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COMP3456: Computational Methods for Life Sciences (2013 - Semester 2)

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Unit: COMP3456: Computational Methods for Life Sciences (6 CP)
Mode: Normal-Day
On Offer: Yes
Level: Senior
Faculty/School: School of Computer Science
Unit Coordinator/s: A/Prof Charleston, Michael
Session options: Semester 2
Versions for this Unit:
Site(s) for this Unit:
Campus: Camperdown/Darlington
Pre-Requisites: (INFO1105 OR INFO1905) AND (COMP2007 OR INFO2120) AND [6 credit points from BIOL or MBLG].
Brief Handbook Description: This unit introduces the algorithmic principles driving advances in the life sciences. It discusses biological and algorithmic ideas together, linking issues in computer science and biology, and thus is suitable for students in both disciplines. Students will learn algorithm design and analysis techniques to solve practical problems in biology.
Assumed Knowledge: None.
Lecturer/s: A/Prof Charleston, Michael
Timetable: COMP3456 Timetable
Time Commitment:
# Activity Name Hours per Week Sessions per Week Weeks per Semester
1 Lecture 2.00 1 13
2 Laboratory 2.00 1 13
T&L Activities: Laboratory: Practical and theoretical exercises and discussion.

Objectives are to gain practice in algorithm development and implementation, and practical problem solving.

Attributes listed here represent the key course goals (see Course Map tab) designated for this unit. The list below describes how these attributes are developed through practice in the unit. See Learning Outcomes and Assessment tabs for details of how these attributes are assessed.

Attribute Development Method Attribute Developed
1. This unit provides additional practice in the graduate attribute of Research and Inquiry by requiring students to perform scientific investigations of their own, and by analysis of current bioinformatics research by case study. The unit will be taught by current researchers in bioinformatics and will therefore contain a component of current research. Design (Level 5)
Detailed understanding of a broad sampling of modern bioinformatics Engineering/IT Specialisation (Level 5)
This UoS will enhance students’ skill in the graduate attribute of Communication, through writing reports and documentation and by presentation of results to the class or tutors. Communication (Level 4)

For explanation of attributes and levels see Engineering & IT Graduate Outcomes Table.

Learning outcomes are the key abilities and knowledge that will be assessed in this unit. They are listed according to the course goal supported by each. See Assessment Tab for details how each outcome is assessed.

Maths/Science Methods and Tools (Level 2)
1. Significance of computational biology and its impact on the study of life on Earth
2. Understanding of a range of standard algorithms in computer science and how & where to apply them
Design (Level 5)
3. Ability to design algorithms to solve novel bioinformatics problems
Engineering/IT Specialisation (Level 5)
4. General understanding of bioinformatics data, data formats and databases
5. Ability to efficiently implement bioinformatics algorithms in computer applications
Communication (Level 4)
6. Ability in technical writing to communicate complex ideas clearly
Professional Conduct (Level 1)
7. Knowledge of available software for bioinformatics
Assessment Methods:
# Name Group Weight Due Week Outcomes
1 Quiz No 5.00 Week 4 1, 3, 4,
2 Quiz No 5.00 Week 8 1, 2, 3,
3 Assignment Yes 20.00 Week 10 2, 4, 5, 6, 7,
4 Final Exam No 70.00 Exam Period 1, 2, 3, 4,
Assessment Description: Quiz: Quiz 1

Quiz: Quiz 2

Assignment: Assignment 1

Final Exam: Final exam
Grading:
Grade Type Description
Standards Based Assessment Final grades in this unit are awarded at levels of HD for High Distinction, DI (previously D) for Distinction, CR for Credit, PS (previously P) for Pass and FA (previously F) for Fail as defined by University of Sydney Assessment Policy. Details of the Assessment Policy are available on the Policies website at http://sydney.edu.au/policies . Standards for grades in individual assessment tasks and the summative method for obtaining a final mark in the unit will be set out in a marking guide supplied by the unit coordinator.
Policies & Procedures: IMPORTANT: School policy relating to Academic Dishonesty and Plagiarism.

In assessing a piece of submitted work, the School of IT may reproduce it entirely, may provide a copy to another member of faculty, and/or to an external plagiarism checking service or in-house computer program and may also maintain a copy of the assignment for future checking purposes and/or allow an external service to do so.

Other policies

See the policies page of the faculty website at http://sydney.edu.au/engineering/student-policies/ for information regarding university policies and local provisions and procedures within the Faculty of Engineering and Information Technologies.
Prescribed Text/s: Note: Students are expected to have a personal copy of all books listed.
Online Course Content: Content available through WebCT

Note that the "Weeks" referred to in this Schedule are those of the official university semester calendar https://web.timetable.usyd.edu.au/calendar.jsp

Week Description
Week 1 introduction to molecular biology and bioinformatics
Week 2 DNA mapping and brute force algorithms, the partial digest problem and the motif finding problem
Week 3 greedy algorithms and genome rearrangement
Week 4 dynamic programming and sequence comparison, the Manhattan tourist problem, pairwise sequence alignment and longest common subsequence problem
Assessment Due: Quiz
Week 5 sequence alignment under more general models, multiple sequence alignment
Week 6 divide and conquer algorithms, pairwise sequence alignment in linear space, speedups to alignment
Week 7 graph algorithms, Euler and Hamilton graphs, shortest superstring problem and fragment assembly, sequencing by hybridization
Week 8 combinatorial pattern matching, hashing, keyword and suffix trees
Assessment Due: Quiz
Week 9 repeat finding, BLAST & its derivatives
Week 10 clustering, microarray data, hierarchical and k-means clustering, corrupted cliques cliques problem, phylogenetic estimation
Assessment Due: Assignment
Week 11 hidden Markov models, CG islands, the decoding problem & Viterbi algorithm, profile HMM alignment
Week 12 randomized algorithms, Gibbs sampler for motif finding, local search methods
Week 13 review
Exam Period Assessment Due: Final Exam

Course Relations

The following is a list of courses which have added this Unit to their structure.

Course Year(s) Offered
Bachelor of Computer Science and Technology (Computer Science) 2014 and earlier 2009, 2010, 2011, 2012, 2013
Bachelor of Computer Science and Technology (Computer Science)(Advanced) 2014 and earlier 2013
Bachelor of Computer Science and Technology (Information Systems) 2014 and earlier 2010, 2011, 2012, 2013
Bachelor of Computer Science and Technology (Information Systems)(Advanced) 2014 and earlier 2013
Aeronautical Engineering / Science 2011, 2012, 2013
Aeronautical Engineering (Space) / Science 2011, 2012, 2013
Biomedical Engineering / Science 2013, 2014
Biomedical - Information Technology Major 2013, 2014
Chemical & Biomolecular Engineering / Science 2011, 2012, 2013, 2014
Civil Engineering / Science 2011, 2012, 2013, 2014
Electrical Engineering (Bioelectronics) / Science 2011, 2012
Electrical Engineering / Science 2011, 2012, 2013, 2014
Electrical Engineering (Power) / Science 2011, 2012, 2013, 2014
Electrical Engineering (Telecommunications) / Science 2011, 2012, 2013, 2014
Biomedical /Science 2015, 2016, 2017
Chemical & Biomolecular / Science 2015
Civil / Science 2015
Electrical / Science 2015
Electrical (Power) / Science 2015
Electrical (Telecommunications) / Science 2015
Software Mid-Year 2016, 2017
Software 2015, 2016, 2017
Software / Arts 2016, 2017
Software / Commerce 2016, 2017, 2018, 2019, 2020
Software / Medical Science 2016, 2017
Software / Music Studies 2016, 2017
Software / Project Management 2016, 2017
Software / Science 2016, 2017, 2018, 2019, 2020
Software/Science (Health) 2018, 2019, 2020
Software / Law 2016, 2017, 2018, 2019, 2020
Mechanical Engineering (Biomedical) / Science 2011, 2012
Mechanical Engineering / Science 2011, 2012, 2013
Mechanical Engineering (Space) / Science 2011, 2012, 2013
Mechatronic Engineering / Science 2011, 2012, 2013
Mechatronic Engineering (Space) / Science 2011, 2012, 2013
Project Engineering and Management (Civil) / Science 2011
Software Engineering (till 2014) 2010, 2011, 2012, 2013, 2014
Software Engineering / Arts 2011, 2012, 2013, 2014
Software Engineering / Commerce 2010, 2011, 2012, 2013, 2014
Software Engineering / Medical Science 2011, 2012, 2013, 2014
Software Engineering / Project Management 2012, 2013, 2014
Software Engineering / Science 2011, 2012, 2013
Bachelor of Information Technology (Computer Science) 2014 and earlier 2009, 2010, 2011, 2012, 2013
Information Technology (Computer Science)/Arts 2012, 2013, 2014
Information Technology (Computer Science) / Commerce 2012, 2013, 2014
Information Technology (Computer Science) / Medical Science 2012, 2013, 2014
Information Technology (Computer Science) / Science 2012, 2013, 2014
Information Technology (Computer Science) / Law 2012, 2013, 2014
Bachelor of Information Technology (Information Systems) 2014 and earlier 2010, 2011, 2012, 2013
Information Technology (Information Systems)/Arts 2012
Software/Science (Medical Science Stream) 2018, 2019, 2020
Information Technology (Information Systems) / Science 2012
Flexible First Year (Stream A) / Science 2012

Course Goals

This unit contributes to the achievement of the following course goals:

Attribute Practiced Assessed
Maths/Science Methods and Tools (Level 2) No 44%
Design (Level 5) Yes 20.83%
Engineering/IT Specialisation (Level 5) Yes 27.17%
Communication (Level 4) Yes 4%
Professional Conduct (Level 1) No 4%

These goals are selected from Engineering & IT Graduate Outcomes Table which defines overall goals for courses where this unit is primarily offered. See Engineering & IT Graduate Outcomes Table for details of the attributes and levels to be developed in the course as a whole. Percentage figures alongside each course goal provide a rough indication of their relative weighting in assessment for this unit. Note that not all goals are necessarily part of assessment. Some may be more about practice activity. See Learning outcomes for details of what is assessed in relation to each goal and Assessment for details of how the outcome is assessed. See Attributes for details of practice provided for each goal.