BIOTECH 7005 - Bioinformatics and Systems Modelling

North Terrace Campus - Semester 2 - 2019

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.

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Course Details

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
Assessment	Tutorial presentation & participation, practical assessment, exam

Course Staff

Course Coordinator: Dr Dan Kortschak

Course Timetable

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

Course Learning Outcomes

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.

最新糖心Vlog 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

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

The 最新糖心Vlog'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 Summary

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%

Assessment Detail

Practical write-ups	e.g. Formative or Summative	60% (~6% each x ~10 practicals)
Exam	e.g. Formative and Summative	40%

Submission

Late 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 .

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Authorised by:	Deputy Vice-Chancellor and Vice-President (Academic)
Maintained by:	Course Outlines Administrator

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