BIOINF 3000 - Bioinformatics III

North Terrace Campus - Semester 2 - 2022

Recent technological advances have brought large-scale DNA sequencing within the reach of small companies and research laboratories, and opened the door for research and applied uses for sequencing. This course teaches analysis of these data sets, and interpretation of the significance of the patterns found therein. It also provides an understanding of the specific considerations of different sequencing technologies, as well as an understanding of the algorithms used to align, assemble, and annotate sequence data. While DNA sequencing is useful for sequencing genomes, it also has widespread applications in methods used to understand interactions, whether they be within a cell or organism (signaling, regulation, protein function) or between organisms (at the level of populations, symbioses, and communities). This course also provides an understanding of these systems.

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Course Details

Course Code	BIOINF 3000
Course	Bioinformatics III
Coordinating Unit	School of Biological Sciences
Term	Semester 2
Level	Undergraduate
Location/s	North Terrace Campus
Units	3
Contact	12 x 1 hour lectures, 12 x 2 hour tutorials, 12 x 2 hour practicals
Available for Study Abroad and Exchange	N
Assumed Knowledge	GENETICS 2510 and BIOCHEM 2500 or BIOCHEM 2502
Assessment	Practical tasks

Course Staff

Course Coordinator: Professor David Adelson

Course Timetable

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

Course Learning Outcomes

1. Perform simple alignment, assembly, and annotation algorithms for "toy" data sets.
2. Formulate and justify appropriate choices in technology, strategy, and analysis for a range of projects involving DNA, RNA, or protein sequence data.
3. Employ command line sequence analysis tools to analyze small real-world biological sequence data sets, and demonstrate familiarity with the syntax and options required to generate meaningful interpretations.
4. Discuss the contents of prokaryotic and eukaryotic genomes, the general function and origin of the main components, and considerations and difficulties in genome sequencing associated with these.
5. Demonstrate understanding of common methods and applications for analysis of gene expression.

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, 5
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, 3, 5
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, 4
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.	2, 3, 5
Attribute 7: Digital capabilities Graduates are well prepared for living, learning and working in a digital society.	1, 2, 3, 4, 5
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.	3, 5

Learning & Teaching Activities

Learning & Teaching Modes

Practicals are supported by lectures and tutorials that build students' understanding of the details of designing and performing bioinformatics analysis pipelines.

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, tutorials and practicals), as well as non-contact time (e.g., reading and revision).

Learning Activities Summary

The course covers theoretical and practical aspects of conducting bioinformatics research analyses using contemporary tools such as the R statistical environment using R Studio, literate programming using R markdown notebooks and presenting analyses.

The course will involve a scaffolded development of techniques used to perform bioinformatics and statistical analyses of small biological datasets with supporting lectures to establish an understanding of the background theory for the practical studies.

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	Approximate timing of assessment
Practical tasks	Formative and Summative	60	No	1-5	Weeks 2-12
Exam	Summative	40	No	1-5	Exam period

Assessment Detail

Practical tasks (total of 60%)
Each practical will include an assessment task which will be dependent on the aspects of the work being performed in the practical, to be submitted at the beginning of the subsequent practical. Each report will be approximately 1000-1500 words.
In order to allow appropriately scoped practicals that are within the capabilities of the students and that will allow adequate coverage of the material being taught, approximately 6 assessment tasks will comprise this component of the course.
Assessments for practical tasks will be returned to students within two weeks of completion of the task to allow students to incorporate assessor feedback into their learning and practice.

Exam (total of 40%)
The final theory exam will examine all components of the course. It will consist of short answer and long answer questions. Duration of exam is 3 hours.

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 .

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
This section contains links to relevant assessment-related policies and guidelines - all university policies.
Fraud Awareness

Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student鈥檚 disciplinary procedures.

The 成人大片 is committed to regular reviews of the courses and programs it offers to students. The 成人大片 therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.

Authorised by:	Deputy Vice-Chancellor and Vice-President (Academic)
Maintained by:	Course Outlines Administrator

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