introduction to

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Introduction to

Bioinformatics

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Introduction to Bioinformatics.

LECTURE 9: Clustering gene expression

* Chapter 9: The genomics of wine-making

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Introduction to BioinformaticsLECTURE 9: CLUSTERING GENE EXPRESSION

9.1 Chateau Hajji Feruz Tepe* Wine making dates back to at least 5000 BC, based on archeological finds in Iran: Hajji Feruz Tepe .

Overview of Neolithic houses at Hajji Feruz Tepe that yielded six wine jars in the floor along one wall of the room.

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Introduction to BioinformaticsLECTURE 9: CLUSTERING GENE EXPRESSION

* Wine making dates back to at least 5000 BC, based on archeological finds in Iran: Hajji Feruz Tepe .

One of six jars once filled with wine from the Neolithic residence at Hajji Feruz Tepe (Iran).

Chemical analysis of patches of a reddish residue covering the interior of this vessel showed that this originally was resinated wine.

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Introduction to BioinformaticsLECTURE 9: CLUSTERING GENE EXPRESSION

* Recipe for wine making:

1. fruit juice (or other sugar-rich liquid)

2. yeast: Saccharomyces cerevisiae

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Introduction to Bioinformatics9.1 CHATEAU HAJJI FERUZ TEPE

Yeast (Saccharomyces cerevisiae) is a unicellular fungus found naturally in grapevines and responsible of wine-making fermenting sugars and producing alchool.

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Introduction to Bioinformatics9.1 CHATEAU HAJJI FERUZ TEPE

From being budded off from its parent cell, to reproducing its own offspring, each yeast cell goes through a number of typical steps that also involve changes in gene expression, turning whole pathways on and off.

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Introduction to BioinformaticsLECTURE 9: CLUSTERING GENE EXPRESSION

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Introduction to Bioinformatics9.1 CHATEAU HAJJI FERUZ TEPE

Remember, a gene is an on-off switch and RNa and proteins are messengers between the genes.

If a gene is ‘on’ the gene is ‘expressed’. The degree to which the gene is expressed is called the expression level of the gene.

If a gene is off, it can be said that it has expression level zero.

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Introduction to Bioinformatics9.1 CHATEAU HAJJI FERUZ TEPE

Today the study of such phenomena is possible through the technology of microarray that can measure the expression level of every gene in a cell.

With the gene expression data, genes can be clustered on the basis of the similarity of their expression profiles.

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Introduction to Bioinformatics9.1 CHATEAU HAJJI FERUZ TEPE

* With water, sugar and flour, yeast ferments the sugars in the dough and produces carbon dioxide CO2 (this causes the dough to rise). In this process it produces alcohol as a by-product (originally perhaps as near-toxic protection!).

* When the sugar supply is exhausted S. cerevisiae must find a new source of energy: when oxygen is available it shifts to respiration: alcohol now becomes the source of energy.

* This state change is called the diauxic shift

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Introduction to Bioinformatics9.1 CHATEAU HAJJI FERUZ TEPE

* S. cerevisiae is (one of) the most studied organism in biology

* S. cerevisiae is a complex unicellular Eukaryote

* 12.5 Mbp genome in 16 linear chromosomes (except mitochondriae) containing 6400 genes (2000 more than E. coli).

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Introduction to Bioinformatics9.1 CHATEAU HAJJI FERUZ TEPE

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Introduction to Bioinformatics9.1 CHATEAU HAJJI FERUZ TEPE

* S. cerevisiae can be regarded as a complex factory transforming many raw materials to final materials, involving many ‘conveyor belts’ between the genes

* Such a conveyor belt of coupled expressed genes is called a genetic pathway

* The diauxic shift means that the whole system has to be transformed from the old process to the new process, meaning that entire new pathways are formed, and old pahways are shut-off.

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Introduction to Bioinformatics9.1 CHATEAU HAJJI FERUZ TEPE

* Therefore it is usefull to monitor the genome-wide expression of S. cerevisiae in time, including the diauxic shift.

* Such a conveyor belt of coupled expressed genes is called a genetic pathway

* This monitoring can be done with microarrays, the foremost important tools in bioinformatics.

* Other dynamical processes as the Cell Cycle can also be studied with microarrays.

* This requires the data analysis of the microarrays – here we study the clustering of expression profiles: time series of expression levels.

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Introduction to BioinformaticsLECTURE 9: CLUSTERING GENE EXPRESSION

9.2 Monitoring cellular communication

* Purpose of microarrays: snap-shot of the expression levels in the cell.

* Expressed gene = DNA → mRNA → proteins ….

* In the cell therefore expressed genes cause high numbers of mRNA molecules.

* Idea of microarrays: measure the concentrations of mRNA, and reverse-compute the DNA belonging to this mRNA.

* As RNA can be spliced due to exons, the backward computed DNA is not entirely equal to the real DNA: it is called cDNA: complementary DNA.

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Introduction to Bioinformatics9.2 MONITORING CELLULAR COMMUNICATION

* The cDNA computed from mRNA hints to an expressed gene, the cDNA is stored as an EST: Expressed Sequence Tag.

* EST sequencing can identify genes that are ‘missed’ with ab initio gene-finding methods, such as ORF-finder.

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Introduction to BioinformaticsLECTURE 9: CLUSTERING GENE EXPRESSION

9.3 Microarray technologies

* A microarray is an array of sensitive spots, each containing a stretch of DNA, e.g. based on an EST

* Hybridization (=chemical binding) of the DNA with components in the substrate indicates the presence of the associated mRNA

* The hybridization can be made visible by inserting fluoriscent molecules on the DNA (red, green) and later illuminating them with a suitable laser

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Until recently we lacked tools to observe genome-wide expression

1989 saw the introduction of the microarray technique by Stephen Fodor

But only in 1992 this technique became generally available – but still very costly

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Microarray

Microarray-developper

developped microarray

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Introduction to Bioinformatics9.3 MICROARRAY TECHNOLOGIES

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Introduction to Bioinformatics9.3 MICROARRAY TECHNOLOGIES

Example of an Affymetrix microarray simulation. Example of the simulated single-channel oligonucleotide microarray slide image (crop from top left corner) (a). We have used an Affymetrix .cel file as the ground truth data. Thus the text about the slide type is observable. Real Affymetrix slide image is shown for comparison (b).

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Introduction to BioinformaticsLECTURE 9: CLUSTERING GENE EXPRESSION

9.4 The diauxic shift and yeast gene expression

* In 1997 DeRisi et alum used microarrays to measure the genome-wide expression on S. cerevisiae during the diauxic shift.

* 9 initial hours of growth, 6 hours before the diauxic shift, and 6 hour there after.

* They compared the mRNAs in the array at t time-steps before the diauxic shift, and compared those with the mRNA-levels at time 0.

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Introduction to Bioinformatics9.4 THE DIAUXIC SHIFT AND YEAST GENE EXPRESSION

* This experiment gave a set of 43.000 ratios: seven time-points (t1, t2,…, t7) of 6400 gene expression levels normalized o their start value.

* This is the reference design in microarray literature

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Introduction to Bioinformatics9.4 THE DIAUXIC SHIFT AND YEAST GENE EXPRESSION

* This experiment typically provides a time series that is small relative to the size of the genome ; here m=7 timepoints for n=6400 genes.

* This is due to the cost of an array: ~ 1000 euro/array

* With this kind of experiment we can in principle also reconstruct the gene regulatory networks

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Introduction to Bioinformatics9.4 THE DIAUXIC SHIFT AND YEAST GENE EXPRESSION

9.4.1 Data Description

* First analyse the relative change in activity

* Less than 5% of the genes change more than 1.5-fold, or less then 0.67-fold.

* fold-change: f = new_value/old_value; if f > 1 the fold-chance is f, if f < 1 then the fold-change is – 1/f

* Example: x0 = 1, x1 = 0.3333, fold-change is -3, x0 = 1, x1 = 3, fold-change is +3.

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Introduction to Bioinformatics9.4 THE DIAUXIC SHIFT AND YEAST GENE EXPRESSION

9.4.1 Data Description

* Now select only those genes with an absolute fold-change above a certain threshold:

abs(fold-change) > threshold

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Introduction to Bioinformatics9.4 THE DIAUXIC SHIFT AND YEAST GENE EXPRESSION

9.4.1 Data Clustering

* Next, cluster the genes relative to their expression levels.

* High intra-cluster similarity and low inter-cluster similarity.

* Use a distance/similarity measure and a clustering algorithm.

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Introduction to Bioinformatics9.4 THE DIAUXIC SHIFT AND YEAST GENE EXPRESSION

Data Clustering

1. Define a suitable Distance Measure d(x1,x2), e.g. Pearson’s correlation coefficient, or a normalized distance like the Mahalanobis distance, or a metric like the generalized p-norm.

2. Define a clustering criterion, e.g.:

C = ∑ij in same cluster dij - ∑ij in different cluster dij.

3. Apply a suitable clustering algorithm, e.g. hierarchical, or K-means clustering.

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Introduction to Bioinformatics9.4 THE DIAUXIC SHIFT AND YEAST GENE EXPRESSION

Hierarchical clustering

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Introduction to Bioinformatics9.4 THE DIAUXIC SHIFT AND YEAST GENE EXPRESSION

K-means clustering

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Introduction to Bioinformatics9.4 THE DIAUXIC SHIFT AND YEAST GENE EXPRESSION

Gene function and Clustering

1. Genes with similar expression profiles have similar functions.

2. Define a clustering criterion, e.g.:

C = ∑ij in same cluster dij - ∑ij in different cluster dij.

3. Apply a suitable clustering algorithm, e.g. hierarchical, or K-means clustering.

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Introduction to Bioinformatics9.4 THE DIAUXIC SHIFT AND YEAST GENE EXPRESSION

Gene function and Clustering

1. Single linkage = min i,j ||x[i] – y[j]||.

2. Average linkage = mean i,j ||x[i] – y[j]||.

3. Centroid distance: dAB = ||mA – mB||

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Introduction to Bioinformatics9.4 THE DIAUXIC SHIFT AND YEAST GENE EXPRESSION

9.4.3 Data Visualisation

* In a tree using Hierarchic clustering.

* In a plane using MDS

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Introduction to Bioinformatics9.4 THE DIAUXIC SHIFT AND YEAST GENE EXPRESSION

Gene function and Clustering

2. Multi Dimensional Schaling

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Introduction to Bioinformatics9.4 THE DIAUXIC SHIFT AND YEAST GENE EXPRESSION

Gene function and Clustering

1. Hierarchical clustering: level of cut-off

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Introduction to Bioinformatics9.4 THE DIAUXIC SHIFT AND YEAST GENE EXPRESSION

Pre-processing

* Select only genes with ‘enough’ fold-change

* Delete missing values

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Introduction to Bioinformatics9.4 THE DIAUXIC SHIFT AND YEAST GENE EXPRESSION

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Introduction to BioinformaticsLECTURE 9: CLUSTERING GENE EXPRESSION

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Introduction to BioinformaticsLECTURE 9: CLUSTERING GENE EXPRESSION

9.5 CASE STUDY: Cell-cycle regulated genes

* A set of microarrays over the cell-cycle of yeast.

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Introduction to BioinformaticsLECTURE 9: CLUSTERING GENE EXPRESSION

From being budded off from its parent cell, to reproducing its own offspring, each yeast go through a number of typical step that also involve changes in gene expression, turning whole pathways on and off.

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Introduction to BioinformaticsLECTURE 9: CLUSTERING GENE EXPRESSION

Here we examine the expressions of the entire yeast genome through two rounds of the cell cycle.

The temporal expression of genes are measured by microarray at 24 time points every five hours. In detail we have the expression profile of about 6400 genes.

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Introduction to Bioinformatics9.5 THE CELL CYCLE

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Introduction to BioinformaticsLECTURE 9: CLUSTERING GENE EXPRESSION

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Introduction to BioinformaticsLECTURE 9: CLUSTERING GENE EXPRESSION

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Introduction to BioinformaticsLECTURE 9: CLUSTERING GENE EXPRESSION

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END of LECTURE 9