ECTS
ECTS Course Catalogue
Course details
Course code: BBS20176o13Semester: 2013/2014 winter
Name: Bioinformatics
Major: Biology
Study Type: second cycle
Course type: compulsory
Study Semester: 1
ECTS points: 4
Hours (Lectures / Tutorials / Other): 15 / 15 / 0
Lecturer: dr hab. Joanna Szyda
Language of instruction: Polish / English
Learning outcomes: Knowledge: Student: W1 - has the knowledge to interpret biological phenomena and processes in research and practical activities [KB2_W01]; W2 - knows the research methodology applicable in the natural sciences [KB2_W01]; W3 - formulates research hypotheses, and solves basic scientific problems [KB2_W01]; W4 - knows how to create interesting multimedia presentation. [KB2_W01]. W5 - describes and explains the biological phenomena in mathematical terms [KB2_W02]; W6 - understands the importance of methods of descriptive statistics and mathematical modeling in description and interpretation of biological phenomena and processes, in the statistical analysis of data and in obtaining and processing information [KB2_W02]; W7 - knows the most important statistical tools to assess the course of natural phenomena and processes [KB2_W02]; W8 - performs analysis of variance using statistical packages. Knows the applicability of parametric and nonparametric tests. [KB2_W02]. Skills: Student: U1 – is able to create databases. Applies appropriate statistical methods for data analysis, the description of phenomena and for hypotheses testing. Performs mathematical calculations also using statistical packages. [KB2_U03]; U2 - based on data analysis formulates and interprets the results of research tasks. Skillfully juxtaposes them in tables and illustrates them graphically. Compares them with other sources. Draws the appropriate conclusions and makes appropriate judgments. [KB2_U04]; U3 - demonstrates skills of critical analysis and selection of information, especially from electronic sources. Creates and uses bioinformatics databases. Assesses the links between genes. Conducts analysis of the genome (linkage analysis, association analysis). Uses bioinformatics’ algorithms. [KB2_U04_BT];
Competences: Personal and social competences (attitudes and behaviors): Student: K1 - is willing to regularly update knowledge on biology and related disciplines, and conscious of the need for learning throughout life [KB2_K01]; K2 - is active in improving skills through courses and specialized training and the literature [KB2_K01]; K3 - is able to inspire and organize the learning process of others. [KB2_K01].
Prerequisites: none
Course content: Databases, Hardy-Weinberg equilibrium, recombination rate, linkage analysis, association analysis, Monte Carlo simulations, Markov chains, classification methods
Recommended literature: 1. Barnes, M.R. (2007) Bioinformatics for Geneticists: A Bioinformatics Primer for the Analysis of Genetic Data. Wiley; 2. Higgs, P.G., Attwood, T.K. (2008) Bioinformatyka i ewolucja molekularna, PWN
Assessment methods: Assessment of tutorials: Based on the average grade calculated from the project, two written tests and presentation. The final grade can be enhanced by student’s active participation in tutorials. The presence at tutorials is compulsory. The student may have one unauthorized absence. Completion of the course: students with a valid credit for a tutorial undergo a written or oral exam (5-10 open questions) in the examination session. The written examination lasts up to 90 minutes. During the exam
Comment: specialization: Laboratory Techniques in Biology