Bioinformatics, 6 credits

Bioinformatik, 6 hp

732A51

Main field of study

Statistics

Course level

Second cycle

Course type

Single subject and programme course

Examiner

Krzysztof Bartoszek

Course coordinator

Krzysztof Bartoszek

Director of studies or equivalent

Jolanta Pielaszkiewicz

Available for exchange students

Yes

Contact

ECV = Elective / Compulsory / Voluntary
Course offered for Semester Weeks Language Campus ECV
Single subject course (Half-time, Day-time) Autumn 2021 202144-202202 English Linköping
Single subject course (Half-time, Day-time) Autumn 2021 202144-202202 English Linköping
F7MSL Statistics and Machine Learning, Master´s Programme - First and main admission round 3 (Autumn 2021) 202144-202202 English Linköping E
F7MSL Statistics and Machine Learning, Master´s Programme - Second admission round (open only for Swedish/EU students) 3 (Autumn 2021) 202144-202202 English Linköping E

Main field of study

Statistics

Course level

Second cycle

Advancement level

A1N

Course offered for

  • Master's Programme in Statistics and Machine Learning

Entry requirements

  • 180 ECTS credits passed including 90 ECTS credits in one of the following subjects:
    • mathematics
    • applied mathematics
    • statistics
    • bioinformatics
      or
    • computer science
  • Passed courses in:
    • statistics, basic course
    • computer science, basic course
  • English corresponding to the level of English in Swedish upper secondary education (Engelska 6)
    Exemption from Swedish

Intended learning outcomes

After completion of the course the student should on an advanced level be able to:

  • account for concepts in molecular biology and apply various techniques used for generating data.
  • accoount for major algorithms and principles of statistical models used for analysis of high-dimensional molecular data.
  • apply some of the most important bioinformatics and statistical software tools to real molecular data examples.

Course content

The course introduces basic molecular biology concepts and how to analyze data with bioinformatics and statistics. More specifically, the course includes:

  • Basics of molecular biology and genetics
  • Hidden Markov models, genetic sequence analysis                                                               
  • Sequence similarity, sequence alignment                                                                              
  • Phylogeny reconstruction                                                                                                   
  • Quantitative trait modelling                                                                                              
  • Microarray analysis                                                                                                        
  • Network biology           

Teaching and working methods

The teaching comprises lectures and computer exercises. The lectures are devoted to presentations of concepts and methods. The computer exercises provide practical experience of bioinformatics and statistical software usage for analysis of molecular genetic data. Homework and independent study are a necessary complement to the course. Language of instruction: English

Examination

Written reports on computer exercises. One final written or oral examination. Detailed information about the examination can be found in the course’s study guide.

If the LiU coordinator for students with disabilities has granted a student the right to an adapted examination for a written examination in an examination hall, the student has the right to it. If the coordinator has instead recommended for the student an adapted examination or alternative form of examination, the examiner may grant this if the examiner assesses that it is possible, based on consideration of the course objectives.

Students failing an exam covering either the entire course or part of the course twice are entitled to have a new examiner appointed for the reexamination.

Students who have passed an examination may not retake it in order to improve their grades.

Grades

ECTS, EC

Other information

Planning and implementation of a course must take its starting point in the wording of the syllabus. The course evaluation included in each course must therefore take up the question how well the course agrees with the syllabus. 

The course is carried out in such a way that both men´s and women´s experience and knowledge is made visible and developed.

Department

Institutionen för datavetenskap
Code Name Scope Grading scale
DAT1 Examination 3 credits EC
FRIV Voluntary Assignment 0 credits EC
LAB1 Laboratory 3 credits EC
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