SCHWARZEROVÁ, J. Genomická predikce a celo-genomové asociační studie v rámci metabolických sítí [online]. Brno: Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. 2021.
Jana Schwarzerová has conducted an impressive research project, by applying state of the art statistical methods in the context of genomic and metabolic data analysis. She investigated Genotype-Phenotype relationships in Arabidopsis thaliana by applying genomic prediction, metabolite genome wide association studies (mGWAS) and Bayesian prediction models, as well as a novel approach for the reconstruction of the biochemical Jacobian. Among other results, her extensive study finds interesting adaptions of A. thaliana under different environmental conditions. Furthermore, the work illustrates the great potential of the employed methods in the field of bioinformatics and ecology. Overall, her applied work can be considered remarkable. However, the written thesis lacks coherence and for large parts the reading fluency is disturbed by an erratic style. In conclusion, the master thesis is good, but with more time and dedication to a scientific writing style, the work could have been excellent.
Ms Schwarzerova has worked on the question how metabolomic data can be interpreted, linked and placed in a functional context with genomic data. She developed a variety of algorithms for genomic prediction, applied and worked on metabolic genome-wide association studies (mGWAS) and Bayesian prediction methods. Ms Schwarzerova further implemented a complex network analysis for inverse calculation of the biochemical Jacobian matrix and continued to adapt it to the specific conditions of the data sets. There are new and innovative methods for analyzing the genotype-phenotype relationship and they are widely applicable in many contexts ranging from examination of biological systems up to biomedical and clinical studies. These methods are also of great importance for professional practice in data analysis and form the basis for statistical evaluation of highly complex data sets and their functional interpretation. Ms Schwarzerova performed exceptionally well, worked independently and provided many of her own impulses. I assess Ms Schwarzerova's work as very good (1).
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