Note: This unit is an archived version! See Overview tab for delivered versions.
COMP5456: Introduction to Bioinformatics (2016 - Summer Main)
| Unit: | COMP5456: Introduction to Bioinformatics [not running] (6 CP) |
| Mode: | Normal-Day |
| On Offer: | Yes |
| Level: | Postgraduate |
| Faculty/School: | School of Computer Science |
| Unit Coordinator/s: |
A/Prof Charleston, Michael
|
| Session options: | Summer Main |
| Versions for this Unit: | |
| Site(s) for this Unit: |
| Campus: | Camperdown/Darlington |
| Pre-Requisites: | None. |
| Prohibitions: | COMP3456. |
| Brief Handbook Description: | This unit brings together a wide range of skills that are routinely practised in bioinformatics, from the ‘hard’ subjects of mathematics, statistics and computer science, to the ‘soft’ subjects in the biological / health sciences and pharmacology. The unit covers the essentials of bioinformatics data gathering, manipulation, mining and storage that underpin bioinformatics research. It further provides additional practice in the graduate attributes of Research and Inquiry, Information Literacy and Communication through analysis of scientific research, use of large bioinformatics data sets, and writing of reports. |
| Assumed Knowledge: | Some experience with basic programming (coding) in Java, C, C++ or Perl; Some proven ability in mathematical or information sciences (as evinced in the prerequisites); Some knowledge of molecular biology either through first year BIOL papers or MBLG1001. |
| Department Permission | Department permission is required for enrollment in this session. |
| Lecturer/s: |
A/Prof Charleston, Michael
|
||||||||||||||||||||
| Timetable: | COMP5456 Timetable | ||||||||||||||||||||
| Time Commitment: |
|
||||||||||||||||||||
| T&L Activities: | Lecture: Deliver and discuss course material Independent Study: individual study of material, prescribed readings and assessment work |
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 3) |
| Detailed understanding of a broad sampling of modern bioinformatics | Engineering/IT Specialisation (Level 3) |
| Study of standard algorithms in computer science and how they are applies to for Life Science | Maths/Science Methods and Tools (Level 2) |
| 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 3) |
| Students will be exposed to current software for bioinformatics in the labs | Professional Conduct (Level 3) |
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.
Design (Level 3)| Assessment Methods: |
|
||||||||||||||||||||||||||||||||||||||||||
| Assessment Description: |
Practical Exercises: Short computer-based assessments based on previous two days' course work. Quiz: written quiz covering material in first half of the course. Final Exam: Final written examination on all the course content, but with focus on that covered in the second half. |
||||||||||||||||||||||||||||||||||||||||||
| Grading: |
|
||||||||||||||||||||||||||||||||||||||||||
| 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 |
| Assessment Due: Practical 1 | |
| Assessment Due: Practical 2 | |
| Assessment Due: Quiz | |
| Week 2 | DNA mapping and brute force algorithms, the partial digest problem and the motif finding problem |
| Assessment Due: Practical 3 | |
| Assessment Due: Practical 4 | |
| 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 |
| 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 |
| Week 9 | repeat finding, BLAST & its derivatives |
| Week 10 | clustering, microarray data, hierarchical and k-means clustering, corrupted cliques cliques problem, phylogenetic estimation |
| 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 |
| Design (Level 3) | Yes | 24.17% |
| Engineering/IT Specialisation (Level 3) | Yes | 24.17% |
| Maths/Science Methods and Tools (Level 2) | Yes | 41.67% |
| Communication (Level 3) | Yes | 0% |
| Professional Conduct (Level 3) | Yes | 0% |
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.
