book

Open Source Software in Life Science Research

by L Harland, M Forster

October 2012

Beginner to intermediate

582 pages

15h

English

Woodhead Publishing

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Abstract:1.1 Introduction1.2 Legacy1.3 Ambition1.4 Path chosen1.5 The ‘ilities1.6 Overall vision1.7 Lessons learned1.8 Implementation1.9 Who uses LSP today?1.10 Organisation1.11 Future aspirations
Abstract:2.1 Introduction2.2 Basic Bioclipse-OpenTox interaction examples2.3 Use Case 1: Removing toxicity without interfering with pharmacology2.4 Use Case 2: Toxicity prediction on compound collections2.5 Discussion2.6 Availability
Abstract:3.1 Introduction3.2 Project Prospect and open ontologies3.3 ChemSpider3.4 ChemDraw Digester3.5 Learn Chemistry Wiki3.6 Conclusion3.7 Acknowledgments
Abstract:4.1 Introduction4.2 A short mass spectrometry primer4.3 Metabolomics and metabonomics4.4 Data types4.5 Metabolomics data processing4.6 Metabolomics data processing using the open source workflow engine, KNIME4.7 Open source software for multivariate analysis4.8 Performing PCA on metabolomics data in R/KNIME4.9 Other open source packages4.10 Perspective4.11 Acknowledgments
Abstract:5.1 Introduction5.2 ImageJ5.3 ImageJ macros: an overview5.4 Graphical user interface5.5 Industrial applications of image analysis5.6 Summary
Abstract:6.1 The KNIME platform6.2 The KNIME success story6.3 Benefits of 'professional open source'6.4 Application examples6.5 Conclusion and outlook6.6 Acknowledgments
Abstract:7.1 The growing need for content curation in industry7.2 The BioSharing initiative: cooperating standards needed7.3 The ISA framework – principles for progress7.4 Lessons learned7.5 Acknowledgments
Abstract:8.1 I ntroduction8.2 Data types8.3 Tools overview8.4 C++ API for developers8.5 Case study: a simple ChIP-seq pipeline8.6 Performance8.7 Conclusion8.8 Resources
Abstract:9.1 Introduction9.2 Leveraging ’omics data for drug discovery9.3 The EBI Atlas software9.4 Deploying Atlas in the enterprise9.5 Conclusion and learnings9.6 Acknowledgments
Abstract:10.1 Introduction10.2 General changes over time10.3 The hardware solution10.4 Maintenance of the system10.5 Backups10.6 Keeping up-to-date10.7 Disaster recovery10.8 Personnel skill sets10.9 Conclusion10.10 Acknowledgements
Abstract:11.1 Introduction11.2 Our service and its goals11.3 Manage the data: relieving the burden of data-handling11.4 Organising the data11.5 Standardising to your requirements11.6 Analysing the data: helping users work with their own data11.7 Helping biologists to stick to the rules11.8 Running programs11.9 Helping the user to understand the details11.10 Summary
Abstract:12.1 Overview12.2 Methods12.3 Technical overview12.4 Infrastructure12.5 Review12.6 Acknowledgements
Abstract:13.1 Introduction13.2 Application of the FLOSS assessment framework13.3 Conclusion13.4 Acknowledgements
Abstract:14.1 A changing attitude14.2 The need to make sense of large amounts of data14.3 Open source search technologies14.4 Creating the foundation layer14.5 Visualisation technologies14.6 Prefuse visualisation toolkit14.7 Business applications14.8 Other applications14.9 Challenges and future developments14.10 Reflections14.11 Thanks and Acknowledgements
Abstract:15.1 Utopia Documents in industry15.2 Enabling collaboration15.3 Sharing, while playing by the rules15.4 History and future of Utopia Documents
Abstract:16.1 Introduction16.2 Wiki-based Enterprise Encyclopaedia16.3 Semantic MediaWiki16.4 Conclusion and future directions16.5 Acknowledgements
Abstract:17.1 The Targetpedia17.2 The Disease Knowledge Workbench (DKWB)17.3 Conclusion17.4 Acknowledgements
Abstract:18.1 The need for integrated, semantic resources in drug discovery18.2 The Semantic Web in drug discovery18.3 Implementation challenges18.4 Chem2Bio2RDF architecture18.5 Tools and methodologies that use Chem2Bio2RDF18.6 Conclusions
Abstract:19.1 The challenge of Big Data19.2 Semantic technologies19.3 Semantic technologies overview19.4 The design and features of TripleMap19.5 TripleMap Generated Entity Master ('GEM') semantic data core19.6 TripleMap semantic search interface19.7 TripleMap collaborative, dynamic knowledge maps19.8 Comparison and integration with third-party systems19.9 Conclusions
Abstract:20.1 Introduction20.2 Interoperability20.3 Mirth20.4 Mule ESB20.5 Unified Medical Language System (UMLS)20.6 Open source databases20.7 Analytics20.8 Final architectural overview
Abstract:21.1 Introduction21.2 The need to validate open source applications21.3 Who should validate open source software?21.4 Validation planning21.5 Risk management and open source software21.6 Key validation activities21.7 Ongoing validation and compliance21.8 Conclusions
Abstract:22.1 Introduction22.2 Background22.3 Open source innovation22.4 Open source software in the pharmaceutical industry22.5 Open source as a catalyst for pre-competitive collaboration in the pharmaceutical industry22.6 The Pistoia Alliance Sequence Services Project22.7 Conclusion

Content preview from Open Source Software in Life Science Research

Foreword

Enoch S. Huang, Newton, Massachusetts, April 2012

Twelve years ago, I joined the pharmaceutical industry as a computational scientist working in early stage drug discovery. Back then, I felt stymied by the absence of a clear legal or IT framework for obtaining official support for using Free/Libre Open Source Software (FLOSS) within my company, much less for its distribution outside our walls. I came to realize that the underlying reason was because I do not work for a technology company wherein the establishment of such policies would be a core part of its business. Today, the situation is radically different: the corporate mindset towards these technologies has become far more accommodating, even to the point of actively recommending ...