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Introduction to DNA Microarrays DNA Microarrays and DNA chips resources on the web Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2001.11 INTRODUCTION Microarray analysis is a new technology that allows scientists to simultaneously detect thousands of genes in a small sample and to analyze the expression of those genes. Microarrays are simply ordered sets of DNA molecules of known sequence. Usually rectangular shaped, they can consist of a few hundred to hundreds of thousands of sets. Each individual sequence goes on the array at precisely defined location. Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2001.11 Potential application domains 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 Specific genotype (population) targeted drugs More targeted drug treatments – AIDS Genetic testing and privacy Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2001.11 The technique Based on already known methods, such as fluorescence and hybridization. High throughput miniaturized method. It's main purpose is to compare gene transcription levels in two or more different kinds of cells. - Microarrays - DNA chips - SAGE - Beads (liquid chip) … Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2001.11 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, that have then to be analyzed, processed and stored. 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 among them. This is a job for… Bioinformatics ! Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2001.11 General overview Making the chip Experiment design, sequence selection, collection maintenance, PCR, spotting, printing, synthesis Probe hybridization Scanning and image treatment Fluorescence correction, find spots, background Analysing the data Probe purification, labelling, hybridization, washing Filtering, normalisation Clustering (hierarchical, centroid, SPC) Representation, storage Graphics, databases, web public resources Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2001.11 } wet lab THE EXPERIMENT : making the chip 1- Designing the chip : choosing genes of interest for the experiment and/or select the samples - Selection of sequences that represent the investigated genes. - Finding sequences, usually in the EST database. - Problems : sequencing errors, alternative splicing, chimeric sequences, contamination… Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2001.11 THE EXPERIMENT : making the chip 2- Spotting the sequences on the substrate - Substrate : usually glass, but also nylon membranes, plastic, ceramic… - Sequences : cDNA (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 insitu printing, photolithography (for oligos, Affymetrix method) Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2001.11 THE EXPERIMENT : making the chip Microspotting and ink-jetting Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2001.11 THE EXPERIMENT : making the chip The microspotting is done by a robot called “arrayer” Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2001.11 THE EXPERIMENT : making the chip Oligo-spotting (Affymetrix method) Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2001.11 THE EXPERIMENT : hybridization Sample preparation - Extracting DNA (for genomic studies) or mRNA (for gene expressions studies) from the two or more samples to compare. - Making cDNAs with extracts, and labeling them with different fluorochromes to allow direct comparison. (Cy-3, Cy-5, DIG…) - Some techniques use radiolabeling Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2001.11 THE EXPERIMENT : hybridization Probes are overlaid on the chip, put in a hybridization chamber, and then washed. Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2001.11 THE EXPERIMENT : generating data Chip scanning - Fluorescence measurements are made with scanning laser fluorescence microscope that scans the slide, illuminating each DNA spot and measuring 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 ratio in both samples Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2001.11 THE EXPERIMENT : generating data Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2001.11 1- Samples 2- Extracting mRNA 3- Labeling 4- Hybridizing 5- Scanning 6- Visualizing Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2001.11 Examples of images Affymetrix chip Stanford array Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2001.11 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 Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2001.11 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 human readable. How to visualize, organize and explore the meaning of information consisting of several million measurements of expression of thousands of genes under thousands of conditions?… Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2001.11 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 divide 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. Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2001.11 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 condition clustering. •Goal: Find “natural” groups or clusters. •Note: according to the method, the number of clusters will be fixed from the beginning (centroid clustering) or determined after the analysis (hierarchical clustering) Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2001.11 Clustering data Before clustering, a few steps to “clean the data” are necessary (normalization, filtering) Clustering methods (examples) : 1- Agglomerative Hierarchical 2- Centroids: K-means or SOM 3- Super-Paramagnetic Clustering 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, 12:201205 (pdf) http://www.isrec.isb-sib.ch/~vpraz/chips/Sherlock.pdf Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2001.11 Agglomerative Hierarchical Clustering Distance between joined clusters 4 2 5 3 1 1 3 2 4 Dendrogram Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique 5 The dendrogram induces a linear ordering of the data points LF-2001.11 Agglomerative Hierarchical Clustering Before doing a such clustering, one has to define two things: 1- The similarity measure between two genes (or experiments) Centered correlation Uncentered correlation Absolute correlation Euclidean 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 Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2001.11 Centroid methods - K-means •Start with random position of K centroids. •Assign points to centroids •Move centroids to center of assigned points •Iterate until centroids are stable Iteration = 0 Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2001.11 Centroid methods - K-means •Start with random position of K centroids. •Assign points to centroids •Move centroids to center of assigned points •Iterate until centroids are stable Iteration = 1 Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2001.11 Centroid methods - K-means •Start with random position of K centroids. •Assign points to centroids •Move centroids to center of assigned points •Iterate until centroids are stable Iteration = 3 Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2001.11 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. Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique A self-organizing map. LF-2001.11 Super-Paramagnetic Clustering (SPC) M.Blatt, S.Weisman and E.Domany (1996) Neural Computation The idea behind SPC is based on the physical properties of dilute magnets. Calculating correlation between magnet orientations at different temperatures (T). T=Low Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2001.11 Super-Paramagnetic Clustering (SPC) M.Blatt, S.Weisman and E.Domany (1996) Neural Computation The idea behind SPC is based on the physical properties of dilute magnets. Calculating correlation between magnet orientations at different temperatures (T). T=High Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2001.11 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 Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2001.11 Clustering data Available clustering tools •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 more… Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2001.11 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. Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2001.11 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) Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2001.11 Web resources : data analysis tools Expression Profiler Online clustering and analysis tools (EBI) GenEx Database, repository and analysis tools (NCGR) 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. Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2001.11 Web resources : public databases SMD The Stanford Microarray Database Chip DB Searchable database on gene expression (MIT) ExpressDB Public queries of E. coli and yeast data GEO Gene expression data repository and online resource (NCBI) 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 Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2001.11 Web resources : public databases AMAD Downloadable web driven database system ArrayExpress Public data deposition and public queries (EBI) maxdSQL Downloadable data warehouse and visualization environment GXD Mouse expression data storage and integration GeNet Distribution and visualization of gene expression data from any organism Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2001.11 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-response Supplement to Lyer et al.(1999) Science 283:83-87 Breast cancer Supplement to Perou et al. Nature 406:747-752(2000) Cancer Molecular Pharmacology Integration of large databases on gene expression and molecular pharmacology. Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2001.11 Web resources : general information Leung’s Links page & software 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 (EBI) Brown Lab Guide Microarrays protocols and arrayer construction. Swiss Institute of Bioinformatics Institut Suisse de Bioinformatique LF-2001.11