![]() In one of the early studies on the identification of microorganisms by MALDI TOF MS, Arnold R.J. Coefficients of correlation are a math algorithm for a comparison of two analytical signals presented as functions this method can also be applied to spectrometry data and to other areas ( Horlick and Hieftje, 1978 Ng and Horlick, 1981). As for biotyping of microorganisms, two main approaches have been assessed: (1) the coefficient of correlation for FSA and (2) peak table-based methods. As a measure for similarity/dissimilarity between spectra, different approaches have been proposed: a sum of difference modules in peak intensities or squares of differences that ended up in one channel (a fixed region of a spectrum) ( Crawford and Morrison, 1968 Knock et al., 1970 Grotch, 1971), a ratio of peak intensities in one channel ( Hertz et al., 1971), dot-product ( Stein and Scott, 1994), Euclidean distance ( Rasmussen and Isenhour, 1979), probability-based matching ( McLafferty et al., 1974 Stauffer et al., 1985), and more complicated procedures, for example, “divergence” ( Farbman et al., 1973). ![]() Initially, a wide range of mathematical approaches was available for a comparison of mass spectrometry data these approaches have been tested and optimized on applications related to the identification of organic compounds by gas chromatography with mass spectrometry ( Crawford and Morrison, 1968 Grotch, 1970, 1971 Knock et al., 1970 Hertz et al., 1971 Costello et al., 1974 Mathews and Morrison, 1974 McLafferty et al., 1974 Rasmussen and Isenhour, 1979 Stauffer et al., 1985 Stein and Scott, 1994). ĭue to the advent of the matrix-assisted laser desorption ionization time of flight mass-spectrometry (MALDI TOF MS) and because microorganism identification by means of reference mass spectra libraries has become possible, it is now necessary to choose or develop a new mathematical algorithm for the analysis of mass spectrometry data. The resulting database, raw data, and the set of R scripts are available online at. We proposed a method for calculating cut-off thresholds based on averaged intraspecific distances. We demonstrated that the algorithms based on peak-picking and analysis of complete data have accuracy no less than that of Biotyper 3.1 software. We used 74 microbial strains from the collections of ICiG SB RAS, UNIQEM, IEGM, KMM, and VGM as the models. ![]() We also studied the possibility of using full spectra analysis (FSA) without calculating mass peaks (PPA), which is the logical development of the method. In this study, the geometric approach was realized as R scripts which allowed us to design a Web-based application. We demonstrated its efficiency in delimiting two closely related species of the Bacillus pumilus group. This algorithm was implemented in a Jacob4 stand-alone package. ![]() In our previous study we proposed the geometric approach for processing mass spectrometry data, which represented a mass spectrum as a vector in a multidimensional Euclidean space. Choosing the optimal mathematical apparatus is the pivotal issue for this task. This problem can be solved by creating an Internet platform for open databases of protein spectra of microorganisms. However, it is significantly limited by the absence of a universal database of reference mass spectra. Identification of microorganisms by MALDI-TOF mass spectrometry is a very efficient method with high throughput, speed, and accuracy. 3Department of Cytology and Genetics, Novosibirsk State University, Novosibirsk, Russia.2Kurchatov Genomics Center of Federal Research Center Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia.1Laboratory of Molecular Biotechnologies of Federal Research Center Institute of Cytology and Genetics of The Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia.
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