LFU:online

After successful completion of the course students will be aware of the importance of experiental design, understand various types of data produced, execute data transformations and analyses and extend their capacities of interpreting molecular biological datasets.

-Review of the methodological principles of the molecular approaches: Q-PCR, FISH, deep sequencing, microarray, NGS;

-Review of the types of data that these approaches produce;

-The relevance of environmental and spatial datasets;

-Identification of common types of data for analyses;

-Use various approaches intended to adjust data consistency: error-checking, missing data, outliers,

-Understand the role of data transformations: power, log, arcsine, relative;

-Select analysis approaches suitable for particular datasets and research questions;

-Formulation of testable hypotheses;

-Significant differences

-Identification of variables explaining the observed datasets;

-Importance of experimental design in producing defendable data;

-Set-up experimental designs that would enable defendable data acquisition;

-Interpretation of experimental observations;

-importance of data presentation.

-Familiarization and basic use of programs such as mothur, SDS, PAST, R(selected routines) with prepared test datasets that can be easily adapted for later use

-Hands on analyses of datasets from each of the molecular approaches

Continuous assessment (based on regular written and/or oral contribution by participants).

Course examination according to § 7, statute section on "study-law regulations"

Will be discussed in the first lesson.