Laboratory Information
Management Systems
Douglas Perry, Ph.D.
IU School of Informatics
Laboratory Information
 The sole product of any laboratory,
serving any purpose, in any industry, is
information
2
Laboratory Informatics Defined
 The specialized application of
information technology to optimize and
extend laboratory operations
3
Data Flow in the Laboratory
Lab Automation
& Robotics
Chromatography
Data Systems
Data
Warehousing
Equipment
Interfacing
Laboratory
Instruments
Data Acquisition
Data
Analysis
Laboratory Information
Management
Systems (LIMS)
Information Processing
Electronic
Laboratory
Notebooks
Data
Mining
Knowledge Management
4
Data Acquisition
Architecture
Local Laboratory Network
Instrument
Data Manager
Specialized Data System
PC
SC
Instrument Network
GC
LC
GC
Manual
Data Entry
SP
F
AB
5
Information
Processing
Architecture
LIMS
DBMS
Local Laboratory Network
Instrument
Data Manager
Specialized Data System
PC
SC
Instrument Network
GC
LC
GC
Manual
Data Entry
SP
F
AB
6
Scientific Data
Management Architecture
Data
Warehousing
Electronic
Laboratory
Notebook
Data Mining/
Data Analysis
DBMS
LIMS
Local Laboratory Network
Instrument
Data Manager
PC
SC
Chromatography
Specialized Data
Data
System
System
GC
LC
GC
Instrument Network
Manual
Data Entry
SP
F
AB
7
Enterprise Architecture
Data
Warehousing
Electronic
Laboratory
Notebook
Data Mining/
Data Analysis
DBMS
LIMS
Wide Area Network
8
Functional Hierarchy
in Laboratory Informatics
SDMS, ELN
rules
people
CDS, LIMS
rules
context
DAQ, LAB AUTO
9
Basic Concept of LIMS
 Laboratory Information Management System
 Definition: A collection of computerized methods to acquire,
analyze, store, and report laboratory data
 No “standard” LIMS
 Developed
 Customized
 Configured
 LIMS are disparate because client labs are highly diverse

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Analytical
Clinical
Environmental
Forensic
Production
10
Genesis of LIMS
Facilitation of Routine Laboratory Operations
IN
OUT
Sample
Labeling
Reporting
Job
Assignment
Results
Verification
Progress
Tracking
Results
Entry
11
Modern Lab Workflow
Project/Study Maintenance
New Project
Page
Basis of study may be mult
species:
New Study
Page
Blood
Tissue
Cellular Based
Cell type
Sample Based
Sample Type
Assign
Work
Mgmt
IN
Assign
Work
Mgmt
Add
Samples
Patient
Add tests
Assign
Tests/work
OUT
Project Manager/Owner
Sample Management
Samples may be:
Submissions
Customer
Internal
Libraries
typically internal
Requests
specific samp type
Sample
Type
Add
Samples
Synth
Receive
Samples
Tissue
Find
Samples
Blood
Request
Samples
Serum
Update
Status
Protein
Production
View MS
Get MS file
for each
Spot
Activate/Assign
Tests/Work
Test Data
Entry
Ident peptides
and protein(s)
Search MS
MS and PID Search Manager/Owner
Other
Soluble
Test Results
Preview
Maldi Spot
Load
Run Maldi
Pass
Gel Make
and Load
Gel Image
Preview
Pass
Store Spot
Plate
Sample Handling Manager/Owner
Ready
Samples
Sample
Prep/Dilute
Run 1D and
Treatment
Selection
Select
Treatment
Spot
Digestion
Select 2D
Gels type
and transfer
Perform
Treatment
Make and
Load 1D
Gel
Spot List to
PAA
MS Prep Manager/Owner
Run 2D gels
and set
notification
Fractionate
Gel Staining
Protein Sample Manager/Owner
New Spot
Plate
Created
Acquire Gel
Images in
Progenera
Acquire
Global Spot
List
Acquire
Differential
Spot List
Sapphire
Sync
Image Analysis Manager/Owner
12
Challenge and Opportunity
1988
2003
1 experiment
1 experiment
1 gene
10,000 genes
10 data
10,000,000 data
13
One Real-Life Example
# Samples (M/yr)
150
3.5
1998
2002
14
Preparation and Analysis
1988
1 experiment
2003
DAYS, WEEKS,
OR MONTHS
1 gene
10 data
1 experiment
10,000 genes
ONE
AFTERNOON
10,000,000 data
15
Universal Need for LIMS
 Regardless of focus, all labs need:
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Quality assurance and control
Error reduction
Fast sample turnaround
Management of information
16
Increasing Need for LIMS:
Information Management
 Advances in instrument automation
 Robotics for sample processing
 Microarray technology
 Increased government regulations
 GxP: GLP, GMP, GCP
 Demands of enterprise resource planning
 CRM, MRP, MES
17
Increasing Need for LIMS:
Quality Assurance & Control
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Quality assurance (QA)
Quality control (QC)
Statistical process control (SPC)
ISO 9000
18
Increasing Need for LIMS:
Error Reduction
 Data entry restriction
 Acceptable parameters
 Drop-down lists
 Range checking
 Customer specifications
 Internal controls
 Sample log-in
 Bar code reader
 Automatic calculations
19
Increasing Need for LIMS:
Sample Turnaround

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Automated data entry
Automatic calculations
Rapid data retrieval
Automatic reporting
20
Types of Data
Used in LIMS

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Alphanumeric
Descriptive
Limits
Numeric
TDU Stamp
21
Types of Laboratories
Using LIMS
 Research & Development labs
 Analytical labs
 Manufacturing labs
22
Research & Development
Laboratories
 Objective
 Support pure or applied research
 Characteristics
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Small, autonomous
Diverse, non-routine tests
Low sample volume
Flexible operations
High internal security
Low, circumscribed data flow
23
LIMS requirements for
R&D Labs
 Flexibility
 Sample types, tests, methods, reports
 Traceability
 Audit trails, on-the-fly notation
 Security
 Very limited access, but with lateral
authorization
 Time
 Usually not an issue
24
Analytical Laboratories
 Objective
 Provide a service (information)
 Characteristics
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Large, organization-dependent
Routine tests
High sample volume
Client-driven operations
High, narrow data flow
25
LIMS Requirements for
Analytical Labs
 Tracking
 Samples, orders, reports
 Scheduling
 Tests, equipment maintenance
 Quality assurance
 Validation, QA/QC
 Data access and sharing
 Instrument interfacing
 Client-centered reporting, CoA
26
Manufacturing
Laboratories
 Objective
 Assure product specifications
 Statistical process control
 Characteristics
 Ongoing testing: raw materials, process, final
product, stability
 Dynamic, demanding environment
 High, wide data flow
 Fast turnaround
27
LIMS Requirements for
Manufacturing Labs
 Rapid sample turnaround
 Automation, bar-code entry
 Connectivity
 MRP, ERP, CRM
 Statistical analysis
 Statistical process control
 Flexible reporting
 Diverse information demands
28
Functional Model of
LIMS
DBMS
A
B
C
reporting
29
Data Capture
 Sample identification
 Log-In, reading, labeling
 Work scheduling
 Test initiation, test assignment
 Data acquisition
 Interfacing, instrument control
30
Data Analysis
 Data transfer
 Buffer tapping, file transfer
 Data processing
 Conversion, reduction, specification review,
statistical analysis
31
Reporting
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Client-centered reports
User-defined reports
Automated batch reports
Tabular and graphical formats
Ad hoc queries
Event triggers
Exportation to external IS
32
Lab Management
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Work scheduling
Sample tracking
Job tracking
Standard Operating Protocols
(SOP)
 Pricing and invoicing
 Cost analysis
33
Systems Management
 Security
 External: unauthorized access
 Internal: data sabotage
 Data archiving
 Mirroring
 Off-loading
 Data warehousing
 Long-term storage
 Far-off retrieval
34
Enterprise-Scale
Information Management
Research &
Development
Customer
Service
Product
Support
Regulatory
Affairs
Laboratory
Quality
Assurance
Quality
Control
Raw
Materials
Manufacturing
35
LIMS Implementation
TIME (months)
4
laboratory
objectives
laboratory
operations
functional requirement specifications
+6
lab personnel
+8
vendors
administration
product
selection
customers
IT department
+9
installation
+3 = 2.5 years
validation
36
LIMS Functionality
Examples using Labware™ LIMS
Configuring for Each User
38
Configuring LIMS for GxP
39
Providing SOPs
40
Labeling Samples
41
Maintaining Instruments
42
Configuring Test Components
43
Assigning Tests for Samples
44
Scheduling Tests
45
Acquiring Data
46
Capturing Data
47
Setting Result Responses
48
Retesting with Audit Trail
49
Reviewing Sample Status
50
Determining Chain of Custody
51
Reviewing Results
52
Performing Quality Control
53
Using Statistical Process Control
54
Analyzing Laboratory Operations
55
Submitting Reports
56
LIMS Functionality
Examples using LabVantage Sapphire™
Web-Based Client Portal
Source: Terry Smallmon, LabVantage
58
Process-Oriented Navigation
Source: Terry Smallmon, LabVantage
59
Automated 2D Gel Loading
Source: Terry Smallmon, LabVantage
60
System Integration
Source: Terry Smallmon, LabVantage
61
Access Image Data via LIMS
Source: Terry Smallmon, LabVantage
62
Connect Disparate
Data Sources via LIMS
Source: Terry Smallmon, LabVantage
63
Link Results to
Database via LIMS
Source: Terry Smallmon, LabVantage
64
Link Multiple Search Engines
to Database via LIMS
Source: Terry Smallmon, LabVantage
65
Link Visualization Tools
via LIMS
Source: Terry Smallmon, LabVantage
66
Automate Workflow via LIMS
Source: Terry Smallmon, LabVantage
67
Questions &
Comments