Statistical Background of Bioinformatics

Subject description

Computer tools for microarray analysis (R, Bioconductor) and links to data bases and ontologies.

  • Plan of experiment.
  • Data preparation and preprocessing.
  • Background correction.
  • Normalization.
  • Analysis of differential expression.
  • Methods for discovery of related gene sets.
  • Graphical data visualization.

The subject is taught in programs

Objectives and competences

Students will learn modern methods and steps of use of statistics in bioinformatics and microarray analysis. They will get hands on experience in using computational tools for analysis and visualisation of large data sets.

Teaching and learning methods

Lectures, practical work on computers, project work, individual projects.

Expected study results

Knowledge and understanding:

– use of Linux and  R for analysis of bioinformatics data,

– understanding of special issues in high dimensional data analysis.

Basic sources and literature

Knjige/ books:

  • ATTWOOD, T.K./ PARRY-SMITH, D.J. 1999. Introduction to bioinformatics. Pearson Education, Harlow, England.
  • Durbin, R., Eddy, S.R., Krogh, A., & Mitchison, G.J. Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. New York: Cambridge, UK, Cambridge University Press; 1998.
  • Datta, S. and Nettleton, D. eds., 2014. Statistical analysis of next generation sequencing data. Cham [etc.]: Springer.
  • Korpelainen E. RNA-seq data analysis : a practical approach. Boca Raton: CRC Press, Taylor & Francis Group; 2015.
  • Baker M. 2013. Big biology: The ’omes puzzle. Nature, 494, 7438: 416–419

Spletni viri/ web sources:

Introduction to Linux for bioinformatics

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