"Background. Rapid, easy, economical and accurate species identification of yeasts isolated from clinical samples remains an important challenge for routine microbiological laboratories, because susceptibility to antifungal agents, probability to develop resistance and ability to cause disease vary in different species. To overcome the drawbacks of the currently available techniques we have recently proposed an innovative approach to yeast species identification based on RAPD genotyping and termed McRAPD (Melting curve of RAPD). Here we have evaluated its performance on a broader spectrum of clinically relevant yeast species and also examined the potential of automated and semi-automated interpretation of McRAPD data for yeast species identification. Results. A simple fully automated algorithm based on normalized melting data identified 80% of the isolates correctly. When this algorithm was supplemented by semi-automated matching of decisive peaks in first derivative plots, 87% of the isolates were id" . "21"^^ . . "6"^^ . . "GB - Spojen\u00E9 kr\u00E1lovstv\u00ED Velk\u00E9 Brit\u00E1nie a Severn\u00EDho Irska" . "RIV/61989592:15110/09:00009795!RIV10-MZ0-15110___" . "[DF4336B5810E]" . "Performance of optimized McRAPD in identification of 9 yeast species frequently isolated from patient samples: potential for automation"@en . . . . "Trtkov\u00E1, Jitka" . "Pavl\u00ED\u010Dek, P." . . "15110" . "1471-2180" . "Performance of optimized McRAPD in identification of 9 yeast species frequently isolated from patient samples: potential for automation"@en . . "RIV/61989592:15110/09:00009795" . . "BMC Microbiology" . . . "Performance of optimized McRAPD in identification of 9 yeast species frequently isolated from patient samples: potential for automation" . . . "Koukalov\u00E1, Dagmar" . . . "pathogenic yeasts; identification; McRAPD; melting analysis"@en . . . "333061" . . "P(NR8365)" . "234" . "Ruskov\u00E1, Lenka" . "Hamal, Petr" . "Background. Rapid, easy, economical and accurate species identification of yeasts isolated from clinical samples remains an important challenge for routine microbiological laboratories, because susceptibility to antifungal agents, probability to develop resistance and ability to cause disease vary in different species. To overcome the drawbacks of the currently available techniques we have recently proposed an innovative approach to yeast species identification based on RAPD genotyping and termed McRAPD (Melting curve of RAPD). Here we have evaluated its performance on a broader spectrum of clinically relevant yeast species and also examined the potential of automated and semi-automated interpretation of McRAPD data for yeast species identification. Results. A simple fully automated algorithm based on normalized melting data identified 80% of the isolates correctly. When this algorithm was supplemented by semi-automated matching of decisive peaks in first derivative plots, 87% of the isolates were id"@en . . "5"^^ . "Raclavsk\u00FD, Vladislav" . "Performance of optimized McRAPD in identification of 9 yeast species frequently isolated from patient samples: potential for automation" . . . "9" .