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Information management applied to bioinformatics. Appendices B, C, and D.

posted on 2019-08-16, 08:00 authored by Hiten Vyas

Appendix B. Stakeholder identification


• Raw interview data from stakeholder identification.

• Analysis of the interviews with potential stakeholders.

Appendix C: Raw interview data from Sanger Institute case study.

Appendix D: Raw interview data from AstraZeneca case study


Bioinformatics, the discipline concerned with biological information management is essential in the post-genome era, where the complexity of data processing allows for contemporaneous multi level research including that at the genome level, transcriptome level, proteome level, the metabolome level, and the integration of these -omic studies towards gaining an understanding of biology at the systems level. This research is also having a major impact on disease research and drug discovery, particularly through pharmacogenomics studies. In this study innovative resources have been generated via the use of two case studies. One was of the Research & Development Genetics (RDG) department at AstraZeneca, Alderley Park and the other was of the Pharmacogenomics Group at the Sanger Institute in Cambridge UK. In the AstraZeneca case study senior scientists were interviewed using semi-structured interviews to determine information behaviour through the study scientific workflows. Document analysis was used to generate an understanding of the underpinning concepts and fonned one of the sources of context-dependent information on which the interview questions were based. The objectives of the Sanger Institute case study were slightly different as interviews were carried out with eight scientists together with the use of participation observation, to collect data to develop a database standard for one process of their Pharmacogenomics workflow. The results indicated that AstraZeneca would benefit through upgrading their data management solutions in the laboratory and by development of resources for the storage of data from larger scale projects such as whole genome scans. These studies will also generate very large amounts of data and the analysis of these will require more sophisticated statistical methods. At the Sanger Institute a minimum information standard was reported for the manual design of primers and included in a decision making tree developed for Polymerase Chain Reactions (PCRs). This tree also illustrates problems that can be encountered when designing primers along with procedures that can be taken to address such issues



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