Ribopeaks: a web tool for bacterial classification through <i>m</i>/<i>z</i> data from ribosomal proteins

Tomachewski, Douglas; Galvão, Carolina Weigert; Júnior, Arion de Campos; Guimarães, Alaine Margarete; Rocha, José Carlos Ferreira da; Etto, Rafael Mazer

doi:10.1093/bioinformatics/bty215

Cited by 18 publications

(11 citation statements)

References 15 publications

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“…The mass of ribosomal subunits can be directly calculated from genomic sequences, as they are relatively conserved and rarely post-translationally modified. Their potential to serve as MALDI-TOF MS biomarkers has been applied to clinically relevant phylogenetic groups ( Hotta et al., 2010 ; Ziegler et al., 2015 ; Rothen et al., 2019 ), and multiple databases using marker masses predicted from genomic data are now available ( Kassim et al., 2017 ; Ojima-Kato et al., 2017 ; Tomachewski et al., 2018 ). A ribosomal marker based approach has successfully been applied to distinguish between subspecies and clonal complexes within species such as Streptococcus agalactiae and Escherichia coli ( Matsumura et al., 2014 ; Lafolie et al., 2015 ; Rothen et al., 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Factors Associated With MALDI-TOF Mass Spectral Quality of Species Identification in Clinical Routine Diagnostics

Cuénod

Foucault²,

Pflüger³

et al. 2021

Front. Cell. Infect. Microbiol.

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BackgroundAn accurate and timely identification of bacterial species is critical in clinical diagnostics. Species identification allows a potential first adaptation of empiric antibiotic treatments before the resistance profile is available. Matrix assisted Laser Desorption Ionization Time of Flight mass spectrometry (MALDI-TOF MS) is a widely used method for bacterial species identification. However, important challenges in species identification remain. These arise from (i) incomplete databases, (ii) close relatedness of species of interest, and (iii) spectral quality, which is currently vaguely defined.MethodsWe selected 47 clinically relevant bacterial isolates from 39 species, which can be challenging to identify by MALDI-TOF MS. We measured these isolates under various analytical conditions on two MALDI-TOF MS systems. First, we identified spectral features, which were associated with correct species identification in three different databases. Considering these features, we then systematically compared spectra produced with three different sample preparation protocols. In addition, we varied quantities of bacterial colony material applied and bacterial colony age.ResultsWe identified (i) the number of ribosomal marker peaks detected, (ii) the median relative intensity of ribosomal marker peaks, (iii) the sum of the intensity of all detected peaks, (iv) a high measurement precision, and (v) reproducibility of peaks to act as good proxies of spectral quality. We found that using formic acid, measuring bacterial colonies at a young age, and frequently calibrating the MALDI-TOF MS device increase mass spectral quality. We further observed significant differences in spectral quality between different bacterial taxa and optimal measurement conditions vary per taxon.ConclusionWe identified and applied quality measures for MALDI-TOF MS and optimized spectral quality in routine settings. Phylogenetic marker peaks can be reproducibly detected and provide an increased resolution and the ability to distinguish between challenging species such as those within the Enterobacter cloacae complex, Burkholderia cepacia complex, or viridans streptococci.

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Section: Introductionmentioning

confidence: 99%

Factors Associated With MALDI-TOF Mass Spectral Quality of Species Identification in Clinical Routine Diagnostics

Cuénod

Foucault²,

Pflüger³

et al. 2021

Front. Cell. Infect. Microbiol.

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“…Overall, the authors reported an outperformance of the model compared to existing benchmarks. Furthermore, Tomachewski et al [97] presented a bacterial classifier tool based on r-protein data. The application compares m/z data from MALDI-TOF MS analysis with ML models stored in a database that contains more than 28,500 bacterial taxonomic records.…”

Section: Detection and Identification Of Microorganismsmentioning

confidence: 99%

Recent evolutions of machine learning applications in clinical laboratory medicine

Bruyne

Speeckaert

Biesen

et al. 2020

Critical Reviews in Clinical Laboratory Sciences

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Machine learning (ML) is gaining increased interest in clinical laboratory medicine, mainly triggered by the decreased cost of generating and storing data using laboratory automation and computational power, and the widespread accessibility of open source tools. Nevertheless, only a handful of ML-based products are currently commercially available for routine clinical laboratory practice. In this review, we start with an introduction to ML by providing an overview of the ML landscape, its general workflow, and the most commonly used algorithms for clinical laboratory applications. Furthermore, we aim to illustrate recent evolutions (2018 to mid-2020) of the techniques used in the clinical laboratory setting and discuss the associated challenges and opportunities. In the field of clinical chemistry, the reviewed applications of ML algorithms include quality review of lab results, automated urine sediment analysis, disease or outcome prediction from routine laboratory parameters, and interpretation of complex biochemical data. In the hematology subdiscipline, we discuss the concepts of automated blood film reporting and malaria diagnosis. At last, we handle a broad range of clinical microbiology applications, such as the reduction of diagnostic workload by laboratory automation, the detection and identification of clinically relevant microorganisms, and the detection of antimicrobial resistance.

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“…An example is the lately introduced wide-ranging bacterium classifier tool, called Ribopeaks; it was created using r-protein data from the Genbank and now consists of~30,000 bacterial taxonomic records. The principle of its use is that it compares the incoming m/z data from the analysis using its own models in order to taxonomically classify the bacteria [18].…”

Section: Future Trends Of Msmentioning

confidence: 99%

Use of MALDI-TOF mass spectrometry in the battle against bacterial infectious diseases: recent achievements and future perspectives

Psaroulaki

Chochlakis

2018

Expert Review of Proteomics

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Ribopeaks: a web tool for bacterial classification through m/z data from ribosomal proteins

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 18 publications

References 15 publications

Factors Associated With MALDI-TOF Mass Spectral Quality of Species Identification in Clinical Routine Diagnostics

Factors Associated With MALDI-TOF Mass Spectral Quality of Species Identification in Clinical Routine Diagnostics

Recent evolutions of machine learning applications in clinical laboratory medicine

Use of MALDI-TOF mass spectrometry in the battle against bacterial infectious diseases: recent achievements and future perspectives

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