene expression Omnibus (GEO) - NLM
• The Standford microarray Database
• ExpressDB - Harvard
• Genex - NCGR

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ArrayExpress

• Public repository of microarray based gene
  expression data.
• Implemented in Oracle at EBI.
• Contains
• several curated gene expression datasets
• possible introduction of an image server to
  archive raw image data associated with the
  experiments.
• Accepts submissions in MAGE-ML format via a
  web-based data annotation/submission tool called
  MIAMExpress.
• A demo version of MIAMExpress is available at
  http//industry.ebi.ac.uk/parkinso/subtool/subtyp
  e.html
• Provides a simple web-based query interface and
  is directly linked to the Expression Profiler
  data analysis tool which allows expression data
  clustering and other types of data exploration
  directly through the web.

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Gene Express Omnibus

• The Gene Expression Omnibus ia a gene expression
  database hosted at the National library of
  Medicine
• It supports four basic data elements
• Platform ( the physical reagents used to generate
  the data)
• Sample (information about the mRNA being used)
• Submitter ( the person and organisation
  submitting the data)
• Series ( the relationship among the samples).
• It allows download of entire datasets, it has not
  ability to query the relationships
• Data are entered as tab delimited ASCII
  records,with a number of columns that depend on
  the kind of array selected.
• Supports Serial Analysis of Gene Expression
  (SAGE) data.

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Stanford Microarray Database

• Contains the largest amount of data.
• Uses relational database to answer queries.
• Associated with numerious clustering and analysis
  features.
• Users can access the data in SMD from the web
  interface of the package.
• Disadvantage
• It supports only Cy3/Cy5 glass slide data
• It is designed to exclusively use an oracle
  database
• Has been recently released outside without
  anykind of support !!

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MaxdSQL

• Minor changes to the ArrayExpress object data
  model allowed it to be instantiated as a
  relational database, and MaxdSQL is the resulting
  implementation.
• MaxdSQL supports both Spotted and Affymetrix data
  and not SAGE data.
• MaxdSQL is associated with the maxdView, a java
  suite of analysis and visualisation tools.This
  tool also provides an environment for developing
  tools and intergrating existing software.
• MaxdLoad is the data-loading application software.

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GeneX

• Open source database and integrated tool set
  released by NCGR http//www.ncgr.org.
• Open source - provides a basic infrastructure
  upon which others can build.
• Stores numeric values for a spot measurement
  (primary or raw data), ratio and averaged data
  across array measurements.
• Includes a web interface to the database that
  allow users to retrieve
• Entire datasets, subsets
• Guided queries for processing by a particular
  analysis routine
• Download data in both tab delimited form and
  GeneXML format ( more descriptions later)

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ExpressDB

• ExpressDB is a relational database containing
  yeast and E.coli RNA expression data.
• It has been conceived as an example on how to
  manage that kind of data.
• It allows web-querying or SQL-querying.
• It is linked to an integrated database for
  functional genomics called Biomolecule
  Interaction Growth and Expression Database
  (BIGED).
• BIGED is intended to support and integrate RNA
  expression data with other kinds of functional
  genomics data

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Survey of existing microarray systems
This survey is based on the article published
in BRIEFINGS IN BIOINFORMATICS, Vol 2, No 2, pp
143-158, May 2001 A comparison of
microarray databases
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The Microarray Gene Expression Database Group
(MGED)

• History and Future
• Founded at a meeting in November, 1999 in
  Cambridge, UK.
• In May 2000 and March 2001 development of
  recommendations for microarray data annotations
  (MAIME, MAML).
• MGED 2nd meeting
• establishment of a steering committee consisting
  of representatives of many of the worlds leading
  microarray laboratories and companies
• MGED 4th meeting in 2002
• MAIME 1.0 will be published
• MAML/GEML and object models will be accepted by
  the OMG
• concrete ontology and data normalization
  recommendations will be published.
• information can be obtained from
  http//www.mged.org

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The Microarray Gene Expression Database Group
(MGED)

• Goals
• Facilitate the adoption of standards for
  DNA-array experiment annotation and data
  representation.
• Introduce standard experimental controls and data
  normalization methods.
• Establish gene expression data repositories.
• Allow comparision of gene expression data from
  different sources.

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MGED Working Groups

• Goals
• MIAME Experiment description and data
  representation standards - Alvis Brazma
• MAGE Introduce standard experimental controls
  and data normalization methods - Paul Spellman.
  This group includes the MAGE-OM and MAGE-ML
  development.
• OWG Microarray data standards, annotations,
  ontologies and databases - Chris Stoeckert
• NWG Standards for normalization of microarray
  data and cross-platform comparison - Gavin
  Sherlock

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References

• URL
• Tutorial on Information Management for Genome
  Level Bioinformatics, Paton and Goble, at VLDB
  2001 http//www.dia.uniroma3.it/vldbproc/tutEur
  opea
• European Molecular Biology Network
  http//www.embnet.org/
• Univ. Manchester site (with relational version of
  Microarray data representation, and links to
  other sites)
• http//www.bioinf.man.ac.uk
• Database textbook with absolutely no
  bioinformatics coverage
• For Microarray Data
• http//linkage.rockefeller.edu/wli/microarray/