Course Content

The course is an introduction to scientific computation and computer programming for graduate students with no prior programming background. Students will learn how to solve scientific problems with programming, how to write a complete program that gets inputs, perform computations and gives outputs by using loops, functions, and different data types. The course covers scientific problems from different areas to be solved with Python programs, which is a popular, general-purpose, open-source language and widely used in several areas of informatics in both industry and research fields.

Course Content

This course will provide an introduction to bioinformatics. The computational techniques for mining the large amount of information produced by biological experiments such as genome sequencing, microarray technology, and other high-throughput experimental methods will be introduced. The main emphasis of the course is to provide an overview of the area and describe solutions to fundamental problems of bioinformatics such as DNA and protein sequence alignment, protein structural alignment, protein/RNA structure prediction, phylogenetic tree construction, microarray data analysis, and analysis of gene/protein networks.

Course Content

This course serves as a deficiency course for non-statisticians who are studying informatics at graduate level. Fundamentals of statistical methods and probability theory will be covered with specific examples and applications from cases in informatics and bioinformatics research. The topics offered in this course are; Counting, permutations and combinations, axioms of probability, conditional probability and independence, random variables, basic distributions of discrete and continuous random variables, functions of random variables, expectation, variance, covariance and correlation, sampling distributions, the central limit theorem, estimation and confidence intervals, bias, sufficiency, efficiency and consistency of estimators, hypothesis testing, common tests, error types. Non-parametric tests. Linear regression and ANOVA.

Course Content

This course provides an in-depth review of the publicly available software tools and biological databases. Different types of biological data will be introduced and techniques for organization of biological data will be discussed. Also, the course will cover extensive use of web- based bioinformatics environments for investigation and analysis of biological data.

Course Content

This course will introduce statistical modeling and inference techniques applied to biological problems. The course will cover standard statistical methods, such as multiple regression and principle component analysis, and more recent statistical techniques, such as maximum likelihood methods. Among the techniques covered will be Monte-Carlo-Markov chains using the Metropolis-Hastings algorithm and Gibbs sampling. In addition, the course will cover the use of statistical techniques such as Hidden Markov Models to model family of sequence and structures. Kernel methods and Support Vector Machines for computational biology will also be covered.

Course Content

Systems biology aims to study biological phenomena through the modeling of interactions and general system behavior rather than reducing to the individual parts. This course will cover the basic ideas, tools and contributions of the systems biology and biological network analysis. The subjects to be covered include: dynamics of biological networks; common motifs; network analysis, modeling and visualization methods; applications in transcription, protein interaction, metabolic and co-expression networks. The coursework involves in-class discussion of several case studies, a term project, homework assignments and exams.

Course Content

This course will cover the methods of DNA and protein sequence analysis in depth including analysis of homology, identification of motifs and domains, pair-wise and multiple alignments, and statistical significance of sequence alignments. The course will also cover sequence and motif databases such as GeneBank, SwissProt, Protiste, and Pfam.

Course Content

Next Generation Sequence Analysis and Informatics course provides an introduction to methods for next-generation nucleic acid sequencing (NGS) data analysis. Students will learn most recent high-thoughput sequencing laboratory technologies and informatics tools for data analyses. Up to date best-practice methods and road maps will be discussed in detail. Data structures and algorithms for the NGS technologies and applications including variant detection, CHIP-seq, RNA-seq, de novo assembly, and targeted sequencing will be covered. Computational frameworks and toolkits such as SAMTools, BedTools, BWA, TopHat/Cufflinks, GATK, QIIME, R, and Galaxy will be exploited using sample data. The coursework involves case studies, one term project, homework assignments and exams.

Course Content

Introduction to Pathway Bioinformatics course provides an introduction to cellular network analysis, pathway bioinformatics and systems pharmacology research. Students will learn how to construct, analyze and visualize different types of cellular pathways using available tools. Main topics include cell-signaling pathways, gene regulatory networks, data collection and integration of drug-target and drug-drug similarity networks, drug induced gene expressions signatures and other functional cellular networks in 4 major health topics: Cancer, Immunology, Neurology and Cardiovascular system. The coursework involves case studies, one term project, homework assignments and exams.

Course Content

This course aims to introduce frequently used bioinformatics tools to non-bioinformaticians and will discuss the basic concepts of bioinformatics. Recent developments in biological sciences have produced a wealth of experimental data of sequences and three-dimensional structures of biological macromolecules. With the advances of computer and informational sciences, these data and tools to analyse the data is available from a variety of public sources. The main focus of the course will be to teach how to access, handle and interpret this rapidly expanding amount of biological information at an introduction level. Practical section of the course will emphasize on how to use the computer and bioinformatics applications to aid in biological research.

Course Content

The course is an introduction to structural bioinformatics. Additionally, advanced techniques used in structural bioinformatics will be covered. In the scope of this course, students will learn to analyze, classify, simulate, predict and visualize biological molecules, mainly proteins. We cover the fundamentals of protein structures, structure determination techniques, molecular dynamics simulations, molecular docking and techniques to predict the protein interaction in atomic resolution.

Course Content

This course covers key concepts in statistical learning, specifically regression, classification, resampling methods, linear model selection, regularization; moves beyond linearity; explores tree-based methods, support vector machines, deep learning, survival analysis, unsupervised learning, and multiple testing. Course provides all these concepts and showcase them through Jupyter notebooks, which allow to run Python code. This course is comprehensive in terms of statistical learning methods it covers and focused on the applications of these methods in Python.

Course Content

The graduate seminar will provide the students an opportunity to present advanced papers in Bioinformatics. The students will read and present papers from frontier Bioinformatics conferences, such as RECOMB, ISMB, PSB, and CSB, and from top journals such as Bioinformatics, PNAS, Science, Nature, Genome Research, and Proteins. This course will be a medium for discussing recent breakthroughs and brainstorming new research ideas. Enrolment in this course for at least two semesters will be mandatory for each graduate student enrolled in the Bioinformatics program.

Course Content

Bioinformatics is an emerging scientific area. This interdisciplinary scientific domain is defined as the application of computer science and information technology to the field of molecular biology, bio-omics and biomedicine. Hence, the clear and concise understanding and analysis of the basic processes in living cells are fundamental to bioinformaticians. This course covers molecular and cellular biology topics which are designed especially for computer scientists and non-biology majors. The course provides information about biomolecules, cellular physiology, cell metabolism machinery, major signaling pathways, cell division, and pathological conditions such as cancer and neurodegenerative disorders tailored for candidate bioinformaticians and computational biologists in a compact manner. Furthermore, experimental principles of recent high-throughput technologies and molecular biology and medicine, which requires computational data analysis, are also covered.

Course Content

Program of research leading to M.S. degree, arranged between students and a faculty member. Students register to this course in all semesters starting from the beginning of their second semester while the research program or thesis writing is in progress.

Course Content

Recent advances on technology, molecular biology and high-throughput biological experiments result in data accumulation at a large scale. These data have been provided in different platforms and come from different laboratories therefore, there is a need for compilation and comprehensive analysis. The main focus of the course will be to understand the principal concepts of algorithms, mining methods and database management systems used in analyzing, clustering and storing these data from the computer science perspective for bioinformatics students. Programming assignments and presentations of major bioinformatics algorithms will emphasize on understanding and implementation of bioinformatics applications to aid in biological research. Futhermore, understanding the basic computational concepts used in data analysis will gain experience for later working in corporation with computer scientists.

Course Content

Microarrays are now an established technology in molecular biology with increasing number of applications in genomics and proteomics research. The main objective of this course is to introduce the participants to advanced bioinformatics, statistical methodologies and software tools for analyzing managing various microarray data, such as transcriptomics, proteomics and genotyping. This course is aimed at advanced MS and PhD students and postdoctoral researchers who are applying or planning to apply microarray analysis and bioinformatics methods in their research. The course will be presented under three major topics. 1) Fundamentals of microarray technology 2) analytics of microarray process 3) microarray informatics while latest software packages is introduced within appropriate lectures.

Course Content

Translation of bioinformatics application into healthcare is leading the predictive medicine and allow personalized approaches in both diagnosis and treatment. Thus there is an emerging need for bioinformatics who can analyze and interpret results of large genomics data sets and manage investigation of disease related variations and genes. Through formal lectures, students will learn the fundamentals of clinical bioinformatics, review the current software/tools/platforms, and relevant genomic/bioinformatics resources for the analysis of the genomics data. Critical reading assignments and discussion of case studies will allow students to understand current strategies, bioinformatics pipelines, and standard procedures. The program students who have completed the course will be informed about the current applications of bioinformatics in medical genetics and trained in clinical uses of bioinformatics.

Course Content