Course syllabus

Course-PM

BBT045 Applied bioinformatics lp3 VT22 (7.5 hp)

Course is offered by the department of Biology and Biological Engineering

Course purpose

The course aims at providing applied knowledge behind bioinformatics methods used for biological sequence analysis, gives hands-on experience in practical analysis of next-generation sequencing (NGS) data. The student should gain a critical view on bioinformatics methods, be able to conduct reproducible research in computational biology projects and become familiar with sequence analysis.

Course design

The course will be mix of lectures, flipped classrooms (https://en.wikipedia.org/wiki/Flipped_classroom), practical exercises, individual assignments as home works and a team work project. During the lectures the instructors will teach you new theoretical concepts that will be practiced in class or through homework assignments. Everyone learns differently and has her/his own learning strategies, therefore during the flipped classroom, you will be following the provided videos at your own pace, and teacher in the class will summarise the video material and will be able to answer the questions you have. The homework assignments will be mainly to practice your programming skills and introduce you to algorithmic thinking. The project work will provide a hands on experience in practical bioinformatics and will demonstrate the importance of reproducible science. 

All the communications and practical information, including slides and literature will be posted on here on Canvas. Homeworks, tutorials. exercises and study materials are posted on course webpage: https://zelezniak-lab.github.io/MPBIO-BBT045/ 

Learning outcomes

  • Apply, analyse and implement established bioinformatics methods used for biological sequence analysis, including pairwise sequence alignment, multiple sequence alignment and its evolutionary aspects.
    • Learning activity: You will be familiarised with the theory of how biological sequence alignments works. In the homework assignment students implement the algorithm in R based on the the theory learned in the class. During the project work, you will apply algorithms and analyse the results for the real world bioinformatics problem.
    • Assessment: Implementation is assessed based on the homework assignments. Application is assessed in the project work. Both, analysis and implementation assessed also though the exam questions.
  • Apply established techniques for mapping of NGS reads to reference genomes, analyse and compare the situations for efficient sequence similarity searches.
    • Learning activity: You will be familiarised of how NGS sequencing and data analysis typically done. In the homework assignments you will implement yourself crucial part of NGS analysis algorithms. During the project work you will apply and analyse the results using established methods from the real world bioinformatics problem.
    • Assessment: Analysis and application of NGS techniques will be assessed through the exam questions and team project.
  • Compare and apply methods for de novo sequence assembly of data generated by next generation DNA sequencing.
    • Learning activity: You will be familiarised with the theory behind de novo genome assembly algorithms. Exercises and team project work will provide you with hand-on experience of application of genome assembly algorithms. 
    • Assessment: Homework assignments and exam questions will asses the understanding the theory behind the algorithms and their the practical applications.
  • Apply and implement methods to predict genes and their functions and perform annotation of DNA sequences. Special emphasis will be given to Hidden Markov Models (HMM).
    • Learning activity: During the class lectures, you will be familiarised the theory of HMMs with some very simple theoretical examples from real life (e.g coin tissing) then the theory is extended to gene finding. In the homework assignments implements HMM principles and algorithms to compute probabilities of certain events. During the class exercises and project work you will apply algorithms for the real world bioinformatics problems.
    • Assessment: Homework assignments and exam questions will asses the understanding the theory behind the algorithms and their the practical applications.
  • Evaluate computational issues in analysing sequence data on small and large scale, including algorithmic limitations and the need for heuristic approaches.
    • Learning activity: During the class and homeworks you will be familiarised with computational complexity of algorithms (e.g.  big O notation). 
    • Assessment: During the exam you will need to evaluate the complexity of functions. 
  • Discuss and apply different methods to combine omics data from multiple platforms.
    • Learning activity: During the class and project work you will be familiarised with omics analysis and algorithms. 
    • Assessment: Project work would require to work with NGS/omics technology in practical setting.

Link to the syllabus on Studieportalen.

Study plan

Examination form

Written Exam, team project (compulsory) and individual homeworks (compulsory). 

Contact details