BIOINF 7160 - Transcriptomics Applications
North Terrace Campus - Semester 1 - 2022
General Course Information
Course Code BIOINF 7160 Course Transcriptomics Applications Coordinating Unit School of Biological Sciences Term Semester 1 Level Postgraduate Coursework Location/s North Terrace Campus Units 3 Contact 12 x 1 hour lectures, 12 x 4 hour practicals Available for Study Abroad and Exchange Y Restrictions Available to Graduate Certificate in Bioinformatics, Graduate Diploma in Bioinformatics and Master of Bioinformatics Course Description This course teaches the underlying theory and skills for design and analysis of transcriptome sequencing/assembly experiments and datasets. This will include differential gene expression and transcript assembly. Theoretical background will cover relevant computational, statistical, and network theory, as well as the key biological processes which are under investigation. Practical analysis will involve use of relevant assembly/expression analysis software, R Studio and Bash scripting and/or a compiled programming language in the context of an HPC environment.
Course Coordinator: Professor David Adelson
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning Outcomes1. Use modern literate programming tools such as R Studio Notebooks.
2. Analyse a biological question in order to develop a research analysis pipeline.
3. Use a variety of publicly available data resources and software tools to perform transcriptomic analyses.
4. Implement approaches to ensure reproducibility of a research analysis.
5. Use and communicate statistical concepts to establish and communicate the reliability of transcriptomic analyses.
6. Employ effective techniques to communicate complex research results to a non-specialist audience.
7. Produce a comprehensive analytical report on a transcriptomics research problem.
University Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
University Graduate Attribute Course Learning Outcome(s)
Attribute 1: Deep discipline knowledge and intellectual breadth
Graduates have comprehensive knowledge and understanding of their subject area, the ability to engage with different traditions of thought, and the ability to apply their knowledge in practice including in multi-disciplinary or multi-professional contexts.
1, 2, 3, 4, 5, 6, 7
Attribute 2: Creative and critical thinking, and problem solving
Graduates are effective problems-solvers, able to apply critical, creative and evidence-based thinking to conceive innovative responses to future challenges.
2, 4, 5, 6, 7
Attribute 3: Teamwork and communication skills
Graduates convey ideas and information effectively to a range of audiences for a variety of purposes and contribute in a positive and collaborative manner to achieving common goals.
1, 5, 6, 7
Attribute 4: Professionalism and leadership readiness
Graduates engage in professional behaviour and have the potential to be entrepreneurial and take leadership roles in their chosen occupations or careers and communities.
1, 2, 3, 4, 5, 6, 7
Attribute 5: Intercultural and ethical competency
Graduates are responsible and effective global citizens whose personal values and practices are consistent with their roles as responsible members of society.
5, 6, 7
Attribute 7: Digital capabilities
Graduates are well prepared for living, learning and working in a digital society.
1, 2, 3, 4, 5, 6, 7
Learning & Teaching Activities
Learning & Teaching ModesPracticals are supported by lectures that build students student’s understanding of the details of performing complete transcriptomics analysis pipelines. An integrative project and associated report preparation will help develop students’ capacity to perform complex transcriptomics analyses and communicate analytical results to others in an effective way.
No information currently available.
Learning Activities SummaryThe course covers practical aspects of conducting transcriptomics research analyses using contemporary tools such as the R statistical environment using R Studio, literate programming using R markdown notebooks and presenting analyses to clients and other researchers.
The course will involve a scaffolded development of techniques used to perform bioinformatics and statistical analyses of small transcriptomics datasets with supporting lectures to establish an understanding of the background theory for the practical studies.
The development of analysis techniques will culminate with a single large project that will make use of the techniques developed previously in the course. This project will require that students submit a preliminary analysis report and a full report explaining the results of the analysis at the level of executive summary, complete analytical approach and in depth biological interpretation.
The University's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
Assessment task Type of assessment Percentage of total assessment Hurdle Outcomes being assessed Timing of assessment Practical tasks Formative and summative 60 No Weeks 2-7 Final project progress Formative and summative 10 No Week 9 Final project submission Summative 30 No Week 12
Assessment Related Requirements
Assessment item with hurdle % needed or requirement to meet hurdle Is additional assessment available if student does not meet hurdle requirement? Details of additional assessment, if available Literature review 60 Yes Additional essay with similar format
Assessment DetailPractical tasks (6x: total of 60%)
Each practical will include an assessment tasks which will be dependent on the aspects of the work being performed in the practical, to be submitted at the beginning of the subsequent tutorial.
Final project and report (total of 40%)
Each student will perform a complete transcriptomics analysis on a large dataset. Initially, a pilot/exploratory/feasibility study will be performed and submitted as a final project progress report submitted for assessment. This submission will include assessment of needed computational resources, a preliminary data quality assessment and will be allow the appropriateness of the project to be safely assessed prior to undertaking the major task.
The final assessment task would be the submission of a complete analysis in the form of an executable R notebook (or other language if appropriate) including figures, code segments and natural language explanation, and with an executive summary of 300 words.
SubmissionIf an extension is not applied for, or not granted then a penalty for late submission will apply. A penalty of 10% of the value of the assignment for each calendar day that the assignment is late (i.e. weekends count as 2 days), up to a maximum of 50% of the available marks will be applied. This means that an assignment that is 5 days late or more without an approved extension can only receive a maximum of 50% of the marks available for that assignment.
Grades for your performance in this course will be awarded in accordance with the following scheme:
M10 (Coursework Mark Scheme) Grade Mark Description FNS Fail No Submission F 1-49 Fail P 50-64 Pass C 65-74 Credit D 75-84 Distinction HD 85-100 High Distinction CN Continuing NFE No Formal Examination RP Result Pending
Further details of the grades/results can be obtained from Examinations.
Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.
Final results for this course will be made available through Access Adelaide.
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