Introduction to

DNA Microarrays

DNA Microarrays and DNA chips resources

on the web

Microarray analysis is a new technology which allows scientists to detect thousands of genes in a small sample simultaneously and to analyze the expression of those genes.

Microarrays are simply ordered sets of DNA molecules of known sequence. Usually rectangular, they can consist of a few hundred to hundreds of thousands of sets. Each individual feature goes on the array at precisely defined location on the substrate.

INTRODUCTION

• Identification of complex genetic diseases• Drug discovery and toxicology studies• Mutation/polymorphism detection (SNP’s)• Pathogen analysis• Differing expression of genes over time, between

tissues, and disease states• Preventive medicine• Ability to subtype disease and design drugs that treat

disease causes, rather than symptoms• Specific genotype (population) targeted drugs• More targeted drug treatments – AIDS• Genetic testing and privacy

Potential application domains

The technique

Based on already known methods, such as fluorescence and hybridization.

It's goal is to compare gene transcription in two or more different kinds of cells.

There is four main steps in making an array experiment :

1- Array fabrication

2- Sample preparation and hybridization

3- Scanning the array

4- Exploring the results

The challenge

The big revolution here is in the "micro" term. New slides will contain a survey of the human genome on a 2 cm2 chip! The use of this large-scale method tends to create phenomenal amounts of data, which have then to be stored, processed and analyzed.

As the technique is quite new, analyzing the data is still a problem, and nothing is standardized yet. A few databases and on-line repositories are coming out, and the future standard will probably be chosen between these ones.

This is a job for…Bioinformatics !

Course overview

Theory :

Introduction to the technique of microarrays

Analyzing the data : a few methods

Quick survey of tools, databases, datasets available on the web

Practical :

Using on-line tools for microarrays

Searching public databases

THE EXPERIMENT : making the chip

1- Designing the chip : choosing genes of interest for the experiment

- Selection of chip probes that represent the investigated genes.

- Finding sequences, usually in the EST database.

- Problems : sequencing errors, alternative splicing, chimeric sequences, contamination…

THE EXPERIMENT : making the chip

2- Spotting the probes on the substrate

- Substrate : usually glass, but also nylon membranes, plastic, ceramic…

- Probes : cDNA probes (500-5000 nucleotides, dna chips), oligonucleotides (20~80-mer oligos, oligo chips), genomic DNA ( ~50’000 bases)

- Printing methods : microspotting, ink-jetting (for dna chips) or in-situ printing, for example photolithography (for oligos, Affymetrix method)

THE EXPERIMENT : making the chip

Microspotting and ink-jetting

THE EXPERIMENT : making the chip

The microspotting and ink-jetting are done by a robot called “arrayer”

THE EXPERIMENT : making the chip

Oligo-spotting (Affymetrix method)

THE EXPERIMENT : hybridization

Sample preparation

- Extracting DNA (for genomic studies) or mRNA (for gene expressions studies) from the two samples to compare.

- Target labeling. Making cDNAs with both extracts, and labeling them with different fluors to allow direct comparison.

THE EXPERIMENT : hybridization

Samples are eluted on the chip, put in a hybridization chamber, and then washed.

THE EXPERIMENT : generating data

Chip scanning

- Fluorescence measurements are made with scanning laser fluorescence microscope that illuminates each DNA spot and measures fluorescence for each dye separately. It creates one red and one green image.

- The two images are then superimposed to give a virtual result of RNA amounts in both samples

THE EXPERIMENT : generating data

Chip scanning

1- Samples

2- Extracting mRNA

3- Labeling

4- Hybridizing

5- Scanning

6- Visualizing

Examples of scanner outputs

Affymetrix chip Stanford chip

THE EXPERIMENT : generating data

Image analysis

-These fluorescence measures are then used to determine the ratio, and in turn the relative abundance, of the sequence of each specific gene in the two mRNA or DNA samples.

- This analysis is performed by a software such as “scanalyze”, available at : http://rana.lbl.gov/EisenSoftware.htm

or “Spotfinder” from TIGR

- The files created can then be submitted to further analysis

THE EXPERIMENT : making sense of the data

Although the visual image of a microarray panel is alluring, its information content, per se, is still not readable.

How can one visualize, organize and explore the meaning of information consisting of several million measurements of expression of thousands of genes under thousands of conditions?

THE EXPERIMENT : making sense of the data

Data mining depends on the questions which are asked. The most frequent question is to find sets of genes that have correlated expression profiles (belonging to the same biological process and/or co-regulated), or to devide conditions to groups with similar gene expression profiles ( for example divide drugs according to their effect on gene expression. The method used to answer these questions is called CLUSTERING.

Clustering data

•Input: N data points, Xi, i=1,2,…,N (the color ratios measured with Scanalyze, for example) in a D dimensional space. N and D will be either genes and conditions for gene clustering, or conditions and genes for conditions clustering.•Goal: Find “natural”groups or clusters. •Note: according to the method, the number of clusters will be fixed from the beginning (K-means) or determined after the analysis (hierarchical clustering)

Clustering data

Clustering methods :

1- Agglomerative Hierarchical

2- Centroids: K-means or SOM

3- Super-Paramagnetic Clustering

Before clustering, a few steps to “clean the data are necessary ( normalization, filtering)

For a good introduction on different clustering techniques, read the article from Gavin Sherlock “Analysis of large-scale gene expression data” in Current Opinion in Immunology 2000, 12:201-205 (html,pdf)

52 41 3

Agglomerative Hierarchical Clustering

3

1

4 2

5

Distance between joined clusters

DendrogramThe dendrogram induces a linear ordering of the data points

The dendrogram induces a linear ordering of the data points

1-The similarity measure between two genes (or experiments)

Agglomerative Hierarchical Clustering

Before doing a hierarchical clustering, one has to define two things :

2- The distance measure between the new cluster and the others

Single Linkage: distance between closest pair.

Complete Linkage: distance between farthest pair.

Average Linkage: distance between cluster centers

Centered correlation

Uncentered correlation

Absolute correlation

Euclidean

Centroid methods - K-means

Iteration = 0

•Start with random position of K centroids.•Iteratre until centroids are stable

•Assign points to centroids•Move centroids to centerof assign points

•Start with random position of K centroids.•Iteratre until centroids are stable

•Assign points to centroids•Move centroids to centerof assign points

Iteration = 1

Centroid Methods - K-means

Iteration = 3

•Start with random position of K centroids.•Iteratre until centroids are stable

•Assign points to centroids•Move centroids to centerof assign points

Centroid Methods - K-means

Self-organizing Maps-Choose a number of partitions

- Assign a random reference vector to each partition.

- Pick a gene randomly and assign it to its most similar reference vector.

- Adjust that reference vector is so that it is more similar to the chosen gene.

-Adjust the other reference vectors.

- Repeat thousands of times until partitions are stable.

A self-organizing map.

Super-Paramagnetic Clustering (SPC) M.Blatt, S.Weisman and E.Domany (1996) Neural Computation

• The idea behind SPC is based on the physical properties dilute magnets.

• Calculating correlation between magnet orientations at different temperatures (T).

T=Low

• The idea behind SPC is based on the physical properties dilute magnets.

• Calculating correlation between magnet orientations at different temperatures (T).

T=High

Super-Paramagnetic Clustering (SPC) M.Blatt, S.Weisman and E.Domany (1996) Neural Computation

Super-Paramagnetic Clustering (SPC) M.Blatt, S.Weisman and E.Domany (1996) Neural Computation

• The algorithm simulates the magnets behavior at a range of temperatures and calculates their correlation

• The temperature (T) controls the resolution

T=Intermediate

Clustering data

•M. Eisen’s programs for clustering and display of results (Cluster, TreeView)

–Predefined set of normalizations and filtering–Agglomerative, K-means, 1D SOM

•Matlab–Agglomerative, public m-files.

•Dedicated software packages (SPC)•Web sites: e.g. http://ep.ebi.ac.uk/EP/EPCLUST/•Statistical programs (SPSS, SAS, S-plus)• And much others …

Available clustering tools

Clustering data

The final data representation is then a big matrix with rows being the genes and columns representing the different experiments. To keep the image coherent with the scan output, the ratio numbers calculated by Scanalyze are transformed back in color spots on a green-red based scale.

Clustering data

Another way to represent these data is a graph showing the gene’s expression variation during the different experiments

Expression variation of nine genes along the 19 experiments from Lyer et al. (Fibroblast response to serum stimulation)

Expression Profiler Online clustering and analysis tools

GenEx Database, repository and analysis tools

MAExplorer MicroArray Explorer for data mining Gene Expression, free download

ArrayDB Downloadable tools, short online demo

MAXD Downloadable data warehouse and visualisation for expression data

Jexpress Java tools for gene expression data analysis, free download

Eisen Lab Michael Eisen's suite for image quantitation and data analysis (Scanalyze, Cluster, TreeView). Downloadable.

Web resources : data analysis tools

SMD The Stanford Microarray Database

Chip DB Searchable database on gene expression

ExpressDB Public queries of E. coli and yeast data

GEO Gene expression data repository and online resource

RAD RNA Abundance Database

Expression Connection

Saccharomyces Genome Database expression data retrieval

EpoDB Expression information retrieval for one gene at a time

yMGV Public queries of yeast data

Web resources : public databases

AMAD Downloadable web driven database system

ArrayExpress Public data deposition and public queries

maxdSQL Downloadable data warehouse and visualisation environment

GXD Mouse expression data storage and integration

GeNet Distribution and visualization of gene expression data from any organism

Web resources : public databases

Drosophila microarray project

Drosophila Metamorphosis Time Course Database

Samson Lab Yeast Transcriptional Profiling Experiments

SageMap NCBI SAGE data and analysis tools

NCI60 cancer project Supplement to Ross et al. (Nat Genet., 2000).

Serum-responseSupplement to Lyer et al.(1999) Science 283:83-87

Breast cancerSupplement to Perou et al. Nature 406:747-752(2000)

Cancer Molecular Pharmacology

Integration of large databases on gene expression and molecular pharmacology.

Web resources : public databases

Leung’s Link’s page & softwares’ info

Davison’s DNA Microarray Methodology - Flash Animation

gene-chips Overview of the technique, papers…

Chips & microassays

General information

SMD guide Stanford's links page, very complete

Introduction Online introduction to microarrays

Brown Lab Guide Microarrays protocols and arrayer construction.

Web resources : general information