1. Systematically review literature to justify the rationale for the research or innovation process.
2. Discuss the stages of the research and innovation process from conceptualisation to dissemination in context of bioinformatics.
3. Discusses the technical limitations and the underlying biological and experimental assumptions that impact on data quality.
4. Justify the rationale for requirements for responsible, legal or ethical access and use of biological data, including general data protection (GDPR) considerations, identifiable personal genomic & healthcare data, and geographic biodiversityrelated data concerns.
5. Design a justified hypothesis, plan and a research process to test a hypothesis from conception to completion/archiving in accordance with ethical and research governance regulations, drawing on expert advice where necessary.
Practical Skills
6. Design, plan and undertake a research project to test a hypothesis from conception to completion/archiving in accordance with ethical and research governance regulations, drawing on expert advice where necessary and involving patients and service users.
7. Define the required metadata collected for specific datatypes and analytical approaches.
8. Make use of suitable programming languages and/or workflow tools to automate data handling tasks.
9. Maintain a working knowledge of a range of public data repositories for biological data.
10. Carry out data pre-processing and quality control (QC) to prepare datasets for bioinformatics analysis.
11. Determine the best method for bioinformatics analysis, including the selection of statistical tests, considering the research question and limitations of the experimental design.
12. Obtain data sets from private and/or public resources – considering any legal, privacy or ethical aspects of data use.
13. Carry out the analysis of biological data using appropriate programmatic methods, statistical and other quantitative and data integration approaches – and visualise results.
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14. Build and test analytical pipelines, or write and test new algorithms as necessary for the analysis of biological data.
15. Document all data processing, analysis and implementation of methods in accordance with good scientific practices and industry requirements for regulatory process and IP.
16. Interpret the results of bioinformatics analysis in the context of the experimental design and, where necessary, in a broader biological context through integration with complementary (often public) data.
17. Manage their own time through preparation and prioritisation, time management and responsiveness to change.
18. Perseveres, shows integrity, takes responsibility, addresses areas of concern, and lead own project.
19. Communicate in writing a detailed report of the research project and outcome that conforms to the format of a typical scientific dissertation.