CSCI5050 Bioinformatics and Computational Biology


Course code CSCI5050
Course title Bioinformatics and Computational Biology
Course description This course introduces several core topics in bioinformatics and computational biology. Each topic will be discussed from three aspects: 1) motivation and concepts, 2) computational problems and methods, and 3) available tools and data. The topics include basics in molecular biology, high-throughput experiments and data preprocessing, sequencing and alignment, motifs and domains, ontology and functional enrichment, biological networks and data mining, secondary and tertiary structures, and other latest developments in this research area.
Unit(s) 3
Course level Postgraduate
Semester 1 or 2
Grading basis Graded
Grade Descriptors A/A-:  EXCELLENT – exceptionally good performance and far exceeding expectation in all or most of the course learning outcomes; demonstration of superior understanding of the subject matter, the ability to analyze problems and apply extensive knowledge, and skillful use of concepts and materials to derive proper solutions.
B+/B/B-:  GOOD – good performance in all course learning outcomes and exceeding expectation in some of them; demonstration of good understanding of the subject matter and the ability to use proper concepts and materials to solve most of the problems encountered.
C+/C/C-: FAIR – adequate performance and meeting expectation in all course learning outcomes; demonstration of adequate understanding of the subject matter and the ability to solve simple problems.
D+/D: MARGINAL – performance barely meets the expectation in the essential course learning outcomes; demonstration of partial understanding of the subject matter and the ability to solve simple problems.
F: FAILURE – performance does not meet the expectation in the essential course learning outcomes; demonstration of serious deficiencies and the need to retake the course.
Learning outcomes At the end of the course of studies, students will have acquired the ability to
1. explain the concepts and basic methods related to a broad range of topics in bioinformatics;
2. apply what they have learned based on hands-on experience in using existing tools and locating; processing and analyzing some available data
3. work and research in bioinformatics-related areas.
(for reference only)
Essay test or exam:30%
Recommended Reading List This course does not have any text book. Students will read some latest research papers for class discussions and course project. Here is a list of papers used before. The exact list to be used will be revised every year.
1. Flicek and Birney, Sense from Sequence Reads: Methods for Alignment and Assembly. Nature Methods 6(11s):S6-S12, (2009)
2. Ernst et al., Mapping and Analysis of Chromatin State Dynamics in Nine Human Cell Types. Nature 473(7345):43-49, (2011)
3. Lawrence et al., Detecting Subtle Sequence Signals: A Gibbs Sampling Strategy for Mult Align. Science 262(5131):208-214, (1993)
4. Marioni et al., RNA-seq: An Assessment of Technical Reproducibility and Comparison with Gene Expression Arrays.
5. Rhee et al., Use and Misuse of the Gene Ontology Annotations. Nature Reviews Genetics 9(7):509-515, (2008)
6. Jansen et al., A Bayesian Networks Approach for Predicting Protein-Protein Interactions from Genomic Data. Science (2003)


CSCIN programme learning outcomes Course mapping
Upon completion of their studies, students will be able to:  
1. identify, formulate, and solve computer science problems (K/S); T
2. design, implement, test, and evaluate a computer system, component, or algorithm to meet desired needs (K/S);
3. receive the broad education necessary to understand the impact of computer science solutions in a global and societal context (K/V); T
4. communicate effectively (S/V);
5. succeed in research or industry related to computer science (K/S/V);
6. have solid knowledge in computer science and engineering, including programming and languages, algorithms, theory, databases, etc. (K/S);
7. integrate well into and contribute to the local society and the global community related to computer science (K/S/V); T
8. practise high standard of professional ethics (V);
9. draw on and integrate knowledge from many related areas (K/S/V);
Remarks: K = Knowledge outcomes; S = Skills outcomes; V = Values and attitude outcomes; T = Teach; P = Practice; M = Measured