Open Source Software in Life Science Research
Practical Solutions to Common Challenges in the Pharmaceutical Industry and Beyond
Published on 31. October 2012
Book
Hardback
582 pages
978-1-907568-97-8 (ISBN)
Description
The free/open source approach has grown from a minor activity to become a significant producer of robust, task-orientated software for a wide variety of situations and applications. To life science informatics groups, these systems present an appealing proposition - high quality software at a very attractive price. Open source software in life science research considers how industry and applied research groups have embraced these resources, discussing practical implementations that address real-world business problems.The book is divided into four parts. Part one looks at laboratory data management and chemical informatics, covering software such as Bioclipse, OpenTox, ImageJ and KNIME. In part two, the focus turns to genomics and bioinformatics tools, with chapters examining GenomicsTools and EBI Atlas software, as well as the practicalities of setting up an 'omics' platform and managing large volumes of data. Chapters in part three examine information and knowledge management, covering a range of topics including software for web-based collaboration, open source search and visualisation technologies for scientific business applications, and specific software such as DesignTracker and Utopia Documents. Part four looks at semantic technologies such as Semantic MediaWiki, TripleMap and Chem2Bio2RDF, before part five examines clinical analytics, and validation and regulatory compliance of free/open source software. Finally, the book concludes by looking at future perspectives and the economics and free/open source software in industry.
More details
Series
Language
English
Place of publication
Cambridge
United Kingdom
Publishing group
Elsevier Science & Technology
Target group
Professional and scholarly
Dimensions
Height: 234 mm
Width: 156 mm
Weight
1030 gr
ISBN-13
978-1-907568-97-8 (9781907568978)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Open Source Software in Life Science Research
Practical Solutions to Common Challenges in the Pharmaceutical Industry and Beyond
E-Book
10/2012
Woodhead Publishing Ltd
€230.00
Available for download
Persons
Lee Harland is currently leading the information engineering group at Pfizer - a group tasked with developing cutting edge software that helps scientists use internal and external information more effectively. He is also leading member of the pharma-industry pre-competitive group, the Pistoia Alliance, and has 13 years' experience in bioinformatics, software development and information science within major Pharma. Mark Forster is currently a senior information domain specialist within the Syngenta R&D Information Systems (RDIS) group, supporting R&D scientists in the fields of small molecule discovery and development, plant breeding and biotechnology. He has 15 years of industrial experience in scientific software development, deployment and support in the US and the UK.
Content
Dedication
List of figures and tables
Foreword
About the editors
About the contributors
Introduction
Chapter 1: Building research data handling systems with open source tools
Abstract:
1.1 Introduction
1.2 Legacy
1.3 Ambition
1.4 Path chosen
1.5 The 'ilities
1.6 Overall vision
1.7 Lessons learned
1.8 Implementation
1.9 Who uses LSP today?
1.10 Organisation
1.11 Future aspirations
Chapter 2: Interactive predictive toxicology with Bioclipse and OpenTox
Abstract:
2.1 Introduction
2.2 Basic Bioclipse-OpenTox interaction examples
2.3 Use Case 1: Removing toxicity without interfering with pharmacology
2.4 Use Case 2: Toxicity prediction on compound collections
2.5 Discussion
2.6 Availability
Chapter 3: Utilizing open source software to facilitate communication of chemistry at RSC
Abstract:
3.1 Introduction
3.2 Project Prospect and open ontologies
3.3 ChemSpider
3.4 ChemDraw Digester
3.5 Learn Chemistry Wiki
3.6 Conclusion
3.7 Acknowledgments
Chapter 4: Open source software for mass spectrometry and metabolomics
Abstract:
4.1 Introduction
4.2 A short mass spectrometry primer
4.3 Metabolomics and metabonomics
4.4 Data types
4.5 Metabolomics data processing
4.6 Metabolomics data processing using the open source workflow engine, KNIME
4.7 Open source software for multivariate analysis
4.8 Performing PCA on metabolomics data in R/KNIME
4.9 Other open source packages
4.10 Perspective
4.11 Acknowledgments
Chapter 5: Open source software for image processing and analysis: picture this with ImageJ
Abstract:
5.1 Introduction
5.2 ImageJ
5.3 ImageJ macros: an overview
5.4 Graphical user interface
5.5 Industrial applications of image analysis
5.6 Summary
Chapter 6: Integrated data analysis with KNIME
Abstract:
6.1 The KNIME platform
6.2 The KNIME success story
6.3 Benefits of 'professional open source'
6.4 Application examples
6.5 Conclusion and outlook
6.6 Acknowledgments
Chapter 7: Investigation-Study-Assay, a toolkit for standardizing data capture and sharing
Abstract:
7.1 The growing need for content curation in industry
7.2 The BioSharing initiative: cooperating standards needed
7.3 The ISA framework - principles for progress
7.4 Lessons learned
7.5 Acknowledgments
Chapter 8: GenomicTools: an open source platform for developing high-throughput analytics in genomics
Abstract:
8.1 Introduction
8.2 Data types
8.3 Tools overview
8.4 C++ API for developers
8.5 Case study: a simple ChIP-seq pipeline
8.6 Performance
8.7 Conclusion
8.8 Resources
Chapter 9: Creating an in-house aEUR (TM)omics data portal using EBI Atlas software
Abstract:
9.1 Introduction
9.2 Leveraging 'omics data for drug discovery
9.3 The EBI Atlas software
9.4 Deploying Atlas in the enterprise
9.5 Conclusion and learnings
9.6 Acknowledgments
Chapter 10: Setting up an aEUR (TM)omics platform in a small biotech
Abstract:
10.1 Introduction
10.2 General changes over time
10.3 The hardware solution
10.4 Maintenance of the system
10.5 Backups
10.6 Keeping up-to-date
10.7 Disaster recovery
10.8 Personnel skill sets
10.9 Conclusion
10.10 Acknowledgements
Chapter 11: Squeezing big data into a small organisation
Abstract:
11.1 Introduction
11.2 Our service and its goals
11.3 Manage the data: relieving the burden of data-handling
11.4 Organising the data
11.5 Standardising to your requirements
11.6 Analysing the data: helping users work with their own data
11.7 Helping biologists to stick to the rules
11.8 Running programs
11.9 Helping the user to understand the details
11.10 Summary
Chapter 12: Design Tracker: an easy to use and flexible hypothesis tracking system to aid project team working
Abstract:
12.1 Overview
12.2 Methods
12.3 Technical overview
12.4 Infrastructure
12.5 Review
12.6 Acknowledgements
Chapter 13: Free and open source software for web-based collaboration
Abstract:
13.1 Introduction
13.2 Application of the FLOSS assessment framework
13.3 Conclusion
13.4 Acknowledgements
Chapter 14: Developing scientific business applications using open source search and visualisation technologies
Abstract:
14.1 A changing attitude
14.2 The need to make sense of large amounts of data
14.3 Open source search technologies
14.4 Creating the foundation layer
14.5 Visualisation technologies
14.6 Prefuse visualisation toolkit
14.7 Business applications
14.8 Other applications
14.9 Challenges and future developments
14.10 Reflections
14.11 Thanks and Acknowledgements
Chapter 15: Utopia Documents: transforming how industrial scientists interact with the scientific literature
Abstract:
15.1 Utopia Documents in industry
15.2 Enabling collaboration
15.3 Sharing, while playing by the rules
15.4 History and future of Utopia Documents
Chapter 16: Semantic MediaWiki in applied life science and industry: building an Enterprise Encyclopaedia
Abstract:
16.1 Introduction
16.2 Wiki-based Enterprise Encyclopaedia
16.3 Semantic MediaWiki
16.4 Conclusion and future directions
16.5 Acknowledgements
Chapter 17: Building disease and target knowledge with Semantic MediaWiki
Abstract:
17.1 The Targetpedia
17.2 The Disease Knowledge Workbench (DKWB)
17.3 Conclusion
17.4 Acknowledgements
Chapter 18: Chem2Bio2RDF: a semantic resource for systems chemical biology and drug discovery
Abstract:
18.1 The need for integrated, semantic resources in drug discovery
18.2 The Semantic Web in drug discovery
18.3 Implementation challenges
18.4 Chem2Bio2RDF architecture
18.5 Tools and methodologies that use Chem2Bio2RDF
18.6 Conclusions
Chapter 19: TripleMap: a web-based semantic knowledge discovery and collaboration application for biomedical research
Abstract:
19.1 The challenge of Big Data
19.2 Semantic technologies
19.3 Semantic technologies overview
19.4 The design and features of TripleMap
19.5 TripleMap Generated Entity Master ('GEM') semantic data core
19.6 TripleMap semantic search interface
19.7 TripleMap collaborative, dynamic knowledge maps
19.8 Comparison and integration with third-party systems
19.9 Conclusions
Chapter 20: Extreme scale clinical analytics with open source software
Abstract:
20.1 Introduction
20.2 Interoperability
20.3 Mirth
20.4 Mule ESB
20.5 Unified Medical Language System (UMLS)
20.6 Open source databases
20.7 Analytics
20.8 Final architectural overview
Chapter 21: Validation and regulatory compliance of free/open source software
Abstract:
21.1 Introduction
21.2 The need to validate open source applications
21.3 Who should validate open source software?
21.4 Validation planning
21.5 Risk management and open source software
21.6 Key validation activities
21.7 Ongoing validation and compliance
21.8 Conclusions
Chapter 22: The economics of free/open source software in industry
Abstract:
22.1 Introduction
22.2 Background
22.3 Open source innovation
22.4 Open source software in the pharmaceutical industry
22.5 Open source as a catalyst for pre-competitive collaboration in the pharmaceutical industry
22.6 The Pistoia Alliance Sequence Services Project
22.7 Conclusion
Index
List of figures and tables
Foreword
About the editors
About the contributors
Introduction
Chapter 1: Building research data handling systems with open source tools
Abstract:
1.1 Introduction
1.2 Legacy
1.3 Ambition
1.4 Path chosen
1.5 The 'ilities
1.6 Overall vision
1.7 Lessons learned
1.8 Implementation
1.9 Who uses LSP today?
1.10 Organisation
1.11 Future aspirations
Chapter 2: Interactive predictive toxicology with Bioclipse and OpenTox
Abstract:
2.1 Introduction
2.2 Basic Bioclipse-OpenTox interaction examples
2.3 Use Case 1: Removing toxicity without interfering with pharmacology
2.4 Use Case 2: Toxicity prediction on compound collections
2.5 Discussion
2.6 Availability
Chapter 3: Utilizing open source software to facilitate communication of chemistry at RSC
Abstract:
3.1 Introduction
3.2 Project Prospect and open ontologies
3.3 ChemSpider
3.4 ChemDraw Digester
3.5 Learn Chemistry Wiki
3.6 Conclusion
3.7 Acknowledgments
Chapter 4: Open source software for mass spectrometry and metabolomics
Abstract:
4.1 Introduction
4.2 A short mass spectrometry primer
4.3 Metabolomics and metabonomics
4.4 Data types
4.5 Metabolomics data processing
4.6 Metabolomics data processing using the open source workflow engine, KNIME
4.7 Open source software for multivariate analysis
4.8 Performing PCA on metabolomics data in R/KNIME
4.9 Other open source packages
4.10 Perspective
4.11 Acknowledgments
Chapter 5: Open source software for image processing and analysis: picture this with ImageJ
Abstract:
5.1 Introduction
5.2 ImageJ
5.3 ImageJ macros: an overview
5.4 Graphical user interface
5.5 Industrial applications of image analysis
5.6 Summary
Chapter 6: Integrated data analysis with KNIME
Abstract:
6.1 The KNIME platform
6.2 The KNIME success story
6.3 Benefits of 'professional open source'
6.4 Application examples
6.5 Conclusion and outlook
6.6 Acknowledgments
Chapter 7: Investigation-Study-Assay, a toolkit for standardizing data capture and sharing
Abstract:
7.1 The growing need for content curation in industry
7.2 The BioSharing initiative: cooperating standards needed
7.3 The ISA framework - principles for progress
7.4 Lessons learned
7.5 Acknowledgments
Chapter 8: GenomicTools: an open source platform for developing high-throughput analytics in genomics
Abstract:
8.1 Introduction
8.2 Data types
8.3 Tools overview
8.4 C++ API for developers
8.5 Case study: a simple ChIP-seq pipeline
8.6 Performance
8.7 Conclusion
8.8 Resources
Chapter 9: Creating an in-house aEUR (TM)omics data portal using EBI Atlas software
Abstract:
9.1 Introduction
9.2 Leveraging 'omics data for drug discovery
9.3 The EBI Atlas software
9.4 Deploying Atlas in the enterprise
9.5 Conclusion and learnings
9.6 Acknowledgments
Chapter 10: Setting up an aEUR (TM)omics platform in a small biotech
Abstract:
10.1 Introduction
10.2 General changes over time
10.3 The hardware solution
10.4 Maintenance of the system
10.5 Backups
10.6 Keeping up-to-date
10.7 Disaster recovery
10.8 Personnel skill sets
10.9 Conclusion
10.10 Acknowledgements
Chapter 11: Squeezing big data into a small organisation
Abstract:
11.1 Introduction
11.2 Our service and its goals
11.3 Manage the data: relieving the burden of data-handling
11.4 Organising the data
11.5 Standardising to your requirements
11.6 Analysing the data: helping users work with their own data
11.7 Helping biologists to stick to the rules
11.8 Running programs
11.9 Helping the user to understand the details
11.10 Summary
Chapter 12: Design Tracker: an easy to use and flexible hypothesis tracking system to aid project team working
Abstract:
12.1 Overview
12.2 Methods
12.3 Technical overview
12.4 Infrastructure
12.5 Review
12.6 Acknowledgements
Chapter 13: Free and open source software for web-based collaboration
Abstract:
13.1 Introduction
13.2 Application of the FLOSS assessment framework
13.3 Conclusion
13.4 Acknowledgements
Chapter 14: Developing scientific business applications using open source search and visualisation technologies
Abstract:
14.1 A changing attitude
14.2 The need to make sense of large amounts of data
14.3 Open source search technologies
14.4 Creating the foundation layer
14.5 Visualisation technologies
14.6 Prefuse visualisation toolkit
14.7 Business applications
14.8 Other applications
14.9 Challenges and future developments
14.10 Reflections
14.11 Thanks and Acknowledgements
Chapter 15: Utopia Documents: transforming how industrial scientists interact with the scientific literature
Abstract:
15.1 Utopia Documents in industry
15.2 Enabling collaboration
15.3 Sharing, while playing by the rules
15.4 History and future of Utopia Documents
Chapter 16: Semantic MediaWiki in applied life science and industry: building an Enterprise Encyclopaedia
Abstract:
16.1 Introduction
16.2 Wiki-based Enterprise Encyclopaedia
16.3 Semantic MediaWiki
16.4 Conclusion and future directions
16.5 Acknowledgements
Chapter 17: Building disease and target knowledge with Semantic MediaWiki
Abstract:
17.1 The Targetpedia
17.2 The Disease Knowledge Workbench (DKWB)
17.3 Conclusion
17.4 Acknowledgements
Chapter 18: Chem2Bio2RDF: a semantic resource for systems chemical biology and drug discovery
Abstract:
18.1 The need for integrated, semantic resources in drug discovery
18.2 The Semantic Web in drug discovery
18.3 Implementation challenges
18.4 Chem2Bio2RDF architecture
18.5 Tools and methodologies that use Chem2Bio2RDF
18.6 Conclusions
Chapter 19: TripleMap: a web-based semantic knowledge discovery and collaboration application for biomedical research
Abstract:
19.1 The challenge of Big Data
19.2 Semantic technologies
19.3 Semantic technologies overview
19.4 The design and features of TripleMap
19.5 TripleMap Generated Entity Master ('GEM') semantic data core
19.6 TripleMap semantic search interface
19.7 TripleMap collaborative, dynamic knowledge maps
19.8 Comparison and integration with third-party systems
19.9 Conclusions
Chapter 20: Extreme scale clinical analytics with open source software
Abstract:
20.1 Introduction
20.2 Interoperability
20.3 Mirth
20.4 Mule ESB
20.5 Unified Medical Language System (UMLS)
20.6 Open source databases
20.7 Analytics
20.8 Final architectural overview
Chapter 21: Validation and regulatory compliance of free/open source software
Abstract:
21.1 Introduction
21.2 The need to validate open source applications
21.3 Who should validate open source software?
21.4 Validation planning
21.5 Risk management and open source software
21.6 Key validation activities
21.7 Ongoing validation and compliance
21.8 Conclusions
Chapter 22: The economics of free/open source software in industry
Abstract:
22.1 Introduction
22.2 Background
22.3 Open source innovation
22.4 Open source software in the pharmaceutical industry
22.5 Open source as a catalyst for pre-competitive collaboration in the pharmaceutical industry
22.6 The Pistoia Alliance Sequence Services Project
22.7 Conclusion
Index