BIOINF 7150 - Genomics Applications

North Terrace Campus - Semester 1 - 2022

This course teaches the underlying theory and skills for design and analysis of genome sequencing/resequencing experiments and datasets. This will include variant calling and 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/variant calling software, R Studio and Bash scripting and/or a compiled programming language in the context of an HPC environment.

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Course Details

Course Code	BIOINF 7150
Course	Genomics 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, Master of Biotechnology (Biomedical) and Master of Biotechnology (Biomedical) (Advanced) students only
Assessment	Practical tasks and project submission

Course Staff

Course Coordinator: Professor David Adelson

Course Timetable

The full timetable of all activities for this course can be accessed from Course Planner.

Course Learning Outcomes

1. 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 genomic analyses.
4. Implement approaches to ensure reproducibility of a research analysis.
5. Use and communicate statistical concepts to establish and communicate the reliability of genomic analyses.
6. Employ effective techniques to communicate complex research results to a non-specialist audience.
7. Produce a comprehensive analytical report on a genomics 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 Modes

Practicals are supported by lectures that build students student’s understanding of the details of performing complete genomics analysis pipelines. An integrative project and associated report preparation will help develop students’ capacity to perform complex genomics analyses and communicate analytical results to others in an effective way.

Workload

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 practical aspects of conducting genomics 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 genomics 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 Summary

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 Detail

Practical 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 genomics 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.

Submission

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.

Course Grading

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.

Student Feedback

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.
Student Support
Policies & Guidelines
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Authorised by:	Deputy Vice-Chancellor and Vice-President (Academic)
Maintained by:	Course Outlines Administrator