Degree of MSc (Bioinformatics)

Description

This programme offers either a one-year full-time or a 2-year part-time training programme leading to an M.Sc. The programme is designed for students who have relevant scientific and technical background to upgrade themselves and stay relevant to the 4 th pillar of the Singapore economy - Biomedical Sciences. The curriculum will provide them with a skill-set for the creation of excellent, well-validated methods for solving problems in the domain of bioinformatics and related fields that will emerge as a consequence of Singapore's drive in Biomedical Sciences research. The curriculum will cover biology; significant coursework in algorithms; biostatistics, probability; data analysis and data mining; a substantial number of computer-related skills, information technology, and seminars.

Admission Requirements

Candidates should possess a relevant computer or engineering degree and basic programming skills. Preference will be given to those with honors, and relevant working or postgraduate experience.

TOEFL is required for graduates of universities with non-English medium of instruction. The TOEFL score is to be submitted with the application for admission. Applicants without TOEFL would still be eligible to apply, but they may be subjected to an interview/test if deemed necessary by the School.

Academic year

Semester dates for the academic year 2009/2010 can be found at
http://www.ntu.edu.sg/Services/Academic/graduates/Pages/graduateacademiccalendar.aspx.

Curriculum structure

Full-time Option:
The full-time MSc programme has a normal duration of one year. During this time, students must complete 30 academic units (AUs) to graduate. Lectures are normally conducted in the evenings. Laboratory or workshop sessions may also be conducted during the weekdays, in the weekday evenings, or on Saturdays during the day. The project will be undertaken during normal working hours throughout the week

The full-time programme has only the single study option available:

- Coursework & Dissertation: Students are required to take eight (8) courses (24 AUs) consisting of core courses, elective courses and a substantial project (6 AUs).

Each course is 3 AUs and has 39 contact hours consisting of lectures, tutorials, laboratory and/or workshop sessions.

The project is an individual research or development project. The project, which carries a weight of two normal courses, must be proposed/selected in the first 4 weeks of the programme and undertaken full-time for the remainder of the programme. The dissertation for the project must be submitted within 2 years from the commencement of the candidature.

Part-time Option:
The part-time programme has a normal duration of two years. During this time, students must complete 30 academic units (AUs) to graduate from the programme. 3-hour lectures are normally conducted in the evenings. Laboratory or workshop sessions and projects may also be conducted in the weekday evenings or on Saturdays during the day. Attendance at these sessions is compulsory.

In order to provide more flexibility to candidates, particularly as the majority is working full- time, the following options are available:

(a) Coursework & Dissertation: Students are required to take eight (8) courses (24 AUs) consisting of six (6) core courses and two (2) elective courses and a substantial project (6 AUs).

(b) Coursework only: Students are required to take a total of ten (10) courses (30 AUs) consisting of six (6) core courses and four (4) elective courses, one of which must be the Directed Reading module (3 AUs), BI6129: Directed Reading.

Each course is 3 AUs and has a minimum of 39 contact hours consisting of lectures, tutorials, laboratory and/or workshop sessions. Students will select the option most appropriate to their needs towards the end of their first year.

If the student selects the project option, an individual research or development project in the area of bioinformatics embedded systems is undertaken as part of their second year of study. The project, which carries a weightage of two normal courses, must start before the end of the first academic year and usually end in the second semester of the second academic year. Project selections must be made before the students go for their Semester II examinations so as to allow one full year to complete the work. The dissertation for the project must be submitted within 4 years from the commencement of the candidature.

Core Courses

These courses are the foundation courses.

BI6101	Introductory Biology
BI6102	Introductory Bioinformatics
BI6103	Computational Biology
BI6104	Biostatistics
BI6105	Advanced Biology
BI6106	Algorithms for Bioinformatics

Project

Project Dissertation

Elective Courses

BI6121	High Performance Computing for Bioinformatics
BI6122	Biological Systems Modelling
BI6123	Methods and Tools of Proteomics
BI6129	Directed Reading
BI6190	Special Topics
CI6205	Database Systems
M6525	Medical Informatics & Telemedicine

Detailed Descriptions

Core Courses

BI6101 Introductory Biology
AUs: 3
Prerequisites: None
Semester 1

With an emphasis on an integrative and systems approach to learning all about the functional basis of life, the aim of this course is to create a framework of biological knowledge essential to the study of bioinformatics. Regardless of background, this module will infuse the learner with an appreciation of life's inner workings, and perhaps allow even those familiar with biology, the opportunity to re-discover the extraordinary unity within the diversity of life. It begins with the fundamental building blocks of life, molecular and cell biology, then on through biochemistry, genetics, developmental biology and human physiology, and finally to life at the macro level in ecology and evolutionary theory.

Lectures are liberally supplemented with videos, interactive multimedia and hands-on practical sessions. The practical sessions provide the opportunity for experiential learning by illustrating the concepts taught using real-life examples. Wherever relevant, the role information technology plays in the investigation, analysis, modelling and manipulation of life will be highlighted.

BI6102 Introductory Bioinformatics
AUs: 3
Prerequisites: CSC103, CPE103/SC104 or equivalent
                       CSC102/CPE102/SC103 or equivalent
Semester 1

This course covers basic bioinformatics concepts, databases, tools and applications. Introduction: cell biology's central dogma, biological technologies for collecting and storing genomic sequence data; methods of computational biology; Genomic and proteomic resources: information networks, DNA sequence databases, cDNA libraries of expressed genes, protein sequence and structure databases, Human Genome Project; Sequence and structure analysis tools: dynamic programming for sequence alignment, pairwise and multiple alignment techniques, predication of RNA secondary structures, homology modelling for 3D protein structure, clustering and classification, visualisation techniques; Applications in genomics and proteomics: discovery of evolutionary relationships, gene hunting, EST and microarray data analysis for disease diagnoses, drug discovery, genetic engineering. Case studies and hands-on sessions are conducted during the course to prepare the participants for their individual thesis projects.

BI6103 Computational Biology
AUs: 3
Prerequisites: BI6101 and BI6102 or equivalent
Semester 2

Mathematical foundations: sets and sequences; probability theory; Bayes’ theorem; random variables; probability distributions; information theory; sequence alignment by information minimization, mutual information (MI), characterization of splice sites with MI; Probabilistic models of sequences: model selection: parameter estimation: constrained optimization; Lagrange theory; prior models; die models of sequences given data/counts; dice models for pairs/ multiple sequences; random and match models and log-odds ratios for alignment; Markov models of sequences, modeling CpG islands; protein structure prediction: GOR approaches; artificial neurons; gradient-decent learning; multilayer neural networks; back-propagation algorithm; PHD method; protein tertiary structure prediction; precision-recall trade-off; ROC curves; cross-validation; bias-variance trade-off; gene expression data analysis: hierarchical clustering; k-means algorithm, self-organizing feature maps; support vector machines; feature selection: T-test, SVM-RVE; graphical models; gene regulation networks; structure and parameter learning;; Hidden Markov models and gene structure prediction: forward algorithm; backward algorithm; Viterbi algorithm; expectation minimization (EM); Baum-Welch algorithm; Baldi-Chauvin approach; VEIL; GENESCAN; profile HMM; statistical mechanics: Boltzmann-Gibbs distribution; Metropolis-Hastings algorithm; simulated annealing; H-P lattice models of proteins; local MSA with Gibbs sampling; prediction of gene features: signal selection; recognition of translation initiation sites and transcription start sites; promoter finders; Promoter 2.0, NNPP, Promoter Inspector, Grail’s Promoter, DIANA-TIS, LVQ for TATA Recognition; Netstart; recognition of protein features: recognition of transmembrane helices; turn propensity scale for TM helices; hydrophobicity approach; TMHMM – a HMM approach; ENSEMBLE; topography prediction; subcellular localization; secretory pathway; compartments and sorting; PSORT-B; TargetP; SCL-BLAST, adaptation of protein surfaces; SubLoc; motif recognition; profile analysis; word counting method; graphical approaches.

BI6104 Biostatistics
AUs: 3
Prerequisites: CSC103, CPE103/SC104 or equivalent
Semester 1

Knowledge of biostatistical methods is fundamental to the planning, execution, and analyses of biomedical experiments. It is also required for the planning of observational studies and for mathematical modelling of biological phenomena. This core course aims to provide students with sufficient knowledge of biostatistics to handle biomedical projects. Coverage includes: Introduction to biostatistics, analyze univariate, bivariate and multivariate data; Introduction to probability and probability distributions, sampling distributions, point and interval estimations, confidence intervals; Hypothesis testing, testing hypotheses involving means and proportions, examining relationships using correlation and regression, sample size and power estimation; Concepts and methods of design of experiments, simple comparative experiments such as concepts of randomisation and blocking, factorial and fractional factorial designs, analysis of variance and multiple comparisons techniques, non-parametric techniques, multiple linear regression; Well-designed experiment can lead to efficient variable estimation, however, many data are collected without proper design, we will cover statistical learning theory for data mining and regression, and fundamental of classification using Support Vector Machines.

BI6105 Advanced Biology
AUs: 3
Prerequisites: BI6101
Semester 2

Part A: DNA and genes dynamics (by TF Chia):
DNA, genes (replication, recombination repair, genomes, gene clusters and repeats), mRNA (transcription and operons), proteins (synthesis, genetic code and localization), nucleus (transcription initiation, regulation, nucleosomes, nuclear splicing) and cells (protein trafficking, signal transduction, cell regulation, gradients and cascades).

Part B: Biochemistry (by SK Bose):
Protein function; Enzymes & enzyme catalysis; Enzyme kinetics; Regulation of enzyme activity; Metabolic pathways; Energetics of metabolism; Control of metabolic pathways; Role of membranes; Metabolic network in higher organisms; Analysis & modeling of metabolic control = doorway to bioinformatics.

BI6106 Algorithms for Bioinformatics
AUs: 3
Prerequisites: BI6102
Semester 1

This course covers key algorithms that are commonly used within bioinformatics. It includes sequence alignment: scoring matrices and global pairwise sequence alignment; algorithmic techniques for: string matching, suffix trees; sequence alignment using dynamic programming, optimisation of multiple sequence alignments, evolutionary trees, map assembly, combinatorial approaches to sequencing, and parallel processing for compute-intensive algorithms. Unsupervised learning algorithm such as K-mean, SOM will also be covered.

Project Dissertation
AUs: 6
Prerequisites: NIL
Semester 1 and 2

Each student must submit a research dissertation. The aim of the project is to offer students to develop algorithms and application in bioinformatics under the supervision of an academic staff.

Elective Courses

BI6121 High Performance Computing for Bioinformatics
AUs: 3
Prerequisites: BI6101 and BI6102 or equivalent
Semester 2

This course covers practical programming methods and skills for development of bioinformatics software, especially with high performance computing (HPC) systems. Introduction: bioinformatics data processing, algorithm design for sequence and structure analysis, programming language, bioinformatics software packages and toolkits; Infrastructure of HPC systems: client / server architecture, compute cluster, resource management system; Parallel and distributed programming: Amdahl's law, message passing interface, parallel programs for genomic sequence and structure data analysis; Imaging and visualisation: visualizing 3D protein structures, interactive 3D graphics programming. Case studies and hands-on sessions are conducted in the BioInformatics Research Centre to help the participants for their individual thesis projects.

BI6122 Biological Systems Modelling
AUs: 3
Prerequisites: BI6101 and BI6102 or equivalent
Semester 2

This course deals with engineering modeling of biological structures and systems at a molecular and cellular level, for understanding their functional processes and characterizing normal and disordered states. It includes structure of biomolecules and cell, biomolecular mechanics, cell mechanics and adhesion, biological molecular dynamics, biological graph theory, nonlinear physics of DNA, mechanics of cytoskeleton filaments, molecular basis of muscle contraction, and motility-to-peristaltic flow .

BI6123 Methods and Tools of Proteomics
AUs: 3
Prerequisites: BI6101 and BI6102 or equivalent
Semester 2

Proteomics, as a rapidly emerging field, has now established itself as a credible approach for furthering our understanding of the biology of whole organisms. Proteomics study and identify protein structure, interactions of protein/protein and protein/DNA and biology of organisms. We will further introduce the newly developed technology for the quantitative analysis of protein expression and function on a genome-wide scale.

BI6129  Directed Reading
AUs: 3
Prerequisites: NIL
Semester 1 and 2

The course aims to impart detailed knowledge of a highly specialised topic within the field of study of the MSc. The directed reading and independent research will involve an in-depth study of an advanced technology/methodology/technique and its application to the area of study, under the guidance of a faculty member. The directed reading course will be chosen in consultation with a supervisor. Admission into the course requires agreement by a proposed supervisor and submission of a proposal to the School (via the programme director) during the first two weeks of the semester in which the course will be taken.

BI6190  Special Topics: Current Issues In Genomics & Bioinformatics
AUs: 3
Prerequisites: BI6101 and BI6102 or equivalent
Co-requisites: BI6105, BI6104
Semester 2

BI6190 is designed for student to catch up with the latest developments in mentioned fields: new sequencing technologies, discovering biomolecular functions through protein sequence analysis, statistical methods for micro-array data analysis, current transcription regulation paradigms and models shifting in our understanding of gene and genome structures and functions, Monte Carlo strategies  in Statistical bioinformatics, methods in computational imaging of biological objects, 3-D molecular simulation methods and popular bioinformatics software. Students will have an opportunity to apply their programming skills to solving actual computational and biological problems, and learn how to obtain answers to key biological questions in-silico through skillful use of bioinformatics and computational tools and database resources in the public domain. BI6190 is strongly recommended as an elective course for all students pursuing an M.Sc. in Bioinformatics by coursework.

Please click here for more information.

Other Elective Courses

Other elective courses within NTU, offered by other schools such as SBS and NIE can be taken by students to tailor to their interests. However, approval must be obtained from the course director for these general electives.

CI6205 Database Systems
AUs: 3
Prerequisites: NIL
Semester 1 and/ 2

Overview of database models: relational and object-oriented database models; Relational database design: data modelling using the Entity-Relationship diagram and normalisation of relational tables; Database definition and manipulation: SQL and Query By Example; Managing database environments: database administration, transaction processing, concurrency control, client-server processing, and security. Web-based database applications developments using Web development tools.

*M6525 Medical Informatics & Telemedicine
AUs: 3
Prerequisites: NIL
Semester 2

Introduction to Medical Informatics, Introduction to Networking, Object Oriented Design and Modeling, Electronic Medical Records, Derivatives of a Computer Based Patient Record, Nursing Information Systems, Diagnostic Reporting Systems, Standards for Medical System, Terminology and Coding Systems in Medicine, Telemedicine, Medical Imaging, Decision Support, Bioinformatics, Ethics and Confidentiality.

* indicates that course is currently not offered

Seminars

There will be a series of seminars on Bioinformatics related research ethics and morality of choices and decisions regarding the recent developments in genetic engineering and medical research.

RECOMMENDED TIMETABLE (Full-Time)

Note: For a full-time candidate, it is possible to register for a maximum of 5 courses in any semester.

Semester 1 Complete the courses: BI6101 Introductory Biology BI6102 Introductory Bioinformatics BI6104 Biostatistics BI6106 Algorithms for Bioinformatics One elective Semester 2 Complete the courses: BI6103 Computational Biology BI6105 Advanced Biology, and One electives. Full Year Undertake the project and complete the project dissertation.

RECOMMENDED TIMETABLE (Part-Time)

Year 1 Semester 1 To complete the core courses: BI6101 Introductory Biology BI6102 Introductory Bioinformatics Semester 2 To complete the core courses BI6103 Computational Biology BI6105 Advanced Biology and elective BI6129 Directed Reading.

Year 2 Semester 1 To complete the core courses BI6104 Biostatistics BI6106 Algorithms for Bioinformatics Semester 2 To complete (a) the remaining elective and the project dissertation, or (b)  the remaining three electives.

TIMETABLE (Academic Year 2009/2010 Semester 1, August to December 2009)

Courses	Day	Time	Venue	Staff
BI6101 – Introductory Biology	Mon	6.30 – 9.30pm	LT 9	Tan Ter Ming Timothy+ / Salil Kumar Bose
BI6102 – Introductory Bioinformatics	Wed	6.30 – 9.30pm	LT 19	Kwoh Chee Keong+ / Li Jinyan
BI6104 – Biostatistics	Tues	6.30 – 9.30pm	LT 9	Manoranjan Dash+
BI6106 – Algorithms for Bioinformatics	Thurs	6.30 – 9.30pm	LT 9	Vitali Zagorodsnov+ / Bertil Schmidt

TIMETABLE (Academic Year 2008/2009 Semester 2, Jan to May 2009)

Courses	Day	Time	Venue	Staff
BI6103– Computational Biology	Thurs	6.30 – 9.30pm	LT 15	Jagath Rajapakse+ / Kwoh Chee Keong
BI6105 – Advanced Biology	Mon	6.30 – 9.30pm	LT 14	Salil Bose+ / Liu Jian Jun (Adj)
BI6121 - High Performance Computing for Bioinformatics	Wed	6.30 – 9.30pm	LT 15	Ong Yew Soon / Tan Ching Wai+
BI6123 - Methods and Tools of Proteomics	Tues	6.30 – 9.30pm	LT 15	Ng See Kiong / Li Xiaoli / Kolatkar Prasanna Ratnakar / Meena Kishore
BI6190 – Special Topic: Current Problems of Bioinformatics	Fri	6.30pm – 9.30pm	* BII	Frank Eisenhaber / Vladimir Kuznetsov
CI6205 - Database Systems	Tues	1.30pm – 5.00pm	LT 18	Sourav Saha Bhowmick

* BII – Bioinformatics Institute
   (Address: 30 Biopolis Street, #08-00, Matrix, Singapore 138671.  Nearest MRT Station – Buona Vista)

+     Course Co-ordinator

To view the detailed calendar, click here.

CONTACT INFORMATION

1. For further information on admission requirements, class timetable and programme curriculum, please contact the Programme's Office at:
   
   Programme Director M.Sc. (Bioinformatics)
   School of Computer Engineering, NTU
   E-mail: pd-msc-bi{at}ntu.edu.sg

2. For enquiries pertaining to application procedures, leave of absence, candidature extension, withdrawal, examination etc., please refer to the Frequently Asked Questions at
   http://www.faq-centre.com/home/ntu_gso/ or e-mail to:
   gradstudies{at}ntu.edu.sg

3. SCE Graduate Office
   Blk N4, #02A-32
   Nanyang Avenue
   Singapore 639798