Bath et al. Source Code for Biology and Medicine 2011, 6:9
http://www.scfbm.org/content/6/1/9
SOFTWARE REVIEW
Open Access
LimsPortal and BonsaiLIMS: development of a lab
information management system for translational
medicine
Timothy G Bath1, Selcuk Bozdag1, Vackar Afzal1 and Daniel Crowther2*
Abstract
Background: Laboratory Information Management Systems (LIMS) are an increasingly important part of modern
laboratory infrastructure. As typically very sophisticated software products, LIMS often require considerable resources
to select, deploy and maintain. Larger organisations may have access to specialist IT support to assist with
requirements elicitation and software customisation, however smaller groups will often have limited IT support to
perform the kind of iterative development that can resolve the difficulties that biologists often have when specifying
requirements. Translational medicine aims to accelerate the process of treatment discovery by bringing together
multiple disciplines to discover new approaches to treating disease, or novel applications of existing treatments. The
diverse set of disciplines and complexity of processing procedures involved, especially with the use of high
throughput technologies, bring difficulties in customizing a generic LIMS to provide a single system for managing
sample related data within a translational medicine research setting, especially where limited IT support is available.
Results: We have designed and developed a LIMS, BonsaiLIMS, around a very simple data model that can be easily
implemented using a variety of technologies, and can be easily extended as specific requirements dictate. A
reference implementation using Oracle 11 g database and the Python framework, Django is presented.
Conclusions: By focusing on a minimal feature set and a modular design we have been able to deploy the BonsaiLIMS
system very quickly. The benefits to our institute have been the avoidance of the prolonged implementation
timescales, budget overruns, scope creep, off-specifications and user fatigue issues that typify many enterprise software
implementations. The transition away from using local, uncontrolled records in spreadsheet and paper formats to a
centrally held, secured and backed-up database brings the immediate benefits of improved data visibility, audit and
overall data quality. The open-source availability of this software allows others to rapidly implement a LIMS which in
itself might sufficiently address user requirements. In situations where this software does not meet requirements, it can
serve to elicit more accurate specifications from end-users for a more heavyweight LIMS by acting as a demonstrable
prototype.
Background
Within the core laboratory of the Translational Medicine Research Collaboration (TMRC) [1], we routinely
profile human samples in order to identify molecular
biomarkers. We need to track clinical samples during
projects that often use multiple profiling technologies
such as Mass Spectrometry based proteomics, ELISA
immunoassays and Affymetrix profiling technologies on
* Correspondence: d.crowther@dundee.ac.uk
2
Translational Medicine Research Collaboration Institute, Pfizer Inc, Ninewells
Hospital, Dundee, DD1 9SY, UK
Full list of author information is available at the end of the article
overlapping patient samples. The tracking of primary
clinical samples and derived laboratory samples such as
purified mRNA aliquots becomes arduous as the complexity and the sample number increases. Commercial
LIMS solutions are available [2] which are not only
powerful enough to handle these experimental data sets
but are also robust and provide auditing functions to
allow experimental labs to meet regulatory requirements. However these vendor solutions also tend to be
expensive and require significant technical knowledge to
install and run. Different laboratories have very diverse
needs from a LIMS ranging from the kind of informal
© 2011 Bath et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons
Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in
any medium, provided the original work is properly cited.
Bath et al. Source Code for Biology and Medicine 2011, 6:9
http://www.scfbm.org/content/6/1/9
data capture beneficial during academic research to the
demonstrable, rigorous adherence to regulatory and governance standards mandated for drug manufacturing
and human clinical studies. This means that ‘off the
shelf’ LIMS software with generic functionality require
significant customization and tailoring to meet an individual lab’s requirements. This customization is difficult,
time-consuming and expensive to perform. It is also the
case that once a ‘generic’ LIMS has been tailored to
meet a specific lab’s working requirements the modifications made, such as modelling novel workflows, tend to
be very rigid and do not adapt well to include future lab
processes or technologies. In some cases the customisations may not even survive vendor upgrades of the software. In addition, many labs, such as those found in
academia or pre-clinical research do not require regulatory compliance but rather need a LIMS which will
allow them to manage their samples, clones or strains in
an efficient manner in order to facilitate their research.
This tracking can be more pressing when samples and
results data needs to be shared between labs and
research groups in collaborative projects. None of the
available LIMS are trivial to deploy, and in larger laboratories LIMS software implementations are as failureprone as any other large-scale enterprise software implementation project [3].
Translational Medicine aims to improve human health
by translating fundamental scientific research into practical applications and thereby bringing new products to
market it crosses the traditional clinical/preclinical
divide and often involves complex protocol development
and modification, multiple platform technologies and
generation of diverse raw and processed datasets for
analysis. The participants in translational research come
from diverse backgrounds have varying levels of computer understanding, and varying degrees of willingness to
change their working practises and adopt new software
[4]. A bench scientist for example may be accustomed
to recording a far greater level of experimental detail
than a clinician whose background is emergency patient
care, yet both can be involved in the translational
research study. As a result of the diversity of disciplines
typically found in translational medicine, there is often a
proliferation of locally stored data in electronic and
paper formats that may not be backed up and may not
be stored securely and this poses a risk to any organisation that allows it. This risk is especially pertinent
within translational medicine, where management and
integration of diverse data can be fundamental to the
discovery process. A LIMS therefore is a crucial tool in
reducing the risks associated with poor data management in the translational research laboratory.
In this paper we describe BonsaiLIMS, an open source
lightweight LIMS system which allows users to manage
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their studies and sample data though a secure web interface. This has been developed to meet the requirements
of our translational research facility and we believe it
will have wider utility. We also describe LIMSPortal, a
basic portal implementation that includes BonsaiLIMS
at its core and other modules to support security and
user administration features.
BonsaiLIMS functions as an end solution to provide
basic sample tracking capabilities and workflow-specific
extensions via the definition of new attribute-value pairs
that can be associated with a sample. It is sufficiently
simple to replace locally stored spreadsheets and notebook-based records without significant user training or
requiring changes to established working practises. The
benefits of replacing locally stored lab records with a
central database include improved backup and recovery
of data and improved reporting and export of data for
further analysis.
A useful second function of BonsaiLIMS is that rapid
deployment of a very simple/lightweight LIMS can help
lay the foundations of a transition to a commercial or
more heavy weight LIMS. Getting users out of the habit
of storing data locally, defining the data items they wish
to capture and discovering features that are liked or disliked all contribute to the understanding of requirements and evaluation criteria for future, more complex
systems. The process of implementing a LIMS system
will generally require the formalisation of the laboratory
processes that the LIMS is to support. This is especially
useful in cases where bench scientists may not have
prior LIMS experience or be in a position to express
comprehensive software requirements [5]. TMRC has a
very diverse set of lab-based working processes and
modelling all of them in the commercial LIMS system
chosen for our lab was not possible in the time available. Therefore an immediate, interim solution was
required. BonsaiLIMS uses a modern architecture and
flexible object/data model to rapidly deliver a LIMS
without the need for significant up-front business analysis, requirements gathering and workflow modelling,
while providing users with sufficient LIMS functionality
to replace ad-hoc methods of sample tracking and
secure the data being generated at the bench.
Implementation
BonsaiLIMS is implemented as a module that can be
embedded into larger portal application. The reason and
benefits of this architecture are twofold. Firstly, a module may be adapted and changed independently of the
core hosting application. Secondly, it allows for the
development of additional functionality in a layered and
modular fashion i.e. multiple custom workflows which
build upon the base module can be defined and developed in parallel without having any form of mutual
Bath et al. Source Code for Biology and Medicine 2011, 6:9
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dependence. In comparison to other LIMS systems,
where implementing custom workflows is often difficult
due to the lack of separation or definition of distinct
modules, modularity allows for an agile and responsive
development process that can more easily adapted to
meet the user requirements. The BonaiLIMS module is
itself hosted within a portal that allows for a clean
separation and integration of the required module functionality. Additional modules hosted within BonsaiLIMS
have been developed for authentication, authorisation,
security and module deployment.
BonsaiLIMS is implemented using Django, a Python
web framework that supports rapid design and development of web based applications [6]. Additional benefits
of using Django include support for module based
development, increased developer productivity due to
built on constructs that provide DRY (Don’t Repeat
Yourself) functionality and portability across multiple
platforms due to the python programming model
Figure 1 shows the BonsaiLIMS module within the
host LIMSPortal application. LIMSPortal, as part of a
Django framework will easily integrate with other 3rd
party Django applications and can be deployed wherever
there is an available python environment. LIMSportal is
a web based solution which is fully HTML and CSS
standards compliant and has been tested on Internet
Explorer 6/7/8 and Mozilla Firefox 3.x. The basic implementation is built on four object models: Project, Subject, Sample and Analysis. Figure 2 shows the objects
and the 1..n relationships between a project and its subjects, a subject and their samples and a sample and its
analyses. The data model used by BonsaiLIMS is a relational schema that reflects the object model, with flexible data handling coming from the use of name-value
pairs at the analysis level. This removes the need to
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Figure 2 BonsaiLIMS object model Project://collection of related
samples Subject://donor/source of each sample Sample://material
obtained from subject Analysis://operation or process performed on
a sample
specify in advance the types of analysis and results that
may be entered during a particular workflow.
Database Integration
The current BonsaiLIMS implementation is backed by
an Oracle 11 g instance. However, neither the portal
nor BonsaiLIMS utilises any Oracle-specific SQL commands, making them easily portable to other database
back ends. Figure 3 illustrates the logical separation of
core application functionality and RDBMS data persistence operations, showing how the BonsaiLIMS and
LIMSPortal can be deployed with any SQL-compliant
database platform. With respect to user auditing, while
a popular feature of many commercial, regulated systems we felt that maintaining user audit records, and
providing the application functionality to review and
manage audit went beyond the lightweight core functionality that we are aiming for with BonsaiLIMS. These
are all functions that can be handled very well by the
LIMS Portal
Bonsai LIMS
Django
Framework
3rd Party Django
Applications
Python Environment
Figure 1 LimsPortal and BonsaiLIMS Figure 1 depicts the
architecture of a typical Django project. BonsaiLIMS is a loosely
coupled Django application which has some dependencies on the
main framework and some 3rd party Python libraries. LIMSPortal, on
the other hand, is a host “Django project”.
Figure 3 LimsPortal and BonsaiLIMS Any relational database can
be used for persistence due to abstraction of the data access tier.
Bath et al. Source Code for Biology and Medicine 2011, 6:9
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RDBMS and database interaction tier. There is no user
export or specific backup functionality provided by the
application, as this is readily available directly from the
database itself.
Deploying LIMSPortal
LIMSPortal is deployed on an Apache [7] web server
using mod_python module. Static files are deployed on
a separate web server to increase the performance. That
server runs lighttpd [8] process to serve the files over
Internet. Step by step instructions for deployment plus
the required python code and Oracle DDL files are in
the supplementary material.
Performance Tuning
LIMSPortal utilises two main approaches for increased
performance. The first focuses on reducing the number
of requests that must be processed, and the second aims
at reducing the size of each request. To reduce the
number of requests that must be processed in their
entirety, memcached [9] is used to cache the results of
HTTP requests. To reduce the size of requests, AJAX is
used to enable partial page refreshes.
Results and Discussion
Due to its simplicity, this architecture and data model
enables the rapid deployment of a LIMS without the need
for significant preparatory work or ongoing support and
maintenance resourcing. Although the reference implementation was developed using Oracle and Django, the
concepts can be easily implemented using a variety of software development technologies. The extensibility of the
model allows future protocols and data items to be added
by end-users with no reprogramming. It provides lab data
management and helps integrate platform technologies
with very little user training or changes to scientists existing working procedures being required. The implementation presented describes a portal platform and LIMS
component that have been extended to provide additional
functionality such as LDAP integration for user administration and authentication. Although LDAP was chosen as
most appropriate for the translation medicine research
collaboration smaller experimental groups who might benefit most from BonsaiLIMS may even prefer the simpler
option of database or application authentication.
Functionality
The system is supporting the diverse workflows used by
TMRC Genomics, Immunoassay and Tissue Culture
groups. Data is being recorded centrally and ad-hoc
methods involving spreadsheets are being phased out. In
addition, by capturing lab process data in this structured
way, migration to a more heavyweight LIMS is eased
from both user and data perspectives.
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Performance
The application is stable, robust and responsive. For
enhancing data capture, GUI enhancements would
improve usability especially with regard to batch data
entry. Typical usage is that bench work is performed
and hand written notes are made in a lab notebook, and
then important results are input to LIMS. Closer inspection of this process has identified that the data entry
screens are not optimal for much more than single data
item entry or updates.
Comparison with similar software
There are many open source [10,11] and commercial
[12] LIMS systems available that demand significant
investment of time and money in order to obtain the
promised return. These tend to be feature-rich, heavyweight systems that offer generic functionality that can
be tailored to a specific labs needs, or are focused on a
very specific lab function such as Proteomics analysis or
Microarray studies [13]. However, no LIMS could be
found that combined the portal-based hosting framework and combination of modules to deliver specific
functionality that this paper describes.
Intended use & benefits
LIMSPortal achieves goal of moving bench scientists
involved in translational research away from ad-hoc data
recording and facilitates central management of lab data
along with the benefits of improved sample management
and collaboration between lab scientists.
Conclusions
LIMS are complicated systems and are not trivial to implement, especially in the translational research laboratory.
TMRC had pressing requirements for immediate, simple
lab data management and sample tracking and the LIMSPortal/BonsaiLIMS model met these requirements, evidenced by its uptake by Immunoassay, Tissue Culture and
Genomics groups. The system was operational and capturing data within 3 weeks of conception and has now been
back-populated with historical data previously held on
spreadsheets. Lab data is now held in uniform, structured
electronic format, and more readily available for analysis.
While the LIMS is feature poor in comparison to other
LIMS systems available, for example it does not track user
activity for audit purposes, it is an effective interim solution to TMRC’s lab data management and data integration
needs. The migration from a lightweight LIMS such as
BonsaiLIMS to a more powerful system is made far easier,
as stakeholders have a baseline for comparison and have
moved away from a culture of local document and data
storage. LIMSPortal and BonsaiLIMS are currently
deployed at TMRC and are currently managing data for
67 projects and 28,456 samples and aliquots.
Bath et al. Source Code for Biology and Medicine 2011, 6:9
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Availablity and Requirements
The BonsaiLIMS instance installed at TMRC contains
commercially sensitive information and is not publicly
available. However, the Python files, database schema
creation scripts, a user guide and instructions on how to
deploy can all be found in the additional materials files
submitted with this manuscript (Additional files 1, 2, 3,
4, 5) and on the BonsaiLIMS sourceforge page.
• Project name: LimsPortal
• Project home page: http://bonsailims.sourceforge.
net/
• Operating system(s): Platform independent
• Programming language: Python/Django
• Other requirements: Apache webserver, mod_python, lighttpd
• License: GNU LGPL
• Any restrictions to use by non-academics: None
Additional material
Additional file 1: Bonsai Deployment.doc Instructions how to deploy
BonsaiLIMS
Additional file 2: bonsai.zip Compressed file containing the python
source code for BonsaiLIMS
Additional file 3: BonsaiLIMS_DDL_Oracle11g.sql SQL script to
recreate oracle database schema
Additional file 4: site_media.zip Graphics required for web GUI
interface elements
Additional file 5: BonsaiLIMS user guide.doc A brief description of
some of the BonsaiLIMS functionality
Page 5 of 5
Competing interests
SB, VA and TB have no competing interests. DC is an employee of Pfizer Inc.
Received: 28 September 2010 Accepted: 13 May 2011
Published: 13 May 2011
References
1. Translational Medicine Research Collaboration. [http://news.bbc.co.uk/1/
hi/scotland/tayside_and_central/8010580.stm].
2. Ekins S: Computer applications in pharmaceutical research and
development. Wiley.58-60.
3. McDowall RD: Risk management for laboratory automation projects.
Journal of the Association for Laboratory Automation 2004, 9(2):72-86.
4. Plebani Mario: The changing scenario in laboratory medicine and the
role of laboratory professionals in translational medicine. Clinica Chimica
Acta 2008, 393(1):23-26.
5. Broad LA, Maloney TA, Subak EJ Jr: Selection of Lims for a Pharmaceutical
Research and Development Laboratory–A Case Study. Data Handling in
Science and Technology 1990, 6:315-327.
6. Reuven MLerner: At the forge: First steps with Django. Linux Journal
159:11.
7. Laurie Ben, Laurie Peter: Apache: The Definitive Guide. O’Reilly &
Associates, Inc;, 3.
8. Bogus Andre: Lighttpd. Packt Publishing; 2008.
9. Fitzpatrick Brad: Distributed caching with memcached. Linux Journal
2004(124).
10. Viksna Juris, Celms Edgars, Opmanis Martins, Podnieks Karlis,
Rucevskis Peteris, Zarins Andris, Barrett Amy, Sudeshna Guha Neogi,
Krestyaninova Maria, Mark IMcCarthy, Brazma Alvis, Ugis Sarkans: PASSIM an open source software system for managing information in
biomedical studies. BMC Bioinformatics 2007, 8:52.
11. FreeLIMS. [http://sourceforge.net/projects/freelims/], by fina.
12. Piggee Christine: LIMS and the art of MS proteomics. Anal. Chem 2008,
80(13):4801-4806.
13. Maurer Michael, Molidor Robert, Sturn Alexander, Hartler Juergen,
Hackl Hubert, Stocker Gernot, Prokesch Andreas, Scheideler Marcel,
Trajanoski Zlatko: MARS: Microarray analysis, retrieval, and storage
system. BMC Bioinformatics 2005, 6:101.
doi:10.1186/1751-0473-6-9
Cite this article as: Bath et al.: LimsPortal and BonsaiLIMS: development
of a lab information management system for translational medicine.
Source Code for Biology and Medicine 2011 6:9.
List of Abbreviations used
AJAX: Asynchronous JavaScript and XML; CSV: Comma Separated Value; GUI:
Graphical User Interface; LDAP: Lightweight Directory Access Protocol; LIMS:
Laboratory Information Management Systems; TMRC: Translational Medicine
Research Collaboration.
Acknowledgements
We thank all BonsaiLIMS users at TMRC for their feedback and suggestions for
improvement. Particular thanks go to Inger Selmer who provided us with
valuable suggestions for improving the usability of the GUI. Credit is also due to
the University of Dundee High Performance Computing group who provide
application hosting and server administration for LIMSPortal and BonsaiLIMS in
the TMRC. This work was supported by the Translational Medicine Research
Collaboration, a consortium made up of the Universities of Aberdeen, Dundee,
Edinburgh and Glasgow, the four associated NHS Health Boards (Grampian,
Tayside, Lothian and Greater Glasgow & Clyde), Scottish Enterprise and Pfizer.
Author details
Translational Medicine Research Collaboration Institute, University of
Dundee, Ninewells Hospital, Dundee, DD1 9SY, UK. 2Translational Medicine
Research Collaboration Institute, Pfizer Inc, Ninewells Hospital, Dundee, DD1
9SY, UK.
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