BIN 501/CENG465 Introduction to Bioinformatics 

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.



BIN 503 Biological Databases and Data Analysis Tools

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.


BIN 504 Probabilistic and Statistical Modeling for Bioinformatics

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.


BIN 590 Graduate Seminar in Bioinformatics

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 Graduate Program.


BIN 599 Master’s Thesis


BIN 589 Term Project

Must For Non Thesis 

BIN 505 Foundations of Systems Biology

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.

Technical Elective

BIN 506 Protein and DNA Sequence Analysis

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, Prosite, and Pfam.

Technical Elective


BIN 711 Applications of Bioinformatics in Molecular Biology

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 information sciences, these data and tools to analyze the data  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 introductory level. Practical section of the course will emphasize on how to use the computer and bioinformatics applications to aid in biological research. 

Technical Elective

BIN 712 Computational Methods In Bioinformatics

Recent advances on technology, molecular biology and large-scale 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 the understanding and implementation of bioinformatics applications to aid in biological research. Furthermore, understanding the basic computational concepts used in data analysis will gain experience for later work in cooperation with computer scientists.

Technical Elective

BIN 714 Microarray Data Analysis and Informatics

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 and managing various microarray data, such as transcriptomics, proteomics and genotyping. This course is aimed at advanced MS and PhD level students and post-doctoral 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. The latest software packages will be introduced within appropriate lectures. 

Technical Elective