BIOINF 7130 - Bioinformatics Practice
North Terrace Campus - Semester 2 - 2022
General Course Information
Course Code BIOINF 7130 Course Bioinformatics Practice Coordinating Unit School of Biological Sciences Term Semester 2 Level Postgraduate Coursework Location/s North Terrace Campus Units 3 Contact Up to 4 hours per week Available for Study Abroad and Exchange Y Corequisites BIOTECH 7005 Assumed Knowledge Introduction to Programming or equivalent, Maths 1A/1B or equivalent. Restrictions Available to Graduate Certificate in Bioinformatics, Graduate Diploma in Bioinformatics and Master of Bioinformatics (Translational). Course Description Bioinformatics Practice extends on the theoretical basis for bioinformatics analysis of biological systems covered in the Bioinformatics and Systems Modelling course (BIOTECH 7005).
Bioinformatics practice covers the practical aspects of implementing bioinformatics analyses, evaluating approaches to analysing large biological data sets and communicating results. Topics include implementing sequence alignment and genome assembly, clustering data for dimensional reduction and visualising results.
Course Coordinator: Professor David Adelson
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning Outcomes
On successful completion of this course students should be able to:
1 Implement efficient alignment, assembly and clustering algorithms. 2 Formulate and justify appropriate choices in technology, strategy, and analysis for a range of projects involving DNA, RNA, or protein sequence data. 3 Develop pipelines of analysis tools to analyse real-world biological data sets, and show familiarity with the syntax and options required to generate meaningful interpretations. 4 Analyse an analytical approach for efficiency, robustness and correctness and explain the importance of these to non-specialist colleagues. 5 Explain common methods and applications for analysis of gene or protein expression. 6 Use data visualisation software to effectively communicate results.
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.
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.
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.
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.
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.
Attribute 7: Digital capabilities
Graduates are well prepared for living, learning and working in a digital society.
1, 2, 3, 4, 5, 6
Attribute 8: Self-awareness and emotional intelligence
Graduates are self-aware and reflective; they are flexible and resilient and have the capacity to accept and give constructive feedback; they act with integrity and take responsibility for their actions.
Learning & Teaching Activities
Learning & Teaching Modes
Practicals and self-directed teamwork projects will support and extend the theoretical material covered in the cognate course, Bioinformatics and Systems Modelling course (BIOTECH7005). Teamwork projects with individual student components will develop the students’ capacity to work in small groups and effectively devise appropriate ways to break down large problems into reasonably sized work units.
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.A student enrolled in a 3 unit course, such as this, should expect to spend, on average 12 hours per week on the studies required. This includes both the formal contact time required to the course (e.g., lectures and practicals), as well as non-contact time (e.g., reading and revision).
Learning Activities Summary
The course covers the application of advanced bioinformatics techniques including the implementation of sequence alignment and assembly and dimensionality reduction. The course will be delivered as a series of practicals which track the subjects being covered in the partner course, Bioinformatics and Systems Modelling course (BIOTECH7005).
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 Task Type Percentage of total assessment Hurdle Yes/No Learning Outcome Approximate timing of assessment Practical reports Formative & Summative
No 1-6 Weekly Assignments Formative & Summative 60% No 1-6 Weeks 4,8,12 Exam Summative 20% No 1-6 Exam Period
Practical reports (total of 20%)
Each practical will require the submission of a short written report on the topic/outcome of the practical, to be submitted at the beginning of the subsequent practical.
Assignments (3x: total of 60%)
Groups of students will be given a choice of topics in bioinformatics to select from. In groups, the students will research approaches to analysing the biological system using bioinformatics techniques. Students will together present their approach to the class during a practical session (once per student per semester) and will individually implement the approach and submit individual projects.
The breakdown for the assessment for assignments will be 15% group work comprised of a group task to research and prepare a design document for an implementation addressing the problem. 85% will be individual work comprising 70% implementation and testing of software addressing the problem and the remaining 15% will be individually presenting a talk discussing the design and approach and implementation details.
Theory exam (20%)
The final theory exam will examine all components of the course. It will consist of short answer and long answer questions. Questions will be drawn from topics and issues raised in presentations and in students’ assignments. Allowing a dynamic learning from problems faced by the students during the course.
If 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.
The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.
SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.
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This section contains links to relevant assessment-related policies and guidelines - all university policies.
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