BIOINF 3000 - Bioinformatics III

North Terrace Campus - Semester 2 - 2021

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.

  • General Course Information
    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
    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. 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.
    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
    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)
    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, 2, 3, 5
    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, 5
    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
    1, 4
    Career and leadership readiness
    • technology savvy
    • professional and, where relevant, fully accredited
    • forward thinking and well informed
    • tested and validated by work based experiences
    2, 3, 5
    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
    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.
  • 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 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 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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