Statistična podlaga bioinformatike

Opis predmeta

Računalniška orodja za analizo mikromrež (R, Bioconductor) in povezovanje z bazami podatkov in ontologij.

načrt poskusa,
priprava podatkov,
odstranjevanje šuma ozadja,
normalizacija,
analiza diferencialne izraženosti,
metode za iskanje povezav med skupinami genov,
grafične predstavitve in vizualizacija rezultatov.

Cilji in kompetence

Pri predmetu se bodo študentje seznanili s sodobnimi metodami in fazami uporabe statistike v bioinformatiki in analize mikromrež: Usposobili se bodo za samostojno uporabo programskih orodij za analizo in vizualizacijo velikih količin podatkov.

Metode poučevanja in učenja

Predavanja, praktično delo z računalniki, projektno delo, individualne naloge.

Predvideni študijski rezultati

Znanje in razumevanje:

– uporaba Linux in R za analizo bioinformacijskih podatkov,

– razumevanje problema analize visokodimenzionalnih podatkov.

Reference nosilca

Kristina Gruden:

Rotter A, Hren M, Baebler S, Blejec A, Gruden K. Finding differentially expressed genes in two-channel DNA microarray datasets: how to increase reliability of data preprocessing. Omics : a Journal of Integrative Biology. 2008 Sep;12(3):171-182. DOI: 10.1089/omi.2008.0032
Bauer, C., Stec, K., Glintschert, A., Gruden, K., Schichor, C., Or-Guil, M., Selbig, J., & Schuchhardt, J. (2015). BioMiner: Paving the Way for Personalized Medicine. Cancer informatics, 14, 55–63. https://doi.org/10.4137/CIN.S20910
Baebler Š., Svalina M., Petek M., Stare K., Rotter A., Pompe-Novak M., Gruden K. 2017. QuantGenius: Implementation of a decision support system for qPCR-based gene quantification. BMC Bioinformatics, 18, 1: 1–11
Stare, T., Ramšak, Ž., Križnik, M. et al. Multiomics analysis of tolerant interaction of potato with potato virus Y. Sci Data 6, 250 (2019). https://doi.org/10.1038/s41597-019-0216-1
Schwacke R, Ponce-Soto GY, Krause K, Bolger AM, Arsova B, Hallab A, Gruden K, Stitt M, Bolger ME, Usadel B. MapMan4: A Refined Protein Classification and Annotation Framework Applicable to Multi-Omics Data Analysis. Mol Plant. 2019 Jun 3;12(6):879-892. doi: 10.1016/j.molp.2019.01.003. Epub 2019 Jan 9. PMID: 30639314.
Dobnik D, Gruden K, Ramšak Ž, Coll Rius A, editors. Solanum tuberosum : methods and protocols. Berlin: Springer; 2021.
Lukan, T., Pompe-Novak, M., Baebler, Š., Tušek-Žnidarič, M., Kladnik, A., Križnik, M., Blejec, A., Zagorščak, M., Stare, K., Dušak, B., Coll, A., Pollmann, S., Morgiewicz, K., Hennig, J., & Gruden, K. (2020). Precision transcriptomics of viral foci reveals the spatial regulation of immune-signaling genes and identifies RBOHD as an important player in the incompatible interaction between potato virus Y and potato. The Plant journal : for cell and molecular biology, 104(3), 645–661. https://doi.org/10.1111/tpj.14953

Temeljni viri in literatura

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:

Strežnik Nacionalnega centra za biotehnološko informacijo, ZDA http://www.ncbi.nlm.nih.gov
The R Project for Statistical Computing http://www.r-project.org/
Bioconductor – open source software for bioniformatics http://www.bioconductor.org/

Introduction to Linux for bioinformatics https://wiki.bits.vib.be/index.php/Introduction_to_Linux_for_bioinformatics