TY - JOUR

T1 - Accurate noise-robust classification of Bacillus species from MALDI-TOF MS spectra using a denoising autoencoder

AU - Uvarova, Yulia E

AU - Demenkov, Pavel S

AU - Kuzmicheva, Irina N

AU - Venzel, Artur S

AU - Mischenko, Elena L

AU - Ivanisenko, Timofey V

AU - Efimov, Vadim M

AU - Bannikova, Svetlana V

AU - Vasilieva, Asya R

AU - Ivanisenko, Vladimir A

AU - Peltek, Sergey E

N1 - The study was funded by the Ministry of Science and Higher Education of the Russian Federation project “Kurchatov Center for World-Class Genomic Research” No. 075-15-2019-1662 from 2019-10-31. The authors express their gratitude to the Center for Collective Use (CCU) “Bioinformatics” for the computational resources and their software, created within the framework of the budget project FWNR-2022-0020. © 2023 the author(s), published by De Gruyter, Berlin/Boston.

PY - 2023/9/1

Y1 - 2023/9/1

N2 - Bacillus strains are ubiquitous in the environment and are widely used in the microbiological industry as valuable enzyme sources, as well as in agriculture to stimulate plant growth. The Bacillus genus comprises several closely related groups of species. The rapid classification of these remains challenging using existing methods. Techniques based on MALDI-TOF MS data analysis hold significant promise for fast and precise microbial strains classification at both the genus and species levels. In previous work, we proposed a geometric approach to Bacillus strain classification based on mass spectra analysis via the centroid method (CM). One limitation of such methods is the noise in MS spectra. In this study, we used a denoising autoencoder (DAE) to improve bacteria classification accuracy under noisy MS spectra conditions. We employed a denoising autoencoder approach to convert noisy MS spectra into latent variables representing molecular patterns in the original MS data, and the Random Forest method to classify bacterial strains by latent variables. Comparison of the DAE-RF with the CM method using the artificially noisy test samples showed that DAE-RF offers higher noise robustness. Hence, the DAE-RF method could be utilized for noise-robust, fast, and neat classification of Bacillus species according to MALDI-TOF MS data.

AB - Bacillus strains are ubiquitous in the environment and are widely used in the microbiological industry as valuable enzyme sources, as well as in agriculture to stimulate plant growth. The Bacillus genus comprises several closely related groups of species. The rapid classification of these remains challenging using existing methods. Techniques based on MALDI-TOF MS data analysis hold significant promise for fast and precise microbial strains classification at both the genus and species levels. In previous work, we proposed a geometric approach to Bacillus strain classification based on mass spectra analysis via the centroid method (CM). One limitation of such methods is the noise in MS spectra. In this study, we used a denoising autoencoder (DAE) to improve bacteria classification accuracy under noisy MS spectra conditions. We employed a denoising autoencoder approach to convert noisy MS spectra into latent variables representing molecular patterns in the original MS data, and the Random Forest method to classify bacterial strains by latent variables. Comparison of the DAE-RF with the CM method using the artificially noisy test samples showed that DAE-RF offers higher noise robustness. Hence, the DAE-RF method could be utilized for noise-robust, fast, and neat classification of Bacillus species according to MALDI-TOF MS data.

KW - Bacillus

KW - Bacteria

KW - Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization/methods

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85181395192&origin=inward&txGid=9f3b69990a687df5b6d090aaebb2f766

UR - https://www.mendeley.com/catalogue/93908a41-e911-3223-b519-7bb56644d66c/

U2 - 10.1515/jib-2023-0017

DO - 10.1515/jib-2023-0017

M3 - Article

C2 - 37978847

VL - 20

JO - Journal of integrative bioinformatics

JF - Journal of integrative bioinformatics

SN - 1613-4516

IS - 3

ER -