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BIOTECH 7005 - Bioinformatics and Systems Modelling

North Terrace Campus - Semester 2 - 2024

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.

  • General Course Information
    Course Details
    Course Code BIOTECH 7005
    Course Bioinformatics and Systems Modelling
    Coordinating Unit Molec & Biomedical Science
    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
    Assessment Assessments practical based and include a major project.
    Course Staff

    Course Coordinator: Professor David Adelson

    Course Timetable

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

  • Learning Outcomes
    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.

    8

    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
  • Learning Resources
    Online Learning
    MyUni 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
    Assignments
  • Learning & Teaching Activities
    Learning & Teaching Modes

    Lectures: 

    Tutorials: 

    Practicals: 

    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
    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
    * Optional teaching week
  • Assessment

    The University's policy on Assessment for Coursework Programs is based on the following four principles:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. Assessment must maintain academic standards.

    Assessment Summary
    Assessment Task Task Type Due Weighting Hurdle
    (Yes or No)
    Learning Outcome
    Quizzes/Tests in Practicals Formative and Summative

    Online quiz, usually once a week in practical

    10% No 1 - 9
    Practical Tasks Formative and Summative Usually two weeks after practical 60% No 1 - 9
    Final Project and Report Summative Last day of exam period 30% No 1- 9
    Assessment Detail
    Quizzes/tests in practical (6x: total of 10% )

    Practical tasks (6x: 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 tutorial. The assessment tasks will require a written report that may include, replies to specific assessment questions, scripts used to carry out analyses and output from analyses.

    Final project and report (total of 30%)
    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

    Late 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
  • 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.

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