Note: This unit is an archived version! See Overview tab for delivered versions.
COMP3456: Computational Methods for Life Sciences (2013 - Semester 2)
| 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
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| 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
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| Timetable: | COMP3456 Timetable | |||||||||||||||
| Time Commitment: |
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| 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)| Assessment Methods: |
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| Assessment Description: |
Quiz: Quiz 1 Quiz: Quiz 2 Assignment: Assignment 1 Final Exam: Final exam |
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| Grading: |
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| 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.
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| 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 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.
