Bioinformatics Diploma

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 embrace a blend of lectures and practical sessions. The coursework includes readings, assignments, and ends with a project presentation by students.

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: python data types, text, files reading and writing, controlled programming, functions, graphics production, and parallel programming.

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 is required. The students are expected to attend the lectures and share actively in all course activities.

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. CIT673 is a prerequisite for this course. The students are expected to attend the lectures and share actively in all course activities.

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 statistical tools to formulate and test the hypotheses, interpret the results, and quantitatively assess significance of the analysis findings.

This course provides an in-depth exploration of metagenomics, focusing on the study of microbial communities and their ecological roles. Participants will gain theoretical knowledge and hands-on experience in analyzing microbial data using state-of-the-art bioinformatics tools and techniques. The course covers 16S rRNA gene profiling, shotgun metagenomics, functional annotation, and metatranscriptomics. Practical sessions complement lectures, offering students opportunities to apply methodologies for sequencing, data preprocessing, taxonomic annotation, and functional pathway analysis. Assignments and quizzes reinforce learning and ensure competency in computational analysis.

This course examines the integration of machine learning into precision medicine, emphasizing its impact on personalized healthcare. Students will explore supervised, unsupervised, and reinforcement learning methods for analyzing complex biological data. Key topics include omics-based diagnostics, functional and phenotypic analysis, and drug discovery applications. Hands-on sessions and real-world case studies provide practical experience in implementing machine learning in clinical settings. Students will develop skills to interpret models and design individualized treatment plans. The course highlights challenges and opportunities in precision medicine, fostering a deeper understanding of computational approaches in advancing preventative and personalized care.

This course provides a comprehensive introduction to single-cell data analysis, covering key techniques for processing and interpreting single-cell RNA sequencing (scRNA-seq) data. Participants will learn about quality control, normalization, clustering, differential expression, and visualization methods. Hands-on sessions with cutting-edge tools will equip learners to analyze complex single-cell datasets and uncover biological insights.