Transcript BiGCaT

Hunting
strategy
of the bigcat
BiGCaT
Bioinformatics
BiGCaT,
bridge between two universities
Universiteit Maastricht
Patients, Experiments,
Arrays and
Loads of Data
BiGCaT
TU/e
Ideas & Experience
in Data Handling
Major Research Fields
Cardiovascular
Research
BiGCaT
Nutritional &
Environmental
Research
What are we looking for?
What are we looking for?
Different conditions
show different levels
of gene expression
for specific genes
Differences in gene expression?
Between e.g.:
• healthy and sick
• different stages of disease progression
• different stages of healing
• failed and successful treatment
• more and less vulnerable individuals
Shows:
• important pathways and receptors
• which then can be influenced
The transfer of information
from DNA to protein.
From: Alberts et al. Molecular Biology of the Cell, 3rd edn.
Eukaryotic genes
in somewhat more detail
Gene expression measurement
DNA  mRNA  protein
Functional genomics/transcriptomics:
Changes in mRNA
– Gene expression microarrays
– Suppression subtraction lybraries
–
Proteomics:
Changes in protein levels
– 2D gel electrophoresis
– Antibody arrays
–
Gene expression arrays
Microarrays: relative
fluorescense signals.
Identification.
Macroarrays: absolute
radioactive signal.
Validation.
Layout of a microarray experiment
1) Get the cells
2) Isolate RNA
3) Make fluorescent
cDNA
4) Hybridize
5) Laser read out
6) Analyze image
The cat and its prey:
the data
Comprises:
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Known cDNA sequences (not known genes!)
on the array = reporters
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Data sets typically contain 20,000 image spot
intensity values in 2 colors
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One experiment often contains multiple data
points for every reporter (e.g. times or
treatments)
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Each datapoint can (should) consist of multiple
arrays
Bioinformatics should translate this in to useful
biological information
Hunting
Comprises:
 Analyze reporters
 Data pretreatment
 Finding patterns in expression
 Evaluate biological significance of
those patterns
Reporter analysis
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Reporter sequence must be known
(can be sequenced using digest
electrophoresis).
Lookup sequence in genome databases
(e.g. Genbank/Embl or Swissprot)
Will often find other RNA experiments
(ESTs) or just chromosome location.
Blast reporters against what?
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Nucleotide databases (EMBL/Genbank)
Disadvantages: many hits, best hit on
clone, we actually want function (ie
protein)
Nucleotide clusters (Unigene)
Disadvantage: still no function
Protein databases (Swissprot+trEMBL)
Disadvantages: non coding sequence
not found, frameshifts in clones
Two implemented solutions
Start with Unigene (from Blastn or
platform provider), mine using
SRS (direct, through PDB, through
PIR) -> Swissprot/trEMBL
 Use dedicated EMBL-Swissprot Xlinked DB (Blast against EMBL
subset get Swissprot/trEMBL)

Two implemented solutions
Start with Unigene (from Blastn or
platform provider), mine using
SRS (direct, through PDB, through
PIR) -> Swissprot/trEMBL
 Use dedicated EMBL-Swissprot Xlinked DB (Blast against EMBL
subset get Swissprot/trEMBL)

Scotland - Holland: 1-0?
Check Affymetrix reporter sequences.
-
Each reporter 16 25-mer probes.
Blast against ENSEMBL genes
(takes 1 month on UK grid).
Use for cross-species analysis
Adapt RMA statistical analysis in
Bioconductor
Next slide shows data of one
single actual microarray

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Normalized expression shown for both
channels.
Each reporter is shown with a single dot.
Red dots are controls
Note the GEM barcode (QC)
Note the slight error in linear
normalization (low expressed genes are
higher in Cy5 channel)
Next slide shows same data
after processing
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Controls removed
Bad spots (<40% average area)
removed
Low signals (<2.5 Signal/Background)
removed
All reporters with <1.7 fold change
removed (only changing spots shown)
Final slide shows information
for one single reporter

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This signifies one single spot
It is a known gene:
an UDP glucuronyltransferase
Raw data and fold change are
shown
Secondary Analyses
Gene clustering
(find genes that behave equally)
 Cluster evaluation
(what do we see in clusters …)
 Physiological evaluation
(for arrays, proteomics, clusters)
 Understand the regulation

Expr. level
T2 signal
Clustering: find genes with same pattern
2
T1 signal
time
Left hand picture shows expression patterns for 2 genes (these
should probably end up in the same cluster).
Right hand picture shows the expression vector for one gene
for the first 2 dimensions. Can be normalized by amplitude
(circle) or relatively (square).
Cluster evaluation
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Group genes (function, pathway,
regulations etc.)
Find groups in patterns using
visualization tools and automatic
detection.
Should lead to results like:
“This experiment shows that a large number
of apoptosis genes are up-regulated during
the early stage after treatment. Probably
meaning that cells are dying”
Example of GenMAPP results:
Manual lookup on a MAPP
Understanding regulation
The main idea: co-regulated genes could have
common regulatory pathways.
The basic approach: annotate transcription
factor binding sites using Transfac and
use for supervised clustering.
The problem: each gene has hundreds of
tfb’s.
Solution? Use syntenic regions using rVista
(work in progress with Rick Dixon)
Understanding QTL’s
Get blood pressure QTLs:
from ENSEMBL/cfg Welcome group.
Look up functional pathways and Go
annotations using GenMapp: virtual
experiment assume all genes in QTL are
changing.
Create a new blood pressure Mapp: confront
this with real blood pressure/heart failure
microarray data.
Work in progress TU/e MDP3 group.
People involved
Bigcat Maastricht: Rachel van Haaften (IOP), Edwin ter Voert (BMT),
Joris Korbeeck (BMT/UM), Willem Ligtenberg (IOP), Stan Gaj (tUL), Chris Evelo
Tue: Peter Hilbers, Huub ten Eijkelder, Patrick van Brakel, lots of students
CARIM: Yigal Pinto, Umesh Sharma, Blanche Schroen, Matthijs Blankesteijn,
Jos Smits, Jo de Mey, Danielle Curfs, Kitty Cleutjens, Natasja Kisters, Esther
Lutgens, Birgit Faber, Petra Eurlings, Ann-Pascalle Bijnens, Mat Daemen, Frank
Stassen, Marc van Bilssen, Marten Hoffker.
NUTRIM: Wim Saris, Freddy Troost, Johan Renes, Simone van Breda.
GROW: Daisy vd Schaft, Chamindie Puyandeera
IOP Nutrigenomics: Milka Sokolovic, Theo Hackvoort, Meike Bunger, Guido
Hooiveld, Michael Müller, Lisa Gilhuis-Pedersen, Antoine van Kampen, Edwin
Mariman, Wout Lamers, Nicole Franssen, Jaap keijer
Cfg Welcome group: Neil Hanlon (Glasgow) Gontran Zepeda (Edinburg),
Rick Dixon (Leicester), Sheetal Patel (London).
Paris leptin group: Soraya Taleb, Rafaelle Cancello,Nathalie Courtin, Carine
Clement
Organon: Jan Klomp, Rene van Schaik.
BioAsp: Marc Laarhoven.