BIOTECH 7005 - Bioinformatics and Systems Modelling
North Terrace Campus - Semester 2 - 2019
General Course Information
Course Code BIOTECH 7005 Course Bioinformatics and Systems Modelling Coordinating Unit School of Biological Sciences 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 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 (signalling, regulation, protein function) or between organisms (at the level of populations, symbioses, and communities). This course also provides an understanding of these systems.
Course Coordinator: Professor David Adelson
The full timetable of all activities for this course can be accessed from Course Planner.
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) 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, 6, 7, 9 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, 6, 7, 8, 9 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, 5, 8, 9 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, 6, 7, 8 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
8 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, 6, 9
Online LearningMyUni 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
Learning & Teaching Activities
Learning & Teaching Modes
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
* Optional teaching week
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
The University's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
Assessment Task Task Type Percentage
of total assessment for grading purposes
Practical write-ups e.g. Formative or Summative
60% (~6% each x ~10 practicals)
Exam e.g. Formative and Summative 40%
Practical write-ups e.g. Formative or Summative
60% (~6% each x ~10 practicals)
Exam e.g. Formative and Summative 40%
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.
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.
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