Open Source Software in Life Science Research (eBook, ePUB)

Practical Solutions to Common Challenges in the Pharmaceutical Industry and Beyond
Redaktion: Harland, Lee; Forster, Mark

• Format: ePub

Produktbeschreibung

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

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Produktdetails

• Verlag: Elsevier Science & Techn.
• Seitenzahl: 582
• Erscheinungstermin: 31. Oktober 2012
• Englisch
• ISBN-13: 9781908818249
• Artikelnr.: 40159002

Autorenporträt

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.

Inhaltsangabe

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 â?Tomics 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 â?Tomics 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

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