Chapter 4.3. of Molecular Plant Physiology Am Mühlenberg 1, D Golm, GERMANY;

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1 Chapter 4.3 LOTUS JAPONICUS EXPRESSION DATABASE Sebastian Kloska 1, Peter Krüger 2, and Joachim Selbig 2,3* 1 Scienion AG, Volmerstrasse 7a, D Berlin, GERMANY; 2 Max Planck Institute of Molecular Plant Physiology Am Mühlenberg 1, D Golm, GERMANY; 3 University of Potsdam, Institute of Biochemistry and Biology; *Corresponding author selbig@mpimp-golm.mpg.de Phone: Fax: Keywords: Lotus japonicus; Expression Profiling; Statistics; Database; Clustering Haruspex is a data management and analysis system developed at the Max Planck Institute of Plant Molecular Physiology that enables researchers to screen large sets of profiling data. Haruspex provides a common platform for the web-based storage, retrieval, and statistical analysis of expression profiles, while links to external databases provide fast access to comprehensive and up to date information. INTRODUCTION Since the early days of high throughput biology (DeRisi et al., 1997) it has been apparent that data generated with these techniques can only lead to conclusions by employing computer aided methods (Eisen et al., 1998). This new field of bioinformatics deals with the development of exploratory tools, statistical methods (Slonim 2002), and data storage and retrieval systems. When the Max-Planck Institute of Plant Molecular Physiology established a Microarray facility (Thimm et al., 2001), our group initiated the development of a workflow system for the management and analysis of gene expression profiles: Haruspex the Arabidopsis Expression Database. Haruspex was developed in cooperation with the Computer Science Department of the University of Potsdam as a web-based data storage and processing system. This program allows researchers to submit raw expression profiling data to be normalised, filtered, and annotated. The pre-processed values can be grouped together to form project-specific datasets that can be further analysed within Haruspex using statistical methods. 149

2 DATABASE Haruspex has been implemented as a set of server side programs written in C++ and Perl, a mod_perl-based web interface (Stein and MacEachern 2000) and a set of Java applets (Joy et al. 2000) serving as GUIs for more complex user interaction. This front end communicates with a relational database system (Adabas D). The system runs under Linux with an Apache web server (Ben and Laurie 1999). After scanning and quantification of the raw data, the user can submit the results by logging into the system and transferring a data file to the server. Here one has to annotate the hybridization and choose a predefined scheme that describes the spotting pattern of the filter and represents the link between the spot coordinates and all available information about the underlying genes (Figure 1A). After submission, the newly generated dataset can be listed and filtered based on normalized values and annotation. In a second step, data from different hybridisations can be bundled to groups of experiments. These can be used to generate more stable values averaged over a set of replications or may serve as a starting point for statistical tests in order to search for genes with significantly altered expression level (Figure 1B). We have also implemented various clustering methods on the server side. Haruspex provides links to publicly available resources on the net. If the set of target genes represented on the array are annotated with GenBank Accessions the user may directly navigate from the expression profiling result tables to the desired information. Anonymous sequences (e.g. ESTs) for which only raw sequences are available can be mapped via BLAST (Altschul et al. 1990) to the most similar sequence within the target organism. Direct linkage is provided for the GenBank database at the NCBI (Benson et al. 1998), and resources at MIPS (Schoof 2003) and (Huala et al. 2001). 150

3 Figure 1. User Dialogs of the Haruspex Database front end. A. Previous page screen: Submission of new dataset representing a microarray experiment. The user is prompted for a name of the new hybridisation, a spotting scheme, and information about the experiment. After selecting a file on the local file system the data is transferred to the Haruspex server, normalised, and stored in the relational database management system (RDBMS). B. This page screen: Applying statistical tests to sets of hybridizations. After bundling experimental sets, one can apply statistical tests to the data by choosing two sets and specifying parameters for the tests. NORMALISING AND FILTRATION The first step towards comparable data sets is a data filtration and normalisation procedure. For hybridisations with radioactive probes on nylon membranes, which constitute the main source for expression profiling data in the institute, we have implemented this procedure entirely within the database system. Each filter spotted with PCR products is first hybridised with vector specific oligomers. This reference hybridisation provides us with a quantification of the amount of target DNA spotted on the filter, which has a strong influence on the final signal observed in the experiment. After submitting the reference hybridisation to the repository, the user may transfer hybridisation data from complex hybridisations performed with labelled RNA-derived probes on the same filter. When both datasets are available, Haruspex performs a three-step filtration and normalisation process. First, the system searches for undetectable spots in both the complex and reference hybridisations. This is done by comparing the raw data value obtained for a given spot with the value of an empty spot placed within the centre of each sub grid. If the spot signal is not at least twice as high as this local background 151

4 signal we call the given spot undetectable. Undetectable spots on the reference hybridisation tell us that there is not enough probe deposited on the filter and the corresponding signal from the complex hybridisation will be flagged as invalid, while undetectable signals from the complex hybridisations are treated as zero. In the second step both the reference and the complex hybridisations are normalized against the average signal of all valid spots after subtraction of the local background signal. This internal normalisation compensates for global differences in labelling efficiency and hybridisation performance. The final normalisation step correlates the internally normalised value of the complex hybridisation to the normalised signal obtained from the reference hybridisation. The effect of the normalisation scheme is represented in Figure 2. A B Figure 2. Visualisation of the normalisation and filtration procedure. Logarithmic scatter plots of data from experimental replicates. (A) Represents raw data from a complex hybridisation. (B) Shows fully normalised values from the same hybridisation. Red lines denote the limits for 2-fold differences. STATISTICAL TESTS AND DATA EXPLORATION Data generated from a set of experimental replications can be bundled into a group. When combining such replicas the user might choose to discard certain values (e.g. such values flagged as being undetectable or zero). Two such groups may afterwards be compared employing a t-test on a per-gene basis. The newly generated dataset can be browsed and further filtered for genes that show a low P- value and/or high expression ratio under the chosen conditions. Another option provided by Haruspex is the generation of a hierarchical cluster dendrogram (Eisen et al. 1998). Here a hybridisation group should represent a series of experiments performed under different conditions (e.g. time series or dosage response). These can be pre-filtered by various limits like expression intensity or variance between individual hybridisations in order to restrict the analyses to those genes that show the highest dynamic. Afterwards, Haruspex allows the user to 152

5 perform clustering on the data set specifying such parameters as distance measure or linkage type. The resulting data is reinserted into the repository and may be visually explored with a dendrogram navigation system written in Java. CONCLUSION Haruspex allows scientists to efficiently store, explore, and analyse their expression profiling data. It was initially designed to work with filter-based large volume experiments performed with the A. thaliana EST collection of the Michigan State University, but has since been adopted by other groups within the institute working with plant species like potato and L. japonicus (Colebatch et al. 2002). Now that a full genome chip for A. thaliana is available from Affymetrix (Lipshutz et al. 1999), we are working towards inclusion of data generated by this platform. On the other hand the demand for custom designed filter based assays has not diminished, since there are still a large collection of organisms which although interesting for scientists do not generate demands large enough to make them interesting for commercial array providers. Comparability of the data across different platforms and standardised data formats are still one of the major issues within the microarray domain. With a large set of public domain and commercial database systems (Gardiner-Garden and Littlejohn 2001) now available and the inherent complexity of high throughput experiments we still have a long way to go to make expression profiling data as easily accessible and comparable as sequence data at the public sequencing repositories. REFERENCES Altschul, SF, Gish, W, Miller, W, Myers, EW, and Lipman, DJ. (1990) Basic local alignment search tool. Journal of Molecular Biology 215, Ben L and Laurie P. (1999) Apache: the Definitive. O'Reilly Pub. Inc. ( Benson DA, Boguski MS, Lipman DJ, Ostell J, Ouellette BF. (1998) GenBank. Nucleic Acids Res. 26 ( Colebatch G, Kloska S, Trevaskis B, Freund S, Altmann T, Udvardi MK. (2002) Novel aspects of symbiotic nitrogen fixation uncovered by transcript profiling with cdna arrays. Molecular Plant-Microbe Interactions 15, DeRisi JL, Iyer VR, and Brown PO. (1997) Exploring the Metabolic and Genetic Control of Gene Expression on a Genomic Scale. Science 278, Eisen, MB, Spellman, PT, Brown, PO, and Botstein, D. (2001) Cluster analysis and display of genome-wide expression patterns. Proceedings of the National Academy of Sciences USA 95, Gardiner-Garden, M and Littlejohn, TG. (2001) A comparison of microarray databases. Briefings in Bioinformatics 2, Huala E, Dickerman AW, Garcia-Hernandez M, Weems D, Reiser L, LaFond F, Hanley D, Kiphart D, Zhuang M, Huang W, Mueller LA, Bhattacharyya D, Bhaya D, Sobral BW, Beavis W, Meinke DW, Town CD, Somerville C, and Rhee SY. (2001) The Arabidopsis Information Resource (TAIR): a comprehensive database and web-based information retrieval, analysis, and visualization system for a model plant. Nucleic Acids Research 29, ( 153

6 Joy B, Steele G, Gosling J, and Bracha G. (2000) Java TM Language Specification (2nd Edition). Addison-Wesley Pub. Inc. ( Lipshutz RJ, Fodor SP, Gingeras TR, and Lockhart DJ. (1999) High density synthetic oligonucleotide arrays. Nature Genetics: 21: Schoof, H. (2003) Towards interoperability in genome databases: the MAtDB (MIPS Arabidopis thaliana database) experience. Comparative & Functional Genomics: 4, ( Slonim DK. (2002) From patterns to pathways: gene expression data analysis comes of age. Nature Genetics 32 Suppl, Stein L and MacEachern D. (2000) Writing Apache Modules with Perl and C (2nd Edition). O' Reilly Pub. Inc. ( Thimm 0, Essigmann B, Kloska S, Altmann T, and Buckhout TJ. (2001) Response of Arabidopsis to Iron Deficiency Stress as Revealed by Microarray Analysis. Plant Physiology 128,