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BiGCaT Bioinformatics. Hunting strategy of the bigcat. BiGCaT, bridge between two universities. TU/e Ideas & Experience in Data Handling. Universiteit Maastricht Patients, Experiments, Arrays and Loads of Data. BiGCaT. Major Research Fields. Nutritional & Environmental Research.

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bigcat bridge between two universities
BiGCaT,bridge between two universities

TU/eIdeas & Experience in Data Handling

Universiteit Maastricht

Patients, Experiments,Arrays and Loads of Data


major research fields
Major Research Fields

Nutritional &EnvironmentalResearch



what are we looking for1
What are we looking for?

Different conditions show different levels of gene expression for specific genes

differences in gene expression
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
The transfer of informationfrom DNA to protein.

From: Alberts et al. Molecular Biology of the Cell, 3rd edn.

gene expression measurement
Gene expression measurement

DNA  mRNA protein

Functional genomics/transcriptomics:

Changes in mRNA

  • Gene expression microarrays
  • Suppression subtraction lybraries


Changes in protein levels

  • 2D gel electrophoresis
  • Antibody arrays
gene expression arrays
Gene expression arrays

Macroarrays: absolute radioactive signal. Validation.

Microarrays: relative fluorescense signals. Identification.

layout of a microarray experiment
Layout of a microarray experiment
  • Get the cells
  • Isolate RNA
  • Make fluorescent cDNA
  • Hybridize
  • Laser read out
  • Analyze image
the cat and its prey the data
The cat and its prey:the data


  • Known cDNA sequences (not known genes!)on the array = reporters
  • Data sets typically contain 20,000 image spot intensity values in 2 colors
  • One experiment often contains multiple data points for every reporter (e.g. times or treatments)
  • Each datapoint can (should) consist of multiple arrays

Bioinformatics should translate this in to useful biological information



  • Analyze reporters
  • Data pretreatment
  • Finding patterns in expression
  • Evaluate biological significance of those patterns
reporter analysis
Reporter analysis
  • 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
Blast reporters against what?
  • 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
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 X-linked DB (Blast against EMBL subset get Swissprot/trEMBL)
two implemented solutions1
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 X-linked DB (Blast against EMBL subset get Swissprot/trEMBL)
scotland holland 1 0
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
Next slide shows data of one single actual microarray
  • 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
Next slide shows same data after processing
  • 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
Final slide shows information for one single reporter
  • This signifies one single spot
  • It is a known gene:an UDP glucuronyltransferase
  • Raw data and fold change are shown
secondary analyses
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


T1 signal


Clustering: find genes with same pattern

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
Cluster evaluation
  • 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
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
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
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 PuyandeeraIOP 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 ClementOrganon: Jan Klomp, Rene van Schaik.

BioAsp: Marc Laarhoven.