Диссертации, представленные на защиту и подготовленные в НИУ ВШЭ

This dissertation explores the development of accurate and efficient methods for peptide-spectrum match (PSM) scoring in high-resolution tandem mass spectrometry data. Traditional scoring methods often fail to handle the complexity of high-resolution data. Standard XPV method is computationally expensive and slow and that was extended by the Faster XPV algorithm and implemented into the CRUX Toolkit. Same time, the exact p-value (XPV) method extended on high-resolution and breakdown limitations and presented findings in a new algorithm called HR-XPV. It improves calibration by correctly scoring more fragment ions using remainder mass tracking. Additionally, the dissertation presents the SeVa algorithm, which finds single amino acid variation in peptides without needing to search large databases. These methods were tested on several datasets and showed strong results in score calibration and single amino acid mutation detection. This research helps make proteomics and MS/MS data analysis more reliable and accurate for biological and clinical research.