What is a LIMS

1. What is a LIMS ? LIMS � Laboratory Information Management System Computerized system that tracks and manages samples through a protocol interfaces for both laboratory personnel and instruments helps support high throughput operations Laboratory Information Management System or LIMS is a system designed specifically for the particular laboratory. This might include research and development labs, testing labs, quality assurance labs, and more. Typically, LIMS connect the analytical instruments in the lab to workstations or personal computers. These instruments � for example chromatographs -- are used to collect data. An instrument interface is used to forward the data from the hardware to the PC, where the data is organized into meaningful information. This information is further sorted and organized into various report formats. A full-featured LIMS will manage the various lab data on every step of the protocol. (Next slide). <What is high throughput? What is small scale?> Laboratory Information Management System or LIMS is a system designed specifically for the particular laboratory. This might include research and development labs, testing labs, quality assurance labs, and more. Typically, LIMS connect the analytical instruments in the lab to workstations or personal computers. These instruments � for example chromatographs -- are used to collect data. An instrument interface is used to forward the data from the hardware to the PC, where the data is organized into meaningful information. This information is further sorted and organized into various report formats. A full-featured LIMS will manage the various lab data on every step of the protocol. (Next slide). <What is high throughput? What is small scale?>

2. Enterprise cover all aspects of scientific research data capture reagent use and purchasing tracking Protocol-specific cover a specific protocol data capture Types of LIMS

5. Microarrays large-scale sequencing projects like the human genome project have given us the ability to examine the complete transcriptome (the transcriptional response to an environmental challenge new (and expensive) technology large output of data

6. Microarray Data produced in a tabular format (rows and columns) users are relatively unsophisticated in computational and informatic skills much data ends up in spreadsheets which lack the capability to handle rich datasets (no complex query or visualization capabilities)

7. Microarray Databases plethora of databases and schemas three types of interactions: local data management publication of data in a repository analysis of repository data the latter two interactions require a certain level of sophistication to consolidate exogenous data

8. Microarrays: Concept

9. Microarrays: Raw Data

10. Microarrays: Data

11. Local Databases make data available to local researchers may have WWW-based tools database and compute server centralized and closely linked

12. GeneX National Center for Genome Resources www.ncgr.org/research/genex relational database with Perl, R, and Java components

13. GeneX Features Free integrated and extensible toolset multiple types of array technology in single database experiment-centric design supports an XML specification to allow interchange between databases

14. BASE BioArray Software Environment http://base.thep.lu.se/ Relational database (MySQL) with WWW interface built upon C++/javascript/PHP

15. BASE Features Free MIAME compliant user administration array production sample management

17. Repositories provide public access to multiple datasets create standard database similar to sequence automatic deposition of data upon publication

18. Stanford Microarray Database genome-www4.stanford.edu/MicroArray www-based database and a dataset distribution system relational database perl/java toolset supports some complex querying as well as browsing for datasets datasets distributed as compressed flat-files and/or graphical images

19. GEO Gene Expression Omnibus www.ncbi.nlm.nih.gov/geo/ data repository and distribution system precomputed definitions and descriptions of data to aid in data set retrieval

20. Data Interchange Proposed interchange standard MIAME Proposed OMG exchange standards MAML GEML NetGenics

21. MIAME Minimal Information About a Microarray Experiment www.mged.org/Annotations-wg/ Goal specify the minimum amount of information needed to ensure interpretability facilitate creation of repositories encourage journals and funding agencies to require submission of data to repositories

22. Design Considerations reflect data accurately efficient access to data efficient storage of data compatibility with other databases

23. Data Representation

24. MIAME Considerations Experimental design: the set of hybridization experiments as a whole Array design: each array used and each element (spot) on the array Samples: samples used, extract preparation and labeling Hybridizations: procedures and parameters Measurements: images, quantitation, specifications Normalization controls: types, values, specifications

26. Background Center for Biomedical Genomics and Informatics Engaged in a number of gene expression studies ranging from liver disease, osteoarthritis and cancer Species studies human and rat cDNA in house printed slides (5K human chip, 40K human chip)

27. GMU Clinical Genomics studying the relationship between disease and genome expression clinical measurements standard battery of tests genomic measurements gene expression levels genetic variation derive correlation between clinical/genomic factors and treatment outcome

29. Dataflow

30. Generic difference in gene expression patterns We do this via visual inspection following clustering (genes and samples) Often we will reduce the number of genes by some criterion (e.g., cluster only on genes that are 2-fold expressed in at least one sample/category) Often we will group the number of samples by condition in order to compensate for the lack of replicates

31. Clustering of genes and samples

32. Disease vs. Normal 9 genes Normal vs. disease9 genes Normal vs. disease

33. Clinical Data Challenges Collection text formats disperse sources Storage heterogenous incomplete degenerate Protection HIPPA regulations

34. Large Clinical Databases Nadkarni and Brandt (1998) JAMIA 5, 511 Issues involved in data mining EAV databases Nadkarni et al. (1999) JAMIA 6, 478 Extension of EAV with classes and relationships Chen et al. (2000) JAMIA 7, 475 Performance of EAV/CR

35. Issues with Clinical Data Too many columns Over 43,000 attributes Sybase capacity 1024 columns per table 32 indexed up to 50 tables per query Sparse data Multiple entries

36. Sample Clinical Table

37. Solution: EAV Entity-Attribute-Value form of row modeling turns columns into rows eliminates sparse data reduction in database size Faster single value queries Pushes depth rather than width

38. EAV Clinical Table

39. Accessing Single Attributes

40. Limitations for Data Mining Complex boolean queries tough no set operations Complex SQL nested subqueries self-joins Performance

41. Ad Hoc Query Interface Presents a user interface which generates the required complex SQL queries

42. EAV/CR Simulation of a complex logical schema using an extensive yet simple physical schema Addition of object tables to contain like attributes strong data typing Creates metadata about objects to help describe the relationships between data objects

45. Testing EAV/CR Data sources used microbiology data from VA patients extracted from existing DB loaded in EAV/CR schema scaled by replicating data with new IDs Benchmarking two attribute centered queries two entity-centered queries

48. Results Comparable speeds for entity queries massive hit for attribute query up to 10-fold worse "ancestor" improvement represents denormalization space for performance trade-off

49. EAV for Clinical Genomics ? performance issues a problem data mining on attributes I/O issues full EAV not feasible partial row modeling a good option

50. Clinical Database Used CGO database out of Univ of Arkansas as a template Myeloma database Want to generalize it for any cancer

52. Altering CGO remove gene chip references affymetrix MIAME/MAGE non-compliant attach to GeneX generalize clinical system row model test results row model questionaires

54. HIPAA Health Insurance Portability and Accountability Act �ensure the integrity and confidentiality of [patient] information, protect against reasonably anticipated threats or hazards to the security or integrity of the information or unauthorized uses or disclosures of the information�

55. Clinical Data Flow