Book 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.
- Discusses a broad range of applications from a variety of sectors
- Provides a unique perspective on work normally performed behind closed doors
- Highlights the criteria used to compare and assess different approaches to solving problems
Table of Contents
- Cover image
- Title page
- Table of Contents
- Copyright
- 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 I ntroduction
- 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 ’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 ’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