BIOINF 7130 - Bioinformatics Practice

North Terrace Campus - Semester 2 - 2018

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.

  • General Course Information
    Course Details
    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 Staff

    Course Coordinator: Professor David Adelson

    Course Timetable

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

  • Learning Outcomes
    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)
    Deep discipline knowledge
    • informed and infused by cutting edge research, scaffolded throughout their program of studies
    • acquired from personal interaction with research active educators, from year 1
    • accredited or validated against national or international standards (for relevant programs)
    1-6
    Critical thinking and problem solving
    • steeped in research methods and rigor
    • based on empirical evidence and the scientific approach to knowledge development
    • demonstrated through appropriate and relevant assessment
    2,3,4
    Teamwork and communication skills
    • developed from, with, and via the SGDE
    • honed through assessment and practice throughout the program of studies
    • encouraged and valued in all aspects of learning
    4,5,6
    Career and leadership readiness
    • technology savvy
    • professional and, where relevant, fully accredited
    • forward thinking and well informed
    • tested and validated by work based experiences
    1,4
    Intercultural and ethical competency
    • adept at operating in other cultures
    • comfortable with different nationalities and social contexts
    • Able to determine and contribute to desirable social outcomes
    • demonstrated by study abroad or with an understanding of indigenous knowledges
    4,5,6
    Self-awareness and emotional intelligence
    • a capacity for self-reflection and a willingness to engage in self-appraisal
    • open to objective and constructive feedback from supervisors and peers
    • able to negotiate difficult social situations, defuse conflict and engage positively in purposeful debate
    2,4
  • 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.

    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 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).
  • 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 Percentage of total assessment Hurdle Yes/No Learning Outcome Approximate timing of assessment
    Practical reports Formative & Summative

    20%

    No 1-6 Weekly
    Assignments Formative & Summative 60% No 1-6 Weeks 4,8,12
    Exam Summative 20% No 1-6 Exam Period
    Assessment Detail

    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.

    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
  • 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’s disciplinary procedures.

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