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Open source software in life science research : practical solutions in the pharmaceutical industry and beyond /
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 a...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cambridge, UK ; Philadelphia, PA :
Woodhead Pub.,
2012.
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| Colección: | Woodhead Publishing series in biomedicine ;
no. 16. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- Cover; Open source software in life science research: Practical solutions in the pharmaceutical industry and beyond; Copyright; Dedication; Contents; List of figures and tables; Foreword; About the editors; About the contributors; Introduction; 1 Building research data handling systems with open source tools; 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; 1.12 References; 2 Interactive predictive toxicology with Bioclipse and OpenTox.
- 2.1 Introduction2.2 Basic Bioclipse-Open Tox 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; 2.7 References; 3 Utilizing open source software to facilitate communication of chemistry at RSC; 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; 3.8 References; 4 Open source software for mass spectrometry and metabolomics; 4.1 Introduction.
- 4.2 A short mass spectrometry primer4.3 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 Acknowledgements; 4.12 References; 5 Open source software for image processing and analysis: picture this with ImageJ; 5.1 Introduction; 5.2 ImageJ; 5.3 ImageJ macros: an overview; 5.4 Graphical user interface.
- 5.5 Industrial applications of image analysis5.6 Summary; 5.7 References; 6 Integrated data analysis with KNIME; 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; 6.7 References; 7 Investigation-Study-Assay, a toolkit for standardizing data capture and sharing; 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.
- 7.6 References8 GenomicTools: an open source platform for developing high-throughput analytics in genomics; 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; 8.9 References; 9 Creating an in-house 'omics data portal using EBI Atlas software; 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; 9.7 References.