Deep Neural Networks for Peptide Identification in Data Independent Acquisition Mass Spectrometry

Mass spectrometry is an analysis technique used for the quantification and structural determination of molecules, which is widely used in fields such as medicine, cosmetology, marine sciences and others. It was developed over 100 years ago, but continues to evolve with the development of computer technologies. Mass spectrometers produce a huge amount of information that needs to be analyzed and stored, and researchers proposed various approaches for data acquisition and preprocessing. A noticeable attainment in this domain is data-independent acquisition mass spectrometry, which minimizes data loss and looks promising in peptide identification quality increasing. However, it increases the size and complexity of the data, but as technological progress moves rapidly forward, this problem is becoming less substantial and the interest of researchers in data-independent acquisition mass spectrometry is escalating. The use of machine learning algorithms and deep neural networks in computational biology offers promising development of MS data analysis. Mass spectrometry data is prone to sudden appearances and disappearances of spectral ions, but at the same time, due to its chemical nature, it has many connected components that neural networks could generalize and find hidden patterns in the data. The goal of this work is to develop a convolutional neural network for preprocessing DIA spectra, which will help to improve the quality of peptide identification. For this purpose, basic methods and tools for MS analysis were studied and implemented in the pipeline along with the model training. This thesis presents the results of experiments on training the model with different parameters on DIA dataset consisting of mass spectrometry experiments with Saccharomyces cerevisiae (Baker's yeast), which helped to improve the quality of peptide identification for low-resolution data.