BIOTECH 7005 - Bioinformatics and Systems Modelling

North Terrace Campus - Semester 2 - 2014

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 (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 BIOTECH 7005
    Course Bioinformatics and Systems Modelling
    Coordinating Unit School of Molecular and Biomedical Science
    Term Semester 2
    Level Postgraduate Coursework
    Location/s North Terrace Campus
    Units 3
    Contact Up to 5 hours per week
    Assumed Knowledge GENETICS 2500, BIOCHEM 2500, MICRO 2500 or equivalent
    Restrictions Available to GCBIBIOM, GDBIBIOM, MBIBIOM students only
    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 (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: Dr Antonio Focareta

    Course Timetable

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

  • 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)
    Knowledge and understanding of the content and techniques of a chosen discipline at advanced levels that are internationally recognised. 1-4, 6-9
    The ability to locate, analyse, evaluate and synthesise information from a wide variety of sources in a planned and timely manner. 1-3, 5, 6, 8, 9
    An ability to apply effective, creative and innovative solutions, both independently and cooperatively, to current and future problems. 2, 3, 8, 10
    Skills of a high order in interpersonal understanding, teamwork and communication. 2, 4, 5, 9
    A proficiency in the appropriate use of contemporary technologies. 1-3, 6-9
    A commitment to continuous learning and the capacity to maintain intellectual curiosity throughout life. 2-5, 9
    A commitment to the highest standards of professional endeavour and the ability to take a leadership role in the community. 4, 5, 8
    An awareness of ethical, social and cultural issues within a global context and their importance in the exercise of professional skills and responsibilities. 8
  • 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 Topic
    Week 1 Lecture Introduction to Bioinformatics
    Practical/Tutorial command line Linux, regular expressions
    Week 2 Lecture Alignment and Assembly
    Practical/Tutorial command line Linux, sequence assembly
    Week 3 Lecture Alignment 2: algorithms
    Practical/Tutorial alignment matrices, kmer alignment
    Week 4 Lecture Evolutionary processes
    Practical/Tutorial phylogenetic analysis theory/tools
    Week 5 Lecture Repeats and noncoding DNA
    Practical/Tutorial genomic interval data using Galaxy
    Week 6 Lecture Genomics: strategies/algorithms
    Practical/Tutorial plan a sequencing strategy, assemble a small genome
    Week 7 Lecture RNA: transcriptomes
    Practical/Tutorial map reads to a reference genome and interpret results

    Week 8 Lecture Metagenomics and populations
    Practical/Tutorial interpret metagenomic data- genomes and rRNA

    Week 9 Lecture Proteomics
    Practical/Tutorial analyse a mass spectrometry data set
    Week 10 Lecture Interactomes/metabolomes
    Practical/Tutorial work with sample ChIP-seq data
    Week 11 Lecture Tools for bioinformatics
    Practical/Tutorial introduction to scripting
    Week 12 Lecture The future- problems, solutions, technology, careers
    Practical/Tutorial
    Week 13* Lecture Optional teaching week
    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 Percentage
    of total assessment for grading purposes
    Hurdle
    (Yes/No)
    Learning Outcome
    Practical write-ups e.g. Formative or Summative



    60% (~6% each x ~10 practicals)

    Exam e.g. Formative and Summative 40%
    Assessment Detail

    No information currently available.

    Submission

    No information currently available.

    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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