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Standards and Data Handling with Microarrays

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MAGE. Computer standard. A standard 'object model' for Microarrays ... MAGE-ML. A collection of these objects in an XML format ... – PowerPoint PPT presentation

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Title: Standards and Data Handling with Microarrays


1
Standards and Data Handling with Microarrays
  • David J Craigon

2
What is he going to talk about?
  • Data handling- some database stuff
  • followed by
  • Standards- the MGED group of standards

3
What is he going to talk about?
  • Data handling- some database stuff
  • followed by
  • Mostly standards- the MGED group of standards
  • MIAME
  • Ontology
  • MAGE

4
Some Databases
  • ArrayExpress- the EMBL of Microarrays- MAGE based
  • MIAMEExpress
  • Various species specific databases

5
The Microarray Gene Expression Data Society (MGED)
  • A standards body for microarrays
  • Split into 4 working groups
  • MIAME
  • Ontology
  • MAGE
  • Data processing

http//www.mged.org
6
Why have standards?
  • Standards allow us to
  • Be prescriptive (Oi you, do this)
  • Aid communication between people
  • Produce best practices
  • Enables replication of experiments

7
The Microarray Gene Expression Data Society (MGED)
  • A standards body for microarrays
  • Split into 4 working groups
  • MIAME
  • Ontology
  • MAGE
  • Data processing

http//www.mged.org
8
The MIAME Standard
  • Pronounced like the city. Miami.
  • An abbreviation for Minimum Information about A
    Microarray Experiment

9
MIAME
  • Scientific papers should contain all information
    required to replicate an experiment
  • What information do you need for a microarray
    experiment?
  • People talk of documents being MIAME-compliant.

10
A guide to MIAME
11
World of Microarrays
  • Array Design
  • Experimental Design
  • Samples used, extract preparation and labelling
  • Hybridization procedures and parameters
  • Measurement data and specifications of data
    processing

12
Array Design
13
ArrayArray as a whole
Platform Type
Dimensions of the array
Surface and coating specifications
Number of Features
Availability of array, or production protocol
14
Some terminology
  • Any given slide/genechip/whatever is an Array
  • The design of the array (which spot goes where,
    and where all of the spots are) is an Array
    Design
  • Any spot etc. on the array is a Feature
  • What the feature is made of is a Reporter

15
Features
  • A feature is a specific spot on an ArrayDesign
  • Record
  • Feature dimensions
  • How the feature is attached to the array
  • Where each individual feature is on the
    ArrayDesign

16
Reporters
  • A reporter is a specific probe
  • You could spot a reporter multiple times on one
    array, or even on multiple arrays

17
Reporters
  • Record
  • What type of reporter it is
  • Single or double stranded
  • The sequence if available
  • If not PCR primers, approximate length
  • How this reporter was made
  • What it reports for (database reference)

18
Special features
  • Control features
  • How is it a control
  • Composite features
  • How combined?
  • What does it report for?

19
Samples used, extract preparation and labelling
20
Biosource and Biosample
  • Biosources and Biosamples are either
  • The original, living things that the RNA was
    extracted from
  • Or an extract from that organism
  • e.g. Patient, Mouse, mouse brain, RNA extract are
    all Biosamples/Biosources.
  • Biosources are the original material you start
    with- biosamples are all subsequent steps.

21
Biosourcerecord
Contact details for sample (where did you get it
from?)
Mouse (?)
Arabidopsis
Human
Organism
22
More BioSource
  • Relevant descriptors such as
  • Sex
  • Age
  • Developmental stage
  • Organism part
  • Cell type
  • Plant strain or line
  • Genetic variation
  • Individual genetic characteristics
  • Disease state
  • Additional clinical information
  • Which individual?

23
Progression of a microarray experiment
Protocol
Protocol
Protocol
Protocol
Seed
Plant
Cold plant
RNA extract
Labelled RNA extract
BioSource
BioSample
BioSample
BioSample
LabelledExtract
24
Protocols
  • Between each step is a protocol
  • e.g.
  • Growth protocol
  • Sample treatment protocol
  • Separation technique
  • Extraction protocol
  • Labelling protocol

25
Extraction protocol
  • Extraction method
  • RNA, mRNA, genomic DNA extracted
  • Amplification steps

26
Labelling Protocol
  • Amount labelled
  • Label used
  • Label incorporation method
  • Spikes type, qualifier (e.g. concentration,
    expected ratio), the elements on arrays that
    match the spike

27
Hybridisation Protocol
  • Which samples were hybridised to which arrays
  • Solution used
  • Blocking agent
  • Wash procedure
  • Quantity of labelled extract used
  • Time, concentration, volume, temperature
  • Description of hybridisation instruments

28
Measurement Data and Specifications of Data
Processing
  • Three steps
  • Scan
  • Raw analysis
  • Summary analysis

29
Scanning protocol
  • Scanning Hardware, Software used
  • Scan Parameters used e.g. PMT voltage
  • Scanned images (?)

30
Image analysis protocol
  • Image analysis software and version (e.g. GenePix
    version 4)
  • Description of the algorithm used
  • Parameters used
  • All output from the analysis

31
Normalised and Summary data
  • Data processing protocol
  • Gene expression data tables derived from the
    experiment as a whole.
  • Derived measurement value
  • Reliability indicators

32
Experiment
  • About the complete experiment

33
Experiment description
  • Which Arrays are part of the experiment?
  • Which Samples are part of the experiment?

34
Experiment Design
  • Contact details for the experiment
  • Type of the experiment (gene knock-out,
    treatment)
  • Arrays and experimental factors
  • Quality control steps taken
  • Description for the experiment

35
No more MIAME!
36
The Microarray Gene Expression Data Society (MGED)
  • A standards body for microarrays
  • Split into 4 working groups
  • MIAME
  • Ontology
  • MAGE
  • Data processing

http//www.mged.org
37
Computer Standards
  • Computers are a lot less forgiving than people
  • Computer standards allow lots of different
    software to work together

38
Computer standards
  • People are beginning to realise that it is useful
    for computers to understand databases, as well as
    people

39
Ensembl example
40
Advantages of data standards
  • Use many databases together
  • Better automated searching
  • One piece of software can use many databases
    interchangeably

41
What is an ontology?
  • An ontology is like a dictionary- it defines many
    terms
  • An ontology is a restricted dictionary- it
    attempts to avoid duplication
  • An ontology is a structured dictionary- it states
    how the terms relate to each other

42
Demonstration of Gene Ontology, MGED ontology
43
The Microarray Gene Expression Data Society (MGED)
  • A standards body for microarrays
  • Split into 4 working groups
  • MIAME
  • Ontology
  • MAGE
  • Data processing

http//www.mged.org
44
MAGE
  • Computer standard
  • A standard object model for Microarrays
  • Describes the various parts of a microarray
    experiment in a standard way

45
MAGE-ML
  • A collection of these objects in an XML format
  • One file can represent an entire experiment, part
    of an experiment, many experiments, or part of
    many experiments
  • Example- Affymetrix supply ArrayDesign objects in
    a file to describe all of their chips

46
The end!
  • Ive shown you
  • Some microarray databases you can get data from
  • MIAME
  • Ontologies
  • MAGE
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