The bacteria that are the most prevalent causes of urinary tract infections are typically identified using standard microbiological procedures. The biggest downside of these approaches is that they take a long time to complete. As a result, researchers are always looking for new ways to determine the quantity and kind of microorganisms faster and more efficiently. The possibility of utilizing mass spectrometry (MS) and sequencing of 16S rRNA genes to detect bacteria from urine were studied in this doctoral thesis. Classical microbiological tests were utilized to identify bacteria isolated from the urine of patients with cystitis symptoms, after which microbiomes and metaproteom were analyzed. Microbiome investigation included identifying genes for 16S rRNA, while MALDI-TOF MS/MS (matrix aided laser desorption ionization-time of flight tandem mass spectrometry) analysis of the metaproteome tried to acquire strain-level resolution. The aim of the study was to design and test an MS method for direct, rapid, and reliable identification of microorganisms in urine samples of patients whose presence of microorganisms > 105 CFU / ml was confirmed by traditional microbiological methods, and to compare the results with those of genomic analysis of urine microorganisms. Also, the aim of the study was to monitor changes in the composition of the urinary microbiota caused by cystitis and the dynamics of their change due to antibiotic therapy. The research included the analysis of a total of 396 urine samples collected through four phases in KB Dubrava. In addition, 40 culture samples from the four most frequent uropathogens were studied, as well as 8 urine samples from the same person who had been on antibiotic therapy to monitor changes in the urinary microbiota. The findings of this study revealed that mass spectroscopy can be used to identify bacteria in monomicrobial cultures at the genus level. Mass spectroscopy, on the other hand, does not yield good identification results in polymicrobial cultures. The 16S rRNA gene provided taxonomic categorization of uropathogens down to the genus (44 %) and family (56 %) levels, but not species level identification. It was discovered that monitoring the urine microbiota during antibiotic therapy has a substantial influence on the dynamics of changes in its composition, and that the duration of therapy, as well as the initial drug choice, is an extremely essential therapeutic parameter.