This course aims to provide students with the theoretical background and hands on experience of the basic techniques employed in bioinformatics. The course will focus on biological sequence data (DNA, RNA, protein) analysis and their application.

This course provides aspects of structural bioinformatics and deals with computational applications in drug discovery. It starts with a review of protein modeling and then quickly moves into computational techniques with a special emphasis on drug discovery context. Example topics include protein homology modeling, ligand-protein molecular docking, and other prediction methods. The course meetings

The main aim of the course is to introduce programming principles for non-programmers who come from different life science backgrounds. The course starts with explaining why programming is needed and why Python is used. The course covers the following topics with applications from is needed and why Python is used. The course covers the following topics with applications from computational biology

The course covers basic theoretical foundations and hands-on practice on analyzing high_x0002_throughput sequencing data. Topics discussed in depth include sequencing technologies, QC and preprocessing of raw sequencing reads, transcriptome assembly and annotation, and differential expression analysis. The course includes crash training on Bash scripting therefore no prior programming experience

The course involves basics for analysis using R language, Analysis of different omics data like microarray, RNA-Seq data, miRNA data, methylation data etc., how the integration between different data improves our understanding for the disease mechanism, This besides basics for supervised and unsupervised machine learning techniques

The course focuses on variant calling and genome wide association studies to allow disease gene discovery research. The course covers several genomics, statistical and bioinformatics concepts with great emphasis on hands-on coding and solving real research problems. CIT657 is a prerequisite for this course. The students are expected to attend the lectures and share actively in all course

The main aim of the course is to analyze and visualize biological data using the R language. The course starts with how to use the R language to retrieve, process, and store data in different formats. Then the course goes with how to calculate data basic descriptive statistics, visualize data in different graphic types, assume initial conclusions that can help with hypotheses generation, and use