BiGCaT BioinformaticsHunting strategy of the bigcat

BiGCaT,BiGCaT,bridge between two universitiesbridge between two universities

Universiteit MaastrichtPatients, Experiments,

Arrays and Loads of Data

TU/eIdeas & Experience in Data Handling

BiGCaT

Major Research FieldsMajor Research Fields

CardiovascularResearch

Nutritional &Environmental

Research

BiGCaT

What are we looking for?What are we looking for?

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?Differences in gene expression?Between e.g.:Between e.g.:• healthy and sickhealthy and sick• different stages of disease progressiondifferent stages of disease progression• different stages of healingdifferent stages of healing• failed and successful treatment• more and less vulnerable individuals

Shows:Shows:• important pathways and receptors important pathways and receptors • which then can be influencedwhich then can be influenced

The transfer of informationThe transfer of informationfrom DNA to protein.from DNA to protein.

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

Eukaryotic genesEukaryotic genesin somewhat more detailin somewhat more detail

Gene Gene expression measurementexpression measurement

Functional genomics/transcriptomics:Changes in mRNA– Gene expression microarrays– Suppression subtraction lybraries–

Proteomics:Changes in protein levels– 2D gel electrophoresis – Antibody arrays–

DNA mRNA protein

Gene expression Gene expression arraysarraysMicroarrays: 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 cat and its prey:the datathe data

Comprises: 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

HuntingHunting

Comprises: Analyze reporters Data pretreatment Finding patterns in expression Evaluate biological significance of

those patterns

Reporter analysisReporter 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 solutionsTwo 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 solutionsTwo 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 Next slide shows data of one single actual microarraysingle 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 Next slide shows same data after processingafter 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 Final slide shows information for one single reporterfor one single reporter

This signifies one single spot It is a known gene:

an UDP glucuronyltransferase Raw data and fold change are

shown

Secondary Secondary AnalysesAnalyses

Gene clustering(find genes that behave equally)

Cluster evaluation(what do we see in clusters …)

Physiological evaluation(for arrays, proteomics, clusters)

Understand the regulation

2

time

Exp

r. le

vel

Clustering: find genes with same pattern

T1 signal

T2

sig

na

l

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 evaluationCluster 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 regulationUnderstanding 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’sUnderstanding 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 involvedPeople involvedBigcat 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 studentsCARIM: 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 keijerCfg 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.