BIOTECH 7005 - Bioinformatics and Systems Modelling
North Terrace Campus - Semester 2 - 2022
General Course Information
Course Code BIOTECH 7005 Course Bioinformatics and Systems Modelling Coordinating Unit School of Biological Sciences Term Semester 2 Level Postgraduate Coursework Location/s North Terrace Campus Units 3 Contact Up to 5 hours per week Available for Study Abroad and Exchange N Assumed Knowledge GENETICS 2500, BIOCHEM 2500, MICRO 2500 or equivalent Course Description 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. This course 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 (signalling, regulation, protein function) or between organisms (at the level of populations, symbioses, and communities). This course also provides an understanding of these systems.
Course Coordinator: Professor David Adelson
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning Outcomes
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.
It is the aim of this course to provide students with an understanding of the theory and practice of bioinformatic analysis of biological sequence data. Theory includes understanding principles and pitfalls in the biology and analysis of these data, and algorithms for alignment, assembly, annotation, and phylogenetic inference. Practice includes the use of pre-existing and novel tools, and application to a wide range of real-world uses of bioinformatic analysis.
On completion of the course students should be able to:
1 Perform simple alignment, assembly, and annotation algorithms by hand 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 real-world biological sequence data sets, and demonstrate familiarity with the syntax and options required to generate meaningful interpretations. 4 Describe the roles of mutation, recombination, duplication, and selection in generating novel variants and determining their fate in organisms and populations. 5 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. 6 Demonstrate understanding of common methods and applications for analysis of gene expression. 7 Recognize the importance of using protein (or translated nucleotide) sequence data in searches for homology in evolutionary and functional relationships of genes. 8 Survey methods involving the analysis of interactions between proteins, nucleic acids, and other molecules, and their applications to biomedical and other real-world problems. 9 Discuss the considerations involved in the analysis of multi-species sequence data sets, such as microbial metagenomic and host/symbiont genome analysis.
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, 6, 7, 9
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, 6, 7, 8, 9
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, 8, 9
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, 6, 7, 8
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 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.
5, 6, 9
Online LearningMyUni is an essential resource for this course and it is important for students to login regularly to check on important course-related announcements and material.
Students will find the following on MyUni:
All lectures are recorded
All lecture notes
Learning & Teaching Activities
Learning & Teaching Modes
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
* Optional teaching week
Schedule Week Type of learning activity Week 1 Lecture Practical/Tutorial Week 2 Lecture Practical/Tutorial Week 3 Lecture Practical/Tutorial Week 4 Lecture Practical/Tutorial Week 5 Lecture Practical/Tutorial Week 6 Lecture Practical/Tutorial Week 7 Lecture Practical/Tutorial Week 8 Lecture Practical/Tutorial Week 9 Lecture Practical/Tutorial Week 10 Lecture Practical/Tutorial Week 11 Lecture Practical/Tutorial Week 12 Lecture Practical/Tutorial Week 13* Lecture Practical/Tutorial
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 for grading purposes
Practical write-ups e.g. Formative or Summative
60% (~6% each x ~10 practicals)
Exam e.g. Formative and Summative 40%
Practical write-ups e.g. Formative or Summative
60% (~6% each x ~10 practicals)
Exam e.g. Formative and Summative 40%
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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