Post on 30-Dec-2015
transcript
Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09
1
Making Sense of Public Domain Expression Data- GeneVestigator
Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09
2
Microarray databases – characteristics pros and cons
Examples:• GEO and ArrayExpress• GeneVestigator - meta-analytical approach
On the Agenda -
Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09
3http://titan.biotec.uiuc.edu/cs491jh/slides/cs491jh-Yong.ppt#268,6,Capturing Data and Meta-data in Microarray Experiments
Meta-data in Microarray Experiments
Gene expression studies generate large amounts of data !
Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09
4
Properties of High-throughput DataMicroarray databases: have the ability to accept, store and export (share) large quantities of data.
Data (stored) contain:Many genesMany samplesVarious organisms/tissuesVariety of biological phenomenaTime courseReplicatesDifferent technologies: various data format
Data Retrieval:user-friendly web-based interfaces
Links to Analysis Tools
Gene Expression Matrix
Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09
5
The final gene expression matrix (on the right) is needed for higher level analysis and mining
Samples
Gen
es
Gene expression levels
Images
Spo
ts
Spot/Image quantiations
http://titan.biotec.uiuc.edu/cs491jh/slides/cs491jh-Yong.ppt#271,8,Gene Expression Matrix
Microarray Data Precision and Loss
Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09
6
90% of CEL files generated from microarray experiments have never been deposited to any repository. Stokes et al. BMC Bioinformatics 2008 9(Suppl 6):S18
http://www.bio-miblab.org/arraywiki
Only provided in 0.1% of public experiments
Electron microscopy
Processed data loses precision !
Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09
7
A. 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.
Microarray Data Formats
A.A.
B.B.C.C.
Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09
8
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
Publication
Hybridisation Arraydesign
Sample – Source & treatment,
prep. & labelling
Source(e.g., Taxonomy)
Experimental design
Normalization
Gene(e.g., EMBL)
Datameasurements
http://www.ict.ox.ac.uk/odit/projects/digitalrepository/docs/workshop/Helen_Parkinson-RDMW0608.ppt#429,18,Slide 18
The MIAME (Minimum Information About a Microarray Experiment) guidelines contain standards for publication of information. Brazma et al. (2001), Nature Genetics 29(4), 365-71
Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09
10
The relative size of each pie corresponds to the number of experiments contained in each repository.
Microarray Database Repositoriesare Biased
Stokes et al. BMC Bioinformatics 2008 9 (Suppl 6): S18 http://www.biomedcentral.com/1471-2105/9/S6/S18
All human data
Mostly human data
Mostly old data
Mostly custom arrays
MainlyAffy chips
Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09
11
Stokes et al. BMC Bioinformatics 2008 9 (Suppl 6): S18 http://www.biomedcentral.com/1471-2105/9/S6/S18
Overlaps of Data Between Repositories
Total Experiments: 2376 August 2005 – June 2006
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
12
Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09
13
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.
http://www.ncbi.nlm.nih.gov/geo/
14
(GDS)
Gene Express Omnibus - Experiment centered view
Gene Express Omnibus - Gene centered view
Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09
15
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).
Example: GDS563
Faded bars/squares: These correspond to Affymetrix 'Detection call' = Absent.
Duchenne Normal
Experimental design
Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09
16Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09
16
http://www.ebi.ac.uk/microarray-as/ae/
Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09
17
Query ArrayExpress
Gene name Condition
Species
Experiments and description
Annotations
Click
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
Query ArrayExpress – similar expressed genesSelect the ‘find 3 closest genes’ option.IER2, FOS, JUN, have similar expression to nfkbia.
Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09
19
HeatMap Atlas Output
http://www.ebi.ac.uk/microarray-as/atlas/qr?q_gene=saa4&q_updn=updn&q_orgn=MUS+MUSCULUS&q_expt=%28all+conditions%29&view=heatmap&view=
Experimental condition
Number of up/down regulated genes
Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09
20
https://www.genevestigator.com/gv/index.jsp GeneVesigator –a reference expression database and meta-analysis system
Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09
21
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).
Genevestigator – a system for the meta-analysis of microarray data
http://bar.utoronto.ca/ICAR19/ICAR19_BioinfoWorkshop%20-%20Genevestigator.ppt#257,2,Overview of the Genevestigator system
Application server
Java application
Analysis output
Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09
22
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
23
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 1.4.2 or higher) installed (usually available by default on PCs). JRE is freely available for download at Sun Microsystems (http://www.Java.com).
To optimally work with the Genevestigator application, we recommend:• screen resolution: 1024 x 768 or higher• memory: preferably 512 MB RAM or more
User Hardware Requirements
Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09
24
Species: Human Mouse Rat [Mammals]
GeneVestigator Species Availability
Species: Arabidopsis Barley Rice Soybean[Plants]
Human 133_2 & Human Genome 10k 20k 47 k
1109, 3786, 2782
Arrays:
Numberof arrays:
Mouse Genome
12k 40k
3071, 1967
Rat Genome
8k 31k
2146, 858
Arabidopsis Genome 22k
3110
Arrays:Numberof arrays:
Barley Genome 22k
706
Rice Genome 22k
-
Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09
25
Data Sources and ReferencingThe Genevestigator analysis platform comprises a large database ofmanually curated microarray experiments collected from the public domainor from individual contributors. The array annotations necessary for dataanalysis were retrieved from public repositories and/or, if insufficiently available, from the authors themselves.
Genevestigator contains data from the following repositories and databases:
Database Link
Gene Expression Omnibus (GEO)http://www.ncbi.nlm.nih.gov/geo/
ArrayExpresshttp://www.ebi.ac.uk/arrayexpress/
ChipperDBhttp://chipperdb.chip.org/adb/adb-home
The Arabidopsis Information Resource (TAIR)http://www.arabidopsis.org/
MUSC Microarray Databasehttp:proteogenomics.musc.eduma
Public Expression Profiling Resource (PEPR)http://pepr.cnmcresearch.org
NASC Microarray Database (NASCArrays)http://affymetrix.arabidopsis.info/narrays/experimentbrowse.pl
NIH Neuroscience Microarray Consortiumhttp://arrayconsortium.tgen.org/np2/home.do
Gene Expression Open Source System (GEOSS) https://genes.med.virginia.edu/intro to geoss.html
RNA Abundance Database (RAD) http://www.cbil.upenn.edu/RAD/php/index.php
Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09
26
1. Time (Gene expression during stages of development\life-cycle).2. Space (Tissue specific expression).3. Response (Expression caused by stimuli: biotic stress, abiotic stress, chemical,
hormone, light, drug treatment, disease).
Access:
Free / By license
GeneVestigator – focus on gene expression in the context of:
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
Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09
27http://sbw.kgi.edu/
Metsada Pasmanik-Chor, TAU Bioinformatics Unit, 19/3/09
28Bioinformatics Intro, 15/12/2008, Metsada Pasmanik-Chor
28
Dr. Metsada Pasmanik-ChorBioinformatics Unit,Life Science, TAU
Tel: x 6992E-mail: metsada@bioinfo.tau.ac.il
Bioinfo. Unit webpage: http://bioinfo.tau.ac.il