BMS5308 · Integrative bioinformatics and multi-omic bioinformatics research

Overview

In this unit you will develop advanced skills in the integrative analysis and interpretation of multi-omic datasets. Emphasis is placed on developing robust analytical workflows, integrating evidence across multiple omics platforms, and interpreting biological significance within the context of discovery and translational research. Through structured workshops and independent inquiry, you will also develop skills needed for experimental design that supports integrative analyses and the generation of biologically meaningful insights. Using real-world datasets spanning multiple layers of molecular data, you will synthesise findings across diverse data types, justify analytical decisions, and communicate evidence-based conclusions using contemporary bioinformatic approaches appropriate for a range of multidisciplinary bioinformatics contexts.

Offerings

Assessment

The Handbook does not list a final examination among the assessment items. That is not a guarantee there is none.

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

Learning outcomes

1. Critically evaluate the strengths, limitations, and biological assumptions of multi-omic approaches for investigating complex biological systems.
2. Integrate multi-omics datasets to identify biologically meaningful relationships, conserved molecular responses, and physiologically relevant adaptations of a model system.
3. Design and justify reproducible bioinformatic workflows for the analysis and interpretation of multi-omics data, selecting appropriate analytical methods to address specific biological research questions.
4. Critically interpret and communicate complex multi-omics findings by constructing evidence-based biological conclusions and evaluating their implications for discovery and translational research.
5. Evaluate the reproducibility, methodological rigour, and scientific validity of key multi-omics studies, and justify alternative analytical strategies that improve the robustness and biological interpretation of bioinformatic investigations.

Workload

Teacher-directed learning will be an average of 6 hours per week, including an average of 2 hours per week online lecture content, and 4 hours of on-campus workshops. Student-directed learning will be 6 hours per week, bringing the total of 12 hours per week.

Student experience, not official rules. Nothing here changes what the Handbook says.