BioInformatics

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 4th 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.

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.

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

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.

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.

Part A: DNA and genes dynamics (by Liu Jianjun):
(1) Dynamics of DNA (Mutation: types, mechanisms, functional impact; Recombination; Transposable elements). (2) Genome Sequencing Analysis & Annotation (Sanger sequencing, high-throughput sequencing, strategies for genome sequencing, Human Genome Project). (3) Transcription Regulation & Transcriptomes (Regulation of gene activity, transcription factors, regulation by chromatin structure, long-range regulation). (4) Comparative Genomics and Human/Population Genetics).

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.

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.

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.

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.

It is imperative for computer scientists to maintain relevance to real-world biology and biomedical problems. It is equally imperative for biologists and clinical researchers in the post-genomic era to be highly competent users of basic analytical methods of computational genomics and publicly available bioinformatics tools. It is no less important for all camps to be aware of trend-setting current developments in genomics. BI6190 is designed to address all three issues. Students will catch up with the latest developments in genomics: statistical genomics, interactive data analysis, network analysis, new sequencing technologies, paradigm shifts in our understanding of gene and genome structure and transcription regulation, the micro and macro RNA world, systems biology of cancer, biomarker discovery and genetic disease prediction. Students will apply their programming and analytical skills to solving actual genome biology and medicine problems, and learn how to obtain answers to key biological and biomedical questions in-silico through skilful use of computational genomic resources and in-deep data analysis.

BI6192 Integrative Bioinformatics, Data Analysis and Models in Genomics
AUs: 3
Prerequisites: BI6101, BI6102 and BI6104
Semester 2

With the rapid development of the field, we need to introduce more relevant and current electives to enrich the M.Sc. BI program with real-world complexity of contemporary genomics & proteomics datasets, analytical and computational methods. The course is proposed and will be taught by our adjunct staff at A*Star Bioinformatics Institute (BII) for the purpose of complementing and strengthening our MSc BI programmes.

BI6193 Current Computational Methods and Databases for Protein Sequence and Structure Analysis
AUs: 3
Prerequisites: BI6101 and BI6102
Semester 2

Bioinformatics is a fast moving field and this course will expose students to current trends and state-of-the-art computational biology methods.

This course is proposed and will be taught by our adjunct staff at A*Star Bioinformatics Institute (BII) for the purpose of complementing and strengthening our MSc BI programmes.

BI6193 will complement BI6192 (genomics) by focusing on applications at the proteomic level, from protein sequences to protein 3D structures.

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.

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.

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.

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.

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

TIMETABLE

Academic Year 2013/2014 - Semester 1, from August to December 2013

Course	Day	Time	Venue	Staff
BI6102 – Introductory Bioinformatics	Wednesday	6.30pm – 9.30pm	LT 19	Kwoh Chee Keong
BI6104 – Biostatistics	Tuesday	6.30pm – 9.30pm	LF-LT	Ho Shen-Shyang
BI6106 - Algorithms for Bioinformatics	Thursday	6.30pm – 9.30pm	LT 10	Jagath Rajapakse
CI6205 - Database Systems	Thursday	1.30pm – 5.00pm	LT 11	Xiao Xiaokui

Graduate

Doctor of Philosophy (Ph.D.) & Master of Engineering (M.Eng.)

Master of Science (MSc)

Admissions & Programmes

Important Information for Students

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

BI6121	High Performance Computing for Bioinformatics
BI6122	Biological Systems Modelling
BI6123	Methods and Tools of Proteomics
BI6129	Directed Reading
BI6192	Integrative Bioinformatics, Data Analysis and Models in Genomics
Bi6205	Current Computational Methods and Databases for Protein Sequence and Structure Analysis
CI6205	Database Systems