BIOINF 3005 - Transcriptomics Applications III

North Terrace Campus - Semester 1 - 2020

This course teaches the underlying theory and skills for design and analysis of transcriptome sequencing/assembly experiments and datasets. This will include differential gene expression and transcript assembly. Theoretical background will cover relevant computational, statistical, and network theory, as well as the key biological processes which are under investigation. Practical analysis will involve use of relevant assembly/expression analysis software, R Studio and Bash scripting and/or a compiled programming language in the context of an HPC environment.

  • General Course Information
    Course Details
    Course Code BIOINF 3005
    Course Transcriptomics Applications III
    Coordinating Unit School of Biological Sciences
    Term Semester 1
    Level Undergraduate
    Location/s North Terrace Campus
    Units 3
    Contact 12 x 1 hour lectures, 12 x 4 hour practicals
    Available for Study Abroad and Exchange N
    Assumed Knowledge GENETICS 2510 and BIOCHEM 2500 or BIOCHEM 2502
    Course Description This course teaches the underlying theory and skills for design and analysis of transcriptome sequencing/assembly experiments and datasets. This will include differential gene expression and transcript assembly. Theoretical background will cover relevant computational, statistical, and network theory, as well as the key biological processes which are under investigation. Practical analysis will involve use of relevant assembly/expression analysis software, R Studio and Bash scripting and/or a compiled programming language in the context of an HPC environment.
    Course Staff

    Course Coordinator: Dr Dan Kortschak

    Course Timetable

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

  • Learning Outcomes
    Course Learning Outcomes
    1. Analyse a biological question in order to develop a research analysis pipeline.
    2. Use a variety of publicly available data resources and software tools to perform transcriptomic analyses and
    3. Implement approaches to ensure reproducibility of a research analysis including the use modern literate programming tools such as R Studio Notebooks.
    4. Use and communicate statistical concepts to establish and communicate the reliability of transcriptomic analyses.
    5. Employ effective techniques to communicate research results to a non-bioinformatics- specialist audience.
    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, 4, 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
    1, 3, 4, 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
    3, 4, 5
    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, 2, 3, 4, 5
    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
    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
    5
  • Learning & Teaching Activities
    Learning & Teaching Modes
    Practicals are supported by lectures that build students student’s understanding of the details of performing complete transcriptomics analysis pipelines. Practical task and associated report preparation will help develop students’ capacity to perform transcriptomics analyses and communicate analytical results to others in an effective way.
    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 practical aspects of conducting transcriptomics research analyses using contemporary tools such as the R statistical environment using R Studio, literate programming using R markdown notebooks and presenting analyses to clients and other researchers.

    The course will involve a scaffolded development of techniques used to perform bioinformatics and statistical analyses of small transcriptomics 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 100 No 1-5 Weeks 2-12
    Assessment Detail
    Practical tasks (total of 100%)
    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 1000 – 1500 words in length.
    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.
    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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