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Data Management and Protein Production – from Lab Notebooks to LIMS Robert Esnouf

Data Management and Protein Production – from Lab Notebooks to LIMS Robert Esnouf Oxford Protein Production Facility, University of Oxford… …and the PIMS development team. EBI, 23/9/2008. Introductory presentation. What is information management? What types of laboratory process are there?

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Data Management and Protein Production – from Lab Notebooks to LIMS Robert Esnouf

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  1. Data Management and Protein Production – from Lab Notebooks to LIMS Robert Esnouf Oxford Protein Production Facility, University of Oxford… …and the PIMS development team EBI, 23/9/2008

  2. Introductory presentation • What is information management? • What types of laboratory process are there? • What information should be recorded? • Where can LIMS benefit protein scientists? • What are the potential pitfalls? • Introduction to PIMS concepts • Linking data upstream and downstream • Target selection and crystallization

  3. What is information management? • A way of storing information so that it can be retrieved later: • Mechanism varies from human memory (surprisingly common) to sophisticated relational database systems • Purpose of retrieval can include supporting the next experiment, sharing data with collaborators, publication and depositing data • In a laboratory setting information management is a branch of bioinformatics • Automated experiments may require electronic information management

  4. Types of information management • Paper-based records • Well suited to independent research • Long-term archive • Electronic Laboratory Notebooks (ELN) • Central repository of information • Electronic version of paper systems • Laboratory Information Management Systems (LIMS) • Relational database • Model for laboratory processes • Snapshot of current state of laboratory

  5. What information could be recorded? • Progress (target) tracking • Records status of a project • Progress through a (usually) linear workflow • Sample tracking • Where is a sample (which fridge, freezer etc.)? • Exchange of samples between labs • User auditing • Who did what and when? • Who authorized/checked work? • What is a sample? • What experiments have been done?

  6. What is a sample? • Is it a reagent? • Which batch number was it? • Does it have use-by date? • How much is left? • What target does it “belong” to? • Does it belong to multiple targets (a complex)? • What is in the sample? • Is it a single entity? • Is it a mixture or a complex? • Record a full chemical description or how to recreate sample?

  7. What is an experiment? • Is it part of a fixed workflow? • Suited to repetitive, well defined work • Maps directly onto progress tracking • Workflow should be relatively invariant • Is it an isolated process? • Take input sample(s) • Work on them • Record results • Produce output sample(s) • No concept of a workflow

  8. (Electronic) laboratory notebooks • A store for unformatted information • Easy to enter data • Fairly easy for single user to retrieve data • Difficult for others to retrieve data • Difficult to search for data • Difficult to share non-electronic data • Most data organized chronologically • Single projects can become fragmented • Data associated temporally • Non-electronic data can prove date of discovery

  9. Blurring ELN and LIMS • Linking samples and targets • Sharing data • Controlled vocabulary • E. coli not Escherichia coli • Sodium chloride not NaCl • Fixed descriptions of protocols • Moving towards the LIMS relational database • Model for laboratory workflows and processes

  10. Benefits of LIMS • Distributed projects • Information can be accessed anywhere • Collaborative projects • Different people record into same store • Miniaturized projects • Labelling of samples becomes impossible • Automated projects • Handling layouts in plates etc. • High-throughput processes • System managed by computer with automated sample tracking

  11. Potential pitfalls of LIMS • Data loss • Hardware failure – managable • Data corruption – potentially catastrophic • Data integrity • Data need to be described properly • LIMS can default to being ELN • Extra burden of recording data • Takes time for no immediate benefit • Need easy and intuitive input – risk of sloppiness • Compliance • Unrecorded data are lost • Incomplete data may break data “chain”

  12. Recording Gateway cloning protocols • Example 96 constructs through PCR, Gateway cloning & expression screening with 2 cell lines & 2 protocols: • Uses 34 96-well plates and 36 24-well plates and generates 480 images of colony wells, 1536 lanes on agarose gels and 416 lanes on SDS–PAGE gels

  13. Recording Gateway cloning protocols • Example 96 constructs through PCR, Gateway cloning & expression screening with 2 cell lines & 2 protocols: • Uses 34 96-well plates and 36 24-well plates and generates 480 images of colony wells, 1536 lanes on agarose gels and 416 lanes on SDS–PAGE gels Difficult to decide which data need to be recorded… Burden of recording unnecessary data can be fatal!

  14. What is PIMS? • BBSRC SPoRT funded two consortia: • Scottish Structural Proteomics Facility (SSPF) • Membrane Protein Structure Initiative (MPSI) • PIMS is funded to develop a laboratory information management system (LIMS): • Funded by the BBSRC SPoRT initiative • Funding Jan 2005 – Dec 2009 • Supports SSPF, MPSI, OPPF & YSBL • Developers in Daresbury, EBI, OPPF & YSBL • Support from OPPF, Dundee & Daresbury • http://www.pims-lims.org/

  15. Why develop PIMS? • Longstanding need for rational data management for protein production • Complex, ever-changing workflow • To exploit higher throughput • To aid collaboration and make data public • Academic LIMS (and industrial?) • LISA, HalX, SESAME, MOLE, (Beehive) • Specific to one site, hard to maintain • PIMS is a collaborative effort to find a common solution • Most laboratories have some similar processes • All have some unique processes • PIMS is fully featured LIMS, not target tracking

  16. Some ancient history • Starts with 2001 Airlie House agreement • To share protein production data • TargetDB is limited implementation • Detailed specification of terms • European-based projects • eHTPX: data exchange models (dictionaries) • HAL & HALX: LIMS concentrating on workflow • MOLE: LIMS built on generic data model • SPINE encouraged collaboration • Loose PIMS consortium formed to seek a common solution • BBSRC SPoRT provided funding opportunity

  17. Technologies used • PIMS is used from a web browser • Mozilla Firefox or Internet Explorer • No client software to install (perhaps plugins) • Windows, Macintosh and Linux clients • PIMS requires a web and database server • Typically the same machine • Web server Apache Tomcat • Development on free PostgreSQL • Now available for Oracle • Windows and Linux servers • Technologies used by developers • Java1.5, Hibernate, JUnit, BioJava, dot, batik, AJAX, ...

  18. PIMS will be properly introduced by the other speakers…

  19. Aspects of application development Crystallographicapplications Graphicsapplications LIMS Robotics

  20. PIMS uses a few simple key concepts which can be linked together to model complex workflows Targets Description of sequences, store annotations Constructs Starting points for real experiments, link to targets Samples Tracked samples made & used by experiments Samples have types, owners, locations etc. Experiments Take one (or more samples), produce new sample(s) as outputs Basic concepts of PIMS

  21. A protocol is a reusable user-defined template describing what you record for your experiments. Parameters Numerical values, free text values, T/F. E.g. incubation temperature or the number of PCR cycles; details of incubation conditions; was reagent added? Input Samples Samples or reagents used when performing an experiment that you wish to track Output Samples Samples or reagents produced when performing an experiment that you wish to track Experiments and protocols

  22. Typing helps PIMS offer sensible choices: only a plasmid can be used for transfection experiments… Samples Typed to show what they are Input/Output samples for protocols State what type of sample can be used and what is produced Experiments and protocols An experiment type is defined by its protocol. A protocol type links similar protocols together Typing of PIMS items

  23. Experiments & samples → Workflows Sample A Expt 3 Expt 1 Sample D Sample B Expt 4 Expt 2 Sample C Sample E1 Sample E2

  24. A holder groups samples. This allows PIMS to perform plate experiments in groups Samples For plate experiments output samples of previous experiment are mapped to input samples of next. (Provided sample type matches!) User interface for plate experiments Gives graphical and spreadsheet views. Allows editing, reformatting and spreadsheet upload The PIMS holder (plate experiments)

  25. Can attach files to samples, experiments& targets

  26. Basic protocols used at OPPF PCR Verification PCR Clean Up Sequencing InFusion Transformation Scale Up Plasmid Prep Lysis Purification Trial Expression Concentration

  27. A workflow derived from PIMS

  28. Experiments can read/write data

  29. How was a sample produced?

  30. Export to external target tracker software PDF reports of how samples are made asa permanent record

  31. Used at OPPF and externally by YSBL & SSPF OPAL freely available over the web OPTIC DB managing annotations for 12226 targets Target annotation at the OPPF Albeck et al. (2006) Acta Cryst. D62, 1184

  32. Example: predicting crystallizability • Simple to understand, calculated in advance • Helps to set priorities for lab work

  33. Example: improving construct design

  34. OPPF crystallization facility robots 4°C, 1000 plates 21°C, 10000 plates

  35. Live since Jan 2002 22570 plates 51845521 images >28TB images 351 registered users for OPPF site 100 OPPF/STRUBI 118 Other Oxford 50 Other UK 83 Elsewhere Automation of crystallization trials Mayo et al. (2005) Structure 13, 175

  36. OPPF crystallization management Controlled web-based access from anywhere, e.g. synchrotrons

  37. xtalPIMS managing high-throughput crystallization trials • Fusion of PIMS, Vault and eHTPX work • Can support multiple imaging and storage robots

  38. Acknowledgments PIMS PIs • Kim Henrick, Dave Stuart, Keith Wilson, Richard Blake, Jim Naismith, Neil Isaacs PIMS supported • Anne Pajon, Ed Daniel, Marc Savitsky, Susy Griffiths, Katya Pilicheva CCP4 supported • Chris Morris, Bill Lin Other projects • OPPF: Jon Diprose • MPSI & SSPF: Petr Troshin, Jo van Niekerk • xtalPIMS: Ian Berry, Gael Seroul, Diederick De Vries

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