1. Making Sense of Public Domain Expression Data- GeneVestigator
Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09

2. Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09
On the Agenda -
Microarray databases – characteristics
pros and cons
Examples:
GEO and ArrayExpress
GeneVestigator - meta-analytical approach
Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09

3. Meta-data in Microarray Experiments
Gene expression studies generate large amounts of data !
Data and Meta-data in Microarray Experiments
Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09

4. Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09
Properties of High-throughput Data
Microarray databases: have the ability to accept, store and export (share) large quantities of data.
Data (stored) contain:
Many genes
Many samples
Various organisms/tissues
Variety of biological phenomena
Time course
Replicates
Different technologies: various data format
Data Retrieval:
user-friendly
web-based interfaces
Links to Analysis Tools
Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09

5. Gene Expression Matrix
The final gene expression matrix (on the right) is needed for higher level analysis and mining
Images
Spots
Samples
Spot/Image quantiations 
Genes
Gene expression levels
Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09
Expression Matrix

6. Microarray Data Precision and Loss
Electron microscopy
Only provided in 0.1% of public experiments
Processed data loses precision !
90% of CEL files generated from microarray experiments have never been deposited to any repository. Stokes et al. BMC Bioinformatics (Suppl 6):S18  
Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09

7. Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09
Microarray Data Formats
Raw image data, the intensity of the signal at each spot is proportional to the expression level of the gene under test.
Image intensities are quantified using image analysis software.
B. Raw numerical data (signal intensities).
C. Processed data. A. B. C.
Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09

8. Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09
Problem: Raw Data
Complete description of complex experiments is desired.
We don’t always know what’s important:
“Noise” probes could end up being informative (e.g. detection of a splice variant).
The Future
Better (more accurate) summarization algorithms will emerge.
New uses for raw data may emerge.
Challenge: Store the raw data in accessible form.
Different labs have different needs – a central system is needed !
Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09

9. Complexity and Categories of Data and MIAME 6 parts
The MIAME (Minimum Information About a Microarray Experiment) guidelines contain standards for publication of information. Brazma et al. (2001),
Nature Genetics 29(4),
Publication
Experimental design
Source
(e.g., Taxonomy)
Sample –
Source & treatment,
prep. & labelling
Hybridisation
Array
design
Gene
(e.g., EMBL)
Normalization
Data
measurements
Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09 18 9

10. Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09
Microarray Database Repositories
are Biased
The relative size of each pie corresponds to the number of experiments contained in each repository.
All human data
Mostly custom arrays
Mostly old data
Mostly human data
Mainly
Affy chips
Stokes et al. BMC Bioinformatics (Suppl 6): S18
Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09

11. Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09
Overlaps of Data Between Repositories
Stokes et al. BMC Bioinformatics (Suppl 6): S18  
Total Experiments: 2376
August 2005 – June 2006
Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09

12. User-Friendly Microarray Databases
Many gene expression databases exist: commercial and non-commercial.
Most focus on either a particular technology, particular organism or both.
We will discuss most promising ones:
ArrayExpress – EBI (AE)
The Gene expression Omnibus (GEO; NCBI)
GeneVestigator
Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09

13. Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09
The Gene Expression Omnibus is a public repository in the Entrez database that includes high-throughput gene expression data, hosted at the National library of Medicine (NIH).
GEO was designed to accommodate diverse types of data.
Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09

14. Gene Express Omnibus - Experiment centered view
(GDS)

15. Gene Express Omnibus - Gene centered view
Example: GDS563
Expression profile of the Dystrophin gene in a DataSet examining skeletal muscle biopsies from 12 Duchenne muscular dystrophy patients and 12 normal subjects.
Red bars: level of abundance of an individual transcript across the Samples that make up a DataSet. Values are presented as arbitrary units. Single channel: normalized Values signal count data.
Dual channel: submitted Values are normalized log ratios. Blue square rank order, give an indication of where the expression of that gene falls with respect to all other genes on that array (enrichment).
Duchenne
Normal
Experimental design
Faded bars/squares: These correspond to Affymetrix 'Detection call' = Absent.
Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09

16.
Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09
Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09 16

17. Query ArrayExpress Annotations Experiments and description Condition
Click
Gene name
Species
Results: a list of all experiments, ordered by p value.
For each experiment: short description, experimental factors and gene expression.
Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09

18. Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09
Query ArrayExpress – similar expressed genes
Select the ‘find 3 closest genes’ option.
IER2, FOS, JUN, have similar expression to nfkbia.
Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09

19. Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09
HeatMap Atlas Output
Number of up/down regulated genes
Experimental condition
Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09

20. Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09
GeneVesigator –a reference expression database and meta-analysis system
Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09

21. Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09
Genevestigator – a system for the meta-analysis of microarray data
A database & Web-browser data mining interface for Affymetrix GeneChip data, based on a the new concept of “Meta-Profiles“, relying on reference expression databases.
Allows biologists to study the expression and regulation of genes in a broad variety of contexts by summarizing information from hundreds of manually curated microarray experiments.
Workspaces and views can be stored into files and re-opened for another analysis session (*.gvw which stands for GenevestigatorWorkspace).
Application server
Java application
Analysis output
of the Genevestigator system
Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09

22. Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09
Database Content and Quality
Database consist of large and various manually curated and quality-controlled Affymetrix chips:
Quality control of EACH experiment is manually done by Genevestigator curators using a pipeline of Bioconductor packages performing normalization and probe-level analysis.
Low quality arrays are characterized by:
fall out of range relative to the other arrays from the same experiment,
exhibit higher RNA degradation,
particularly noisy,
do not correlate with replicate samples.
Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09 22

23. Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09
User Hardware Requirements
Genevestigator is a web-based application running in Java.
Java applet provides several advantages:
users don’t have to install any software
users always work with the latest software release
Java is more powerful than HTML/Javascript for data manipulation
To run the application, client machines must have Java runtime environment
(JRE; version or higher) installed (usually available by default on PCs).
JRE is freely available for download at Sun Microsystems (
To optimally work with the Genevestigator application, we recommend:
screen resolution: 1024 x 768 or higher
memory: preferably 512 MB RAM or more
Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09

24. Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09
GeneVestigator Species Availability
Species: Human Mouse Rat
[Mammals]
Arrays:
Human 133_2 & Human Genome 10k k k
1109, 3786, 2782
Mouse Genome
12k k
3071, 1967
Rat Genome
8k 31k
2146, 858
Number
of arrays:
Species: Arabidopsis Barley Rice Soybean
[Plants]
Barley Genome
22k
706
Rice Genome
22k -
Arrays:
Arabidopsis Genome 22k
3110
Number
of arrays:
Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09

25. Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09
Data Sources and Referencing
The Genevestigator analysis platform comprises a large database of
manually curated microarray experiments collected from the public domain
or from individual contributors. The array annotations necessary for data
analysis were retrieved from public repositories and/or, if insufficiently available, from the authors themselves.
Genevestigator contains data from the following repositories and databases:
Link
Database
Gene Expression Omnibus (GEO)
ArrayExpress
ChipperDB
The Arabidopsis Information Resource (TAIR)
MUSC Microarray Database
Public Expression Profiling Resource (PEPR)
NASC Microarray Database (NASCArrays)
NIH Neuroscience Microarray Consortium
to geoss.html
Gene Expression Open Source System (GEOSS)
RNA Abundance Database (RAD)
Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09

26. Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09
GeneVestigator –
focus on gene expression in the context of:
Time (Gene expression during stages of development\life-cycle).
Space (Tissue specific expression).
Response (Expression caused by stimuli: biotic stress, abiotic stress, chemical, hormone, light, drug treatment, disease).
Users can query the database to retrieve the expression patterns of individual genes throughout chosen environmental conditions, growth stages, or organs.
Reversely, mining tools allow users to identify genes specifically expressed during selected stresses, growth stages, or in particular organs
Access:
Free / By license
Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09

27. Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09

28. Thank-you ! Dr. Metsada Pasmanik-Chor Bioinformatics Unit,
Life Science, TAU
Tel: x 6992
Bioinfo. Unit webpage: 28
Bioinformatics Intro, 15/12/2008, Metsada Pasmanik-Chor
Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09