Course Title:
Semester: 2nd
Weekly teaching hours CREDITS (ECTS)
Lecture: 2 5


The topic of the course is to introduce students to the basic notions of Bioinformatics. Bioinformatics is a combination of Computer Science and Molecular Biology focusing at the design of data structures and computational methods to facilitate the efficient storage, retrieval and manipulation of biological molecules (DNA, RNA, genes, proteins).

Understanding basic computational methods such as string and machine learning algorithms and how they are used to develop software tools to processefficiently biologicall data is a main objective of this course. Course introduction to its main notions (Computer Science and Molecular Biology). Exact string matching algorithms (Boyer – Moore, Knooth-Morris-Pratt, Aho-Corasick Automaton). Suffix Trees and applications in Bioinformatics. Inexact matching an sequence alignment. Multiple sequence alignments.Bioinformatics Databases and Data Mining.

Learning outcomes

Learning Outcomes for the course “Bioinformatics” are as follows:

  • Understand the basic notions and principles of Bioinformatics, including the intersection of Computer Science and Molecular Biology.
  • Demonstrate knowledge of exact string matching algorithms such as Boyer-Moore, Knuth-Morris-Pratt, and Aho-Corasick Automaton, and their application in Bioinformatics.
  • Utilize Suffix Trees and understand their applications in various Bioinformatics tasks.
  • Apply sequence alignment techniques for inexact matching and understanding the concept of sequence similarity.
  •  Demonstrate proficiency in multiple sequence alignments, including the understanding of algorithms and tools used in Bioinformatics.
  • Gain familiarity with Bioinformatics databases and understand the principles and techniques of data mining in the context of biological data.
  • Develop practical skills through hands-on projects, including exercises on string algorithmics and research assignments investigating a specific research topic in Bioinformatics.
  • Analyze and interpret scientific papers and research findings in the field of Bioinformatics.
  • Apply critical thinking to evaluate and select appropriate methods, algorithms, and databases for specific Bioinformatics tasks.
  • Communicate effectively about Bioinformatics concepts, methods, and research findings to both technical and non-technical audiences.

General Competences

Computational Thinking: Apply computational and algorithmic approaches to analyze biological data and solve complex problems in Bioinformatics.

Data Analysis and Interpretation: Proficiency in analyzing and interpreting biological data using appropriate statistical and computational methods.

Programming Skills: Develop programming skills to implement algorithms, manipulate biological data, and utilize Bioinformatics tools and software.

Problem-Solving: Apply critical thinking and problem-solving skills to address challenges in analyzing biological data and designing computational solutions in Bioinformatics.

Bioinformatics Tools and Databases: Familiarity with commonly used Bioinformatics tools, databases, and resources, and the ability to navigate and retrieve relevant biological information.

Data Mining and Visualization: Utilize data mining techniques to extract meaningful patterns and insights from large biological datasets, and present findings through visual representations.

Interdisciplinary Collaboration: Collaborate effectively with experts from both computational and biological disciplines to tackle complex problems in Bioinformatics.

Ethical Considerations: Understand and adhere to ethical guidelines in handling biological data, ensuring privacy, and maintaining data integrity.