Chip Tracker: a Microarray Laboratory Information Management System

Transcription

1 Chip Tracker: a Microarray Laboratory Information Management System Patricio Yankilevich M.Sc Project Dissertation for the Degree of Master Science in Informatics with specialism in Bioinformatics The University of Edinburgh September 2003

2 Chip Tracker: a Microarray Laboratory Information Management System Author: Patricio Yankilevich School of Informatics, University of Edinburgh (P.Yankilevich@sms.ed.ac.uk) Academic supervisor: J. Douglas Armstrong PhD School of Informatics, University of Edinburgh (jda@inf.ed.ac.uk) Industrial supervisors: Dr. Vitali Proutski - Dr. Ann Brown Organon Research Unit, Organon Laboratories ltd. (v.proutski@organon.co.uk, ann.brown@organon.co.uk) -2-

3 Declaration This project was carried out between May and September The work discussed in this dissertation, unless otherwise stated, is my own; and the manuscript has been composed myself. Patricio Yankilevich School of Informatics The University of Edinburgh September

4 Abstract In this project a Microarray Laboratory Information Management System (Microarray LIMS) was designed and developed to become part of the integrated bioinformatics infrastructure for the Organon GeneChip microarray facility. The software complies with microarray communication standards and uses an industry standard relational database management system combined with a platform-independent web browser interface for data entry and retrieval. Therefore it is portable and flexible and can also be used as a stand alone tool to manage a microarray laboratory workflow. The resulting system guides the users through the microarray laboratory workflow steps facilitating the management and tracking of biological samples and microarray chips via a user friendly interface. In addition to this, the application automates the data collection process, tracks the chips that have been ordered and prompts pre populated purchase orders of chips, controls the assignation of samples to chips for hybridisation. It randomises chip usage and also logs hybridisation results and chip faults as part of the QC procedures. Finally, a highly flexible reporting tool enables the users and laboratory managers to search the database on the usage history of the platform. This project captures and systemises the real microarray laboratory workflow and thus improves performance and minimises human errors added to the microarray experiment. -4-

5 Acknowledgements I would like to thanks my both families for all the love and support they have given me. Thanks especially to wife Juliana, for providing me with a stable home environment and to my parents Oscar and Ana for their financial support. I would like to take this opportunity to thanks Douglas Armstrong my academic supervisor for letting me take this challenging project at an industrial collaborator. I would like to thanks my industrial supervisors Vitali Protski for his advice and Ann Brown for her assistance and for providing me with a friendly work environment. Finally, I would like to thanks Mark, Julie and David from the Chip team, Bridget from System development, and Alistair and Donald from the Bioinformatics team for helping me with the final corrections. -5-

6 Table of contents Abstract...4 Acknowledgements...5 Table of contents...6 Chapter 1: Introduction...8 Organon Microarray Facility...8 Microarray LIMS...9 Objectives and Solution...10 Chapter 2: The Microarray...11 The New Paradigm of Drug Discovery...11 Introduction to Functional Genomics...11 Microarray Technology...12 Microarray Experiment...16 Chapter 3: Laboratory Information Management Systems and Microarray LIMS...20 Laboratory Information Management Systems (LIMS)...20 Microarray LIMS...21 Chapter 4: Chip Tracker and the Organon Microarray Experiment Process...24 The Organon Microarray Experiment Process...24 Microarray Laboratory Workflow and Chip Tracker Scope...28 Chapter 5: Chip Tracker Design and Architecture...31 Presentation Tier (Front End)...33 Middle Tier...36 Database Management Tier (Back end)...38 Integration with existing Systems...40 ExpAnD...40 Rosetta Resource Tracker...41 Rosetta Resolver...41 Chip Tracker Deployment...42 Chapter 6: Chip Tracker XML Parser and the Microarray Experiment Standards...44 MIAME, MAGE and other Standards...44 Chip Tracker XML Parser and the Gene Expression Markup Language (GEML)...45 Chapter 7: Understanding Chip Tracker Features...49 Logging On and Off...50 Chips in Stock

7 Samples in Stock...54 Prompt of Purchase Orders...57 Chips Arrival...58 Assignation of Chips to Samples...59 Log of Hybridised Chips...63 Statistics and Custom Reports...64 Chapter 8: System Validation of Chip Tracker...66 System Validation...66 Control Life Cycle of the project...67 User Acceptance Test...67 Chapter 9: Conclusion and Future Work...69 Bibliography...71 Appendix A: GEML.xml example file...74 Appendix B: SQL commands...75 Appendix C: User Acceptance Test

8 Chapter 1 Introduction The past decade has seen dramatic progress in the development of high throughput life science technologies such as microarrays, which have become the technology of choice for gene expression analysis. Microarray technology has enabled researchers in the field of functional genomics to conduct new types of experiments that generate immense amounts of data. The data created from these microarray experiments necessitates the creation of tools that can manage both the biological and experimental information used in a microarray experiment workflow more effectively and efficiently. While many scientists are focusing on the analysis of the microarray data other issues such as data management, quality, and standards remain to be addressed. This project presents the Chip Tracker, a Microarray Laboratory Information Management System (Microarray LIMS), designed to manage and track the submissions of biological samples and microarray chips used in the microarray laboratory. The project has been carried out with the aid of an industrial collaborator Organon Laboratories Ltd., at the Organon Research facility in Newhouse, Scotland. Organon have recently installed their first microarray facility, an Affymetrix DNA Microarray platform. Organon Microarray Facility The Organon Microarray facility carries out microarray experiments on thousands of biological samples sent to the facility each year. Systems have been developed to capture the experimental annotation of RNA samples submitted to the microarray laboratory (Expand), to deal with the day to day management and processing of the RNA samples (Resource Tracker) and to migrate data from both Expand and the Affymetrix platform to the Resolver data analysis software. These components -8-

9 interact to produce a fully integrated data management infrastructure for the microarray platform. None of them however deal with the actual Chips used by the facility. The users of the microarray facility request to have their RNA samples hybridised to one or more of the thirteen different types of Affymetrix Chips. The experimental results obtained from the hybridisation are very valuable but also expensive and time consuming. It is therefore important to ensure correct assignment of samples to chips. With each Chip costing in excess of 300 and up to 2,000 Chips being used each year it is apparent that some type of tool is required to track the Chip usage by the facility, from purchase to final quality control analysis. Before the implementation of this project the chip information was stored in Excel spreadsheet and the purchase of chips was based on direct communication with the microarray platform customers. These methods soon however became unwieldy due to the large number and variety of Chips being processed by the facility. By taking all of the above factors into account the decision was taken to add Chip Tracker microarray LIMS component to the existing infrastructure of the microarray facility. Microarray LIMS Laboratory Information Management Systems (LIMS) are used by many types of laboratories for capturing data during the experimental process. Such LIMS can be used in research and development, in-process testing, quality control and assurance. There is a small number of integrated systems that deal with the management of the microarray experiment process as BioArray Software Environment (BASE), GeneTraffic and Affy LIMS. These systems are customisable bioinformatics solutions, but none of them address completely the necessities of a microarray laboratory workflow such as tracking and administration of the materials used in the microarray lab. Capturing this data is essential for both good house keeping and the analysis of the data produced by the experiments. -9-

10 In the absence of a commercially available alternative it was decided to custom build a Chip Tracker database and data entry tool to be integrated with the existing bioinformatics infrastructure of the Organon microarray platform. Objectives and Solution The main objective of this project is the development a system to enable the microarray laboratory workers to manage and track the submissions of biological samples and microarray chips that are been used through the microarray laboratory workflow. As a solution we present the Chip Tracker, a microarray LIMS that automates some of the tasks carried out in the laboratory worker and guides him/her along the steps of the laboratory workflow via a user friendly web interface. The Chip Tracker application also tracks the chips that have been ordered and prompts pre populated purchase orders of the necessary chips depending on the samples arriving in the laboratory. Other laboratory procedures within the microarray experiment workflow are also managed from the Chip Tracker application. Such procedures include control of the assignation of samples to chips for hybridisation, randomisation of chip usage, log of chip faults (part of the QC procedures) and creation of statistical reports on platform usage. The system developed in this project not only fully integrates with the existing Organon microarray platform, but also can be used as a stand-alone microarray LIMS. Finally, this project addresses the needs raised by the scientists working in the microarray lab and captures the real microarray laboratory workflow. -10-

11 Chapter 2 The Microarray The New Paradigm of Drug Discovery Biological and biomedical research is in the midst of a significant transition driven by two primary factors: the massive amount of DNA sequence information and the development of new genomics technologies to exploit it. Consequently, we find ourselves at a time when new types of experiments are possible, and observations, analysis and discoveries are being made on an unprecedented scale [McConnell et al., 2002]. Genomics technologies and in particular DNA microarrays are rapidly increasing our understanding of disease, drug targets, and, in the future, how drugs may be used in the clinics. Such an understanding would potentially improve the traditional drug discovery pipeline, enabling better decisions to be made earlier in the therapeutic discovery and development process. Better decisions should ultimately result in better drugs and therapies and allow safer drugs to reach the market sooner. Microarray applications in drug discovery are expanding and included basic research and target discovery, biomarker determination, pharmacology, toxicogenomics, target selectivity, development of prognostic tests and disease-subclass determination [Butte, 2002]. Introduction to Functional Genomics As mentioned above, the constant advances in molecular biological, analytical and computational technologies are enabling us to systematically investigate the complex molecular processes underlying biological systems. Over the past few years, more than 60 organisms have had their genomes completely sequenced, with another 170 or so are in progress (see The sequence of human genome has been -11-

12 deciphered, by both public and private efforts, and the complete sequence of mouse and other animal and plant genomes are nearing completion. Unfortunately, the DNA sequence does not tell us what the genes do, how cells work, how cells form organisms, what goes wrong in disease, how we age or how to develop a drug or how a phenotype is determined. Thus, functional genomics has become an increasingly important scientific discipline [McConnell et al., 2002]. This rapid accumulation of genome sequence data represents the beginning of a fundamentally new kind of biological research ushering in the so called post-genome era. Functional genomics is the study of gene function through the parallel expression measurements of genomes, most commonly using the technologies of microarrays and serial analysis of gene expression (SAGE). The successful use of these large-scale functional genomics technologies depends on robust and efficient systems for tracking and managing material and information flow. Microarray Technology In this new setting for biological research, DNA array technologies (microarrays) that allow for the simultaneous recording of thousands of gene expression levels in a single experiment have acquired a special role. This technology has opened new ways of looking at organisms in a genome-wide manner. It is now possible to study complete genome patterns of gene expression in prokaryotes or in simple eukaryotes like yeast or C. elegans while in higher organisms, like humans, tens of thousands of genes related to a given living system can be monitored [Dopazo, 2002]. Microarrays work by hybridisation (non-covalent chemical bonding) of fluorescently labelled RNA or DNA in solution to DNA molecules (probes) that are attached to specific locations on the chip surface. The hybridisation reactions take place in parallel across the entire array at the same time. Thus, the hybridisation of a sample to an array is, in effect, a highly parallel search by each molecule for a matching partner on an affinity matrix. The eventual binding of labelled molecules to the surfacebound probe is determined by the rules of molecular recognition. The process is straightforward, highly parallel (all sequences are counted simultaneously), and, if done correctly, quantitative [McConnell et al., 2002]. -12-

13 There are two dominant types of microarrays that have been extensively used for most global gene expression measurements or experiments. The first, which is the one used in the Organon microarray laboratory, are high-density arrays of oligonucleotides (short strands of nucleic acids). The oligonucleotide strands are directly synthesised on glass wafer surface using a process of light-directed combinational synthesis known as photolithography [Lockhart et al., 1996]. A single oligonucleotide array can contain more than 500,000 probes, typically of 25 bases long, in an area smaller than half-inch square. The human U133A array, for example contains over 260,000 different probes that together measure the expression of 22,283 different transcripts (or potential genes) at once. The process of hybridisation and scanning the chips requires highly expensive equipment, increasing the need to maximise usage and minimise failures or delays in the utilisation of the facility. This type of microarray is known as one-channel arrays and is commercially available from Affymetrix under the name of GeneChip. The other main array type, cdna array (also called spotted DNA array), consists of solid support (usually nylon or glass) where cdna or oligonucleotides are arrayed in a fixed pattern. Fluorescent DNA derived from mrna coming from the control and test samples are competitively hybridised to the complementary DNA probes on the array. The radioactive or fluorescence emissions of specifically bound probes are detected using an appropriate scanner. These intensity values are proportional to the amounts of specific RNA, originally present in the cell [Schena et al., 1995]. Two different samples, the control and the treatment, are hybridised to a single cdna array. This is also called a two-channel array. Before hybridisation, samples under study are often amplified and then labelled with fluorescent dyes. The samples are then hybridised to the microarray, and they bind to complementary probes affixed to the microarray surface. The arrays are then scanned, producing a fluorescent image where the fluorescent intensity at any particular probe location indicates the relative concentration of complimentary RNA sequence present in the sample. This enables a quantitative estimate of each gene expression to be calculated. Figures 1 and 2 (provided by Affymetrix) shown how the GeneChip oligonucleotide array is built and how it works. -13-

14 A. B. Figure 1. Oligonucleotide array technology. A) Cartoon depicting a single feature on an Affymetrix GeneChip microarray. B) Hybridisation of tagged probes. C) Scanning of tagged and un-tagged DNA [figures provided by Affymetrix, 1]. C. -14-

15 Figure 2. Overview of gene expression measurements with an Affymetrix platform. The process begins with mrna samples from cells which are labelled with a fluorescent dye and hybridise to the microarray chip. Messenger RNA expression levels are determined using the quantitative fluorescent image. The process starts with the original RNA (orna) copy DNA (cdna) labelled copy RNA (lcrna) fragmented labelled copy RNA (flcrna) Hybridisation Wash and Stain Scan [adapted from figures provided by Affymetrix, 2]. Current evidence implies that oligonucleotide-based arrays are more reliable for global screening, as thus give a more accurate and comprehensive representation of gene expression profile compared to long cdna array. While direct synthesis of oligonucleotides by the photolithographic process offers the advantage of abolishing the need to hydrolyse the oligonucleotide from its synthetic support and re-attach it to the microarray, this approach does not allow an independent confirmation of the fidelity of synthesis. Because of this and because this approach does not allow purification of oligonucleotides prior to attachment to the microarray, oligonucleotide chip manufacturing can lead to internal errors. Batch-to-batch variance may also contribute to data bias, as separate samples are hybridised to separate chips when using oligonucleotide arrays [Li et. al., 2001]. In order to tackle this problem the application developed in this project is endowed with a random assignation of chips to samples process that will be explained later. -15-

16 Organon acquired an Affymetrix GeneChip system to perform its microarray experiments in This system is made of the following four components: Probe Arrays or Chips: GeneChip probe arrays are available in human, rat, mouse and other model organisms. A full range of custom formats is also available. Hybridisation Oven: The GeneChip 640 Hybridisation Oven can process from 1 to 64 arrays per cycle. The oven delivers precise temperature control for consistent performance across all probe array applications. Fluids Station: The FS400 Fluidics Station automates staining and washing of up to four arrays at once. Scanner: After processing in the Fluidics Station, probe arrays should be stored as recommended and transported to a centrally located scanner. Figure 3. Affymetrix GeneChip Platform [adapted from a figure provided by Affymetrix, 3]. The Affymetrix GeneChip platform had become the industry-standard microarray experiment solution for genomics research. Initially applied for target identification, GeneChip RNA expression analysis is being used by innovative biotechnology and pharmaceutical companies downstream in target validation, lead optimisation and clinical trials. Microarray Experiment The goal of a microarray experiment is to measure and compare the relative expression levels of thousands of genes in a sample simultaneously. Typically, these samples compare different stages of the cell cycle, cell types, healthy and diseased cells or different treatments. A higher level goal of genomic and gene expression experiments is to identify new genes involved in a pathway, potential drug targets or expression markers that can then be used in a predictive or diagnostic fashion. -16-

17 A typical microarray experiment involves the following steps: 1. Experiment design 2. Biological experiment to isolated total RNA Sample from the biological specimens 3. The Sample is treated and labelled with fluorescent dye 4. Hybridisation of the labelled Sample to the Array Chip 5. Washing, staining, and scanning of the Array Chip 6. Analysis of the scanned image 7. Generation of gene expression profiles Figure 4. Microarray experiment workflow. A typical microarray experiment workflow involves preparation of the biological samples (orange), array production (blue), in this case is supply by Affymetrix, and array hybridisation, scanning and image analysis (yellow). The actions taking place before and after the microarray laboratory workflow are in green [adapted from a figure provided by Amersham]. Figure 4 shows a detailed schema of the actions performed on a microarray experiment. Note that the tasks involved in the design and manufacture of the array itself are not performed at the Organon microarray laboratory. The arrays are provided -17-

18 by Affymetrix as a part of an agreement between Organon and Affymetrix. The GeneChip arrays most widely used by Organon are: Human Genome U133A and U133B Rat Genome U34A, U34B and U34C (and its new versions) Murine Genome U74A, U74B and U74C (and its new versions) Figure 5. Microarray chip type availability provided by Affymetrix. Phylogenetic tree schematic illustrating GeneChip arrays available today [adapted from a figure provided by Affymetrix, 4]. Microarray technology is associated with handling great amounts of data generated at different steps during the respective microarray experiment. This data must be processed and stored appropriately for the evaluation of experimental results. Several standardisation approaches have been developed for the description of microarray experiments during recent years by various institutions and companies. The most promising approach is the MIAME (Minimum Information About Microarray Experiments) standard, an international initiative supported by EBI (European Bioinformatics Institute) whose aim is to provide a standard defining the required minimum information that has to be stored and transferred for a gene expression microarray study. MIAME is not a formal specification, but a set of guidelines. An explanation of MIAME and other standards is given on chapter

19 The use of standards to integrate genomic analysis throughout drug discovery and development is important for transforming the drug discovery pipeline paradigm and will allow researchers to meet the challenges ahead. Figure 6 illustrates some examples of how the microarrays genomic experiments are used in the different stages of the new process of drug discovery. Figure 6. Modern pipeline of drug discovery. Some examples of genomic experiments involving the use of microarrays at different stages of the process are also presented [adapted from a figure provided by Affymetrix, 5]. The ultimate challenge to the bioinformatics community is the intelligent integration of data from many interrelated sources, which will be necessary to take greatest advantage of the knowledge in the data [Searls, 2000]. It is this integration that enables scientists to turn data into knowledge for answering complex questions in system biology and drug discovery. A complete system for expression arrays requires the implementation and development of different experimental protocols, databases and bioinformatics tools for data collection and analysis. Because of the recent advances in computational and statistical techniques, many scientists are focusing on the analysis of microarray data and developing models from these data. At the same time, issues of data collection, quality, and standards remain major bottlenecks to obtaining useful and applicable results [Bobashev et al., 2002]. The aim of this project was focus on the development of a Laboratory Information Management System (LIMS) that implements these pending issues to avoid any possible bottlenecks while performing the microarray experiments. -19-

20 Chapter 3 Laboratory Information Management Systems (LIMS) and Microarray LIMS Laboratory Information Management Systems (LIMS) The task of managing laboratory data is not a new one. Over the past two decades the use of LIMS has revolutionised how laboratories manage their data. A LIMS is more than software; it has become the workhorse of the laboratory encompassing laboratory workflow combined with user input, data collection, instrument integration, data analysis, user notification, delivery of information and reporting [Turner, 2001]. The essential concept of a basic LIMS is that of a computer system which would automate the clerical activities associated with the processing of the analytical results, improving accuracy and turnaround times to an acceptable level. LIMS is a technique independent of discipline and has applications in any industry where laboratory analysis is important, from Healthcare to Food & Drink and Pharmaceutical industries. A typical LIMS computer system bridges the gap between the analyser and the company s financial and administrative mainframes in all but the smallest labs. Most LIMS require considerable customisation to meet the needs of a specific laboratory. A customised LIMS will focus on the special aspects of their users needs. Differences in research and development or production chain in the individual organisations lead to an increase of the interest in customized systems [Bund et al., 1998]. Usually, also for customized systems, the core software is commercially available, although this is not the case for microarray LIMS, the applications on the market are not flexible or simple enough to be easily adapted to the client laboratory needs. -20-

21 Many of the most popular commercial LIMS take advantage of open systems architectures offering client-server capabilities and enterprise-wide access to lab information with web-based front-end. The development of microarray technology gives place to a new kind of laboratories and experiments that are now part of the new process of drug discovery. Thus the requirements of research groups and laboratory workers are changing. In the last few years microarray laboratories had been created in most of the big R&D companies and with them the need of new LIMS, microarray LIMS, is increasing. A LIMS system can be understood from different viewpoints: To an analyst, LIMS is indeed the computer system which interfaces to his analyser, computes, stores data, and prints results; To a laboratory manager, it is the system which lets him track samples, identifies their current status, audits their turnaround times, and provides better data on usage than he could ever have obtained from the best-organised of paper records; To a management information systems analyst, however, LIMS can and must be a feeder system, passing resource management data to the corporate mainframe. Microarray LIMS A reasonable working definition of the role of analytical laboratory is that it must deliver accurate, understandable results to the originator of the request for analysis, within a suitable timescale. As mentioned earlier for a microarray laboratory such an operation entails the sequence of receiving the biological samples and chips, ordering of chips if needed, assigning corresponding chips to samples, processing samples, hybridising, checking the results, (if necessary re-hybridise the sample), passing this information to the bioinformatics team for analysis and issuing a report to the requester. It is important to note that much of this cycle relates, not to analysis or hybridisation, but to the clerical handling of elements and results of the hybridisations. A microarray LIMS not only should guide the lab worker to perform this tasks, but it is also an indispensable tool for the laboratory manager to track resources, to complete statistical QC/QA routines, document and summarise resource -21-

22 utilisation within the laboratory. Such factors are key when very high cost genomics experiments are being completed. The following academic data management for microarrays systems were evaluated for its suitability for Organon microarray laboratory requirements. MADGE [Kokocinski et al., 2003], a data management software for cdna microarrays. BioArray Software Environment (BASE) [Lao et al., 2002], a more flexible platform for comprehensive management and analysis of microarray data. And finally, QuickLIMS [McIndoe et al., 2003], a LIMS system to manage data for the DNA-microarray fabrication. Although this projects are LIMS participating on different stages of the microarray process, none of them cover the microarray laboratory workflow data management specifically as Chip Tracker does, making this project novel and necessary. Some of these systems do not appear in the following table due to its recent release. Figure 7. Mainstream Academic Microarray Software. The table show the lack of LIMS systems in the field [3rd Millennium]. The commercial options for microarray LIMS are also very few. There is a new product by Amersham Biosciences, Scierra Microarray Laboratory Workflow System, which is a complete management system for microarray experiments and gene expression data which includes LIMS aspects. This software is probably the best option and is not included in the following table due to its recent release into the market. -22-

23 Figure 8. Mainstream Commercial Microarray Software. As shown there is also a lack of LIMS systems in the market. The systems shown covering LIMS functionality are not designed for a microarray laboratory workflow but for a microarray experiment workflow [3rd Millennium]. The general lack of software in microarray LIMS is because this is a new and emerging field. At the Scottish Bioinformatics Forum 2003 which took place in July at the National e-science Centre in Edinburgh, Professor David Gilbert, Director of the Bioinformatics Research Centre at the University of Glasgow talked about the present challenge for Bioinformatics to close the gap between computational (insilico) and wet-lab research, and the growing need of better LIMS to really contribute to R&D in biomedical and life sciences. -23-

24 Chip Tracker and the Organon Microarray Experiment Process The Organon Microarray Experiment Process Chapter 4 The Organon Microarray Experiment Process utilises four computer systems, ExpAnD, Chip Tracker (now been incorporated), developed in-house, plus Rosetta Resource Tracker and Resolver that were developed by Rosetta Biosoftware. With the integration of these four systems and the adherence to standard operating procedures in the laboratories, the Organon Microarray Experiment Process is a MIAME compliant platform for the storage, normalisation, presentation and publication of data obtained from microarray experiments. The microarray experiment process requires the input from a multidisciplinary team of people. The laboratory researchers design their chip experiments with help of the CSB (Chip Statistics and Bioinformatics team). They are responsible for defining the experimental annotation and generating the RNA for the hybridisations. The microarray lab workers, who are members of the Chip team, are responsible for creating labelled, fragmented crna from the original RNA and hybridising it to the chip. The CSB and Bioinformaticians are responsible for analysing the data. The Bioinformaticians work with the Systems team to maintain the databases and infrastructure of the platform. Standard Operating Procedures documents that enable the users and Chip team to carry out microarray experiments have been publish online on the intranet. The procedure steps required for the process are the following (see figure 9): 1) Experiment Design 2) Sample collection, RNA extraction and QC -24-

25 3) Data entry into ExpAnD, and shipment to the microarray laboratory 4) Chip team process (guided by Chip Tracker and Resource Tracker) 5) Data retrieval from Resolver and Data analysis Figure 9. Organon Microarray Experiment Process dataflow and systems. The figure shows the systems that participate in the Organon Microarray Experiment Process and it interaction. 1. Experiment Design Experiment design is a critical step carry out by the researchers and the Chip Statistics and Bioinformatics team (CSB). The final success of the experiment it is strongly connected with a well defined design. 2. Sample collection, RNA extraction and QC After experiment design the samples should be collected from the organism under study, RNA prepared and quality controlled at the researcher s laboratory. 3. Data entry into ExpAnD and shipment In order to describe chip experiment details the researcher have to use the Experiment Annotation Database (ExpAnD) to record all the details about the RNA samples that will be used to hybridise to the chips. The ExpAnD process ends with a shipment function that creates a list of XML files (XML LIMS Queue) with all the detail information of the samples that have been sent to the microarray laboratory. -25-

26 4. Chip Team Process (guided by Chip Tracker and Resource Tracker) The Chip Team at Newhouse receives submissions of samples from the remote and local research laboratories; both the Chip Tracker and Resource Tracker systems automatically parse the XML LIMS Queue with all the information of the arriving samples. We have developed the Chip Tracker XML Parser component that scans a server directory for new experimental samples data that are uploaded into the Chip Tracker Database without requiring an operator s assistance. This process runs every hour although its regularity can be set to a different interval or be stopped and manually executed. This component will be explained in greater detail on chapter 6. The Chip Team perform further QC on the samples and then use the total RNA to perform: cdna preparation, in vitro transcription, crna fragmentation, hybridisation mix preparation, and finally hybridisation (unlike in two-channel cdna arrays, a single sample is hybridise on a GeneChip). After hybridisation and scanning, visual chip QC and assessment of some QC parameters is performed, data that pass requirements are transferred by FTP to the Rosetta Resolver server and automigrated into the database. Figure 10 highlights the hybridisation tasks involved in the hybridisation process that were previously enumerated in this paragraph. These are the actions performed by the microarray lab worker with the guide of the Resource Tracker system. Note that in addition to the hybridisation tasks the microarray lab workers have to perform management and tracking the biological samples and chips. These management tasks (now performed by using Chip Tracker) together with the hybridisation tasks conform the microarray laboratory workflow. 5. Data retrieval from Resolver and Data Analysis Rosetta Resolver is Organon s main chip data analysis software, and it is the application that the users will first see their chip data on. The data analysis procedures will largely depend on the design of the experiment and before actual data analysis to identify genes of interest is started, some thought should be given to management of the intensity hybridisations in the context of the experiment setup. -26-

27 Figure 10. Hybridisation Process. Highlights the principles of the standard eukaryotic assay. These are all the steps of the hybridisation process that the microarray lab workers at Organon have to go through in order to finally get the scanned image to be analyse by the bioinformatics department. The process can be summarised as orna cdna lcrna flcrna Hybridisation Wash and Stain Scanning [Affymetrix, 1]. At the moment the through put of the microarray laboratory on the experiment process is about 140 chips per month, with a turn around time for experiment of about a month. With the help of the recently installed systems, Chip Tracker and Resource Tracker, the aim of the Chip team is to reach a through put of over 180 chips per month with a turn around time of less than 2 weeks, limited by the resources. Further explanation on the functionality of the Resource Tracker, ExpAnD and Resolver systems, their interaction and how the information flow through is given on the integration with existing systems section in the next chapter. -27-

28 Microarray Laboratory Workflow and Chip Tracker Scope Chip Tracker forms part of an entire platform. This project is the latest to be added and it completes the data management aspects of the microarray platform. Chip Tracker was designed to follow the natural workflow of the microarray laboratory worker. The main features of the Chip Tracker Microarray LIMS are: Manage and administrate the stock of samples and chips. Workflow management. Automated microarray experiment data collection and notifying the Chip team of what chips are required for samples that are en route. Accurate tracking of chips and samples. Pre-populate and prompt purchase orders of the chips required to perform the experiments for recently shipped samples. Load newly purchased chips into the system. Assign chips to samples for hybridisation. Log chips with the hybridisation results. Create custom reports and statistical analysis on the microarray facility usage. Experimental information entered by the researcher is captured by the ExpAnD system and is therefore not part of the microarray laboratory workflow and it is not in the scope of the Chip Tracker system. At the microarray laboratory most of the chip and sample tracking is now going to be done by Chip Tracker, but the tracking of the extracts of the original RNA samples while performing cdna preparation, in vitro transcription, crna fragmentation, hybridisation mix preparation, and the final hybridisation is done with the Resource Tracker system. Although these tasks are part of the microarray laboratory workflow it was not necessary to include them in the scope of this project. Resource Tracker is an add-on component that fully integrates the Rosetta Resolver system, which as mentioned earlier, was specifically developed by Rosetta for the Organon microarray laboratory. Resource Tracker interacts with Resolver database and client interface directly. -28-

29 Figure 11 describes the Organon microarray laboratory workflow. Apart from the RNA extractions during the hybridisation process, every step of the workflow is guided by Chip Tracker. By looking at the figure you will be able to understand how data flows through the Chip Tracker application. Figure 11. Microarray laboratory workflow. This are the features cover by Chip Tracker to complete the Organon microarray laboratory workflow. The following images (figures 12a and 12b) capture the scope of the Chip Tracker project. Figure 12a was taken from the Chip Tracker Unified Modelling Language (UML) model we developed in order to document and guide the project designing. Figure 12a. Chip Tracker Use Case Diagram (taken from the Rational Rose Chip Tracker model). -29-

30 Figure 12b illustrates how Chip Tracker interacts with the rest of the Organon Bioinformatics infrastructure and what is the scope of activities of the application and microarray laboratory. Figure 12b. Chip Tracker Scope and Interaction. The image illustrates the interaction between the microarray platform systems and the activities perform at the microarray lab. Some of the main features of the Chip Tracker system are shown in orange arrows. The retrieval of data from the scanned image and the posterior analysis of the microarray data are perform by the Bioinformatics team. Organon had acquired Rosetta Resolver, which is one of the best gene expression data analysis software solutions in the market to help the bioinformaticians with these tasks. As Chip Tracker is managing, tracking and administrating the chips and samples, and Resource Tracker is tracking the RNA extracts along the different experimental steps, all the functionality requirements of the Organon microarray laboratory workers are completely fulfilled and systemised. -30-

31 Chapter 5 Chip Tracker Design and Architecture The Chip Tracker microarray LIMS was designed to cover the management aspects of the Organon microarray laboratory workflow and to be integrated with the existing Bioinformatics infrastructure. Although the individual components of the system are connected with specific laboratory environments, some general principles have guided the project design. These include the compliance to the actual microarray software communication standards and the use of an industry standard relational database management system combined with platform-independent web browser interface for data entry and retrieval. Chip Tracker is a web application that was designed using a three-tier server model. A carefully designed user friendly web interface allows the lab workers to gain password-protected access to the system remotely. The functionality provided by Chip Tracker will help them to manage and perform the steps in the microarray laboratory workflow. The Chip Tracker has a three tier structure composed of a: Presentation tier, a Middle (Application) tier and a Data Management tier. The Presentation tier, which is the client side tier, is the Graphical User Interface (GUI) - an HTML based visual display generated dynamically by Active Server Pages (ASP) that serves as the portal for the lab worker to interact with the application. The Middle tier acts as an application server, implementing the microarray experiment workflow logic in ASP and accesses the Chip Tracker Database. A Visual Basic component to automate data collection of the Samples being sent to the laboratory, called Chip Tracker XML Parser, is also included in this tier. And finally, the Data Management tier running Oracle 8i Relational Database Management System (RDBMS) where we have designed and implemented the Chip Tracker Database, a simple database schema to model the microarray laboratory workflow. -31-

32 The Chip Tracker application was designed with a dual purpose, it can be used as a stand-alone system or it can be fully integrated with the existing Organon infrastructure. The GEML data exchange format, a standard for communicating information about microarray experiments, was used to integrate Chip Tracker with the existing Organon systems. Figure 13. Chip Tracker three-tier architecture diagram. The decision for creating a three-tier architecture, which moves some of the processing out of the server into the clients and has a separate data access layer, was based on the following: Centralised management of the application, that can be reused or easily modified. Concentration and distribution of requests from the client, so the performance is better and the application is scaleable. Flexible hardware architecture, allowing all three layers to be separately distributed or replicated if required. -32-

33 Integration of existing systems. The Chip Tracker XML Parser implemented in the middle layer provides access to other microarray applications and hides complexity. The middle layer offers a transparent access to underlying systems and contains inter-system functionality. The middle layer also takes care of locating resources, accessing them and gathering results. This architecture would enable us to develop a better and more sophisticated application with the only disadvantage that the resulting software can be more complex and becomes more difficult to understand. The choice of technologies used in this project is based on the Organon technical standards and is a result of a common agreement between ourselves, Organon s Bioinformatics and Software Development teams. The Chip Tracker was the first developmental project conducted in this way. Presentation Tier (Front End) This layer represents the primary interface to the user. Special attention was made in designing a user friendly Graphical User Interface (GUI), suitable for biologists working at the microarray laboratory. The microarray laboratory workers had constant input throughout the life cycle of the project to ensure that the system met their user requirements. Another advantage of the Chip Tracker three-tier architecture is the implementation of what is known as Thin Client, which means that the clients implement only the graphical user interface leaving the server side with the implementation of application logic and the data management. A thin client makes the installation of the system simpler because there is nothing to be installed on the client computer; a web browser is all that is needed. This distribution allowed us to use the computing power at the client for sophisticated presentation, which do most of the control on the application -33-

34 server side and therefore increased the performance, control, maintenance and integration. The technology used to create the GUI includes: Hypertext Markup Language pages (HTML) which are generated dynamically by ASP (Active Server Pages are dynamically processed by the web server before being sent to the client), Hypertext Transfer Protocol GET and POST requests (HTTP), Uniform Resource Locator encoding (URL), JavaScript, Stylesheets. The use of HTML forms is the most common way to communicate data from the presentation to the middle tier. Arguments can be passed using the methods HTTP GET, where the value of the form fields are encoded in the URL, which is not secure, and HTTP POST, a more common method used to send form information in a hidden way. The latter method, POST, is the one mostly used by Chip Tracker to send information to the server or between pages. JavaScript embedded inside the HTML pages it is used to add further functionality to the presentation tier. This functionality includes the validation of form input fields and browser control as the display of pop-up alert messages or control the characters inserted into the system from the clients keyboard (i.e. allow only number keys while entering a numeric filed). By utilising client-side JavaScript in this way the communication between the client machine and the web/application server is minimised and the database field data type constraints enforced. In order to give Chip Tracker interface the Organon look and feel, especially for the fonts and colours, we created a Cascading Style Sheet (CSS) with the definition of how to display the system HTML documents. Many technological innovations rely upon User Interface Design to elevate their technical complexity to a usable product. Technology alone may not win user acceptance and subsequent usability. The User Experience, or how the user experiences the end product, is the key to acceptance. By following a Prototyping -34-

35 approach and performing User Usability Testing throughout the design process we were able to ensure a user optimised interface. This empirical testing permitted the users to provide data about what does work as anticipated and what does not work. Figure 14. Interface features. The image illustrates some of the interface features and its explanation. These features had been specifically designed to make the Chip Tracker a microarray user friendly tool to guide the user along the microarray laboratory workflow. A good User Interface Design can make a product easy to understand and use, which results in greater user acceptance and facilitates the system usage and incorporation in microarray laboratories. Form the User Acceptance Test (described in chapter 8) we received a good feedback about the interface, to the point that the users claimed that they had no need to use the help pages in order to understand how to use the system. Data entry fields and validations behave automatically according the laboratory workflow. Features implemented to guide the user along the different pages of the system shown in figure 14 also include; inhibited field control, specific default values, entry data control. Most of these features have been implemented in JavaScript code. -35-

36 As mentioned earlier the presentation tier acts as a portal to allow the users to store, retrieve and analyse the data in the Chip Tracker Database. The following image represents the site map of the Chip Tracker interface. Figure 15. Chip Tracker interface sitemap. The functionality and information presented in the Chip Tracker interface is detailed in chapter 7, where every page of the system is described in the framework of the microarray laboratory workflow. Middle Tier The main functionality of the Chip Tracker application is implemented in this tier, which acts as an application server. This includes: Encoding of the microarray laboratory workflow logic in ASP being administered by Internet Information Server (IIS web server). Connection to the Chip Tracker Oracle 8i database. Automatic sample data collection from ExpAnD system. Accept form input from the GUI in the presentation tier. Generation of output pages and reports for the presentation tier. The Chip Tracker workflow logic is encoded in ASP pages. The ASP uses both VBScript, which stands for Visual Basic Script, and JavaScript to provide application logic on the server and client sides respectively. The VBScript scripting components implement the server-side processing of the page (i.e. the page is dynamically -36-

37 processed by the web server before being sent to the client). Where as JavaScript implements the client-side processing. The ASP pages work in the following way, first form data is posted via HTTP or via a URL from the client browser to an ASP page on the application server. The ASP script is executed via ASP.DLL on the web server and database access performed on the database server; finally a formatted HTML page is sent back to client browser including the results of the script execution. Connection to the Chip Tracker Database is carried out by using standard Structured Query Language (SQL) commands that are passed to an Open Database Connectivity (ODBC) driver. The ODBC driver is the standard protocol for accessing information in SQL database servers developed by Microsoft Corporation. This database driver is installed in the middle layer, between the application interface and the database. The purpose of this is to translate the application s data queries into commands that the RDBMS understands. Both the application and the RDBMS must be ODBCcompliant for this protocol to work. The Chip Tracker system accesses its database by 2 different types of queries. Static queries are used for standard queries such as checking chip availability or insert arriving chips into the corresponding tables. Other queries are created on the fly depending on the conditions selected by the user to build a custom report for example. All the accesses to the database are carried out using SQL queries that are created or embedded in the ASP pages. The implementation of Chip Tracker uses Internet Information Server (IIS) as a web server to administer the ASP and HTML pages. The use of IIS and ASP make it easy to access data and put it on a web page. Chip Tracker use ASP to make decisions about what to display on the interface web pages. In order to collect the sample information stored in the ExpAnD system automatically a small Visual Basic application named Chip Tracker XML Parser was developed and installed in the application server. The XML Parser automatically scans a server directory for new experimental samples data. If a new data file is found it is uploaded into the Chip Tracker Database without requiring an operator s assistance. This component is discussed in greater detail in the next chapter. -37-