Graphical illustration for explaining the conceptual basis and caveats of applying pattern recognition to mass spectrometry-based microbial identification
Creators: name: Ng, Wenfa
Publisher: name: figshare
Year: 2014
Subjects: subject: Education
subject: 60102 Bioinformatics
subject: FOS: Computer and information sciences
subject: FOS: Computer and information sciences
subject: Environmental Science
subject: Ecology
subject: FOS: Biological sciences
subject: FOS: Biological sciences
subject: Microbiology

description: Although biomarker-based techniques are predominant in microbial identification, they are nevertheless limited to a small subset of microbes for which well-understood biomolecules could serve as species or strains identifiers. Given the inherent constraints of using known biomarkers for understanding the microbial constituents in diverse ecosystems, various data discovery approaches that took advantage of the increasingly important omics workflows have taken centre stage in many studies. Whether at the gene, protein or metabolite level, such techniques typically generate large datasets – via employing high throughput approaches – and are thus not readily amenable for analysis using conventional analytical approaches more suited to smaller datasets. More important, the cornucopia of data generated also meant that, in most instances, data generation overwhelms analytical capacity - specifically, we lack the experimental resources for dissecting the biological basis, functions and mechanisms of the putative biomarkers discerned through large scale, high throughput data mining workflows. Thus, irrespective of the extent to which we understand the biology underlying our datasets, analytical approaches that seek to answer higher level abstract questions, without explicitly tackling the deeper (and more involved) subtleties common in biology, have become <em>de rigueur</em> in contemporary biological research. For example, by comparing the presence or absence of specific metabolites or proteins, pattern recognition approaches are popular for rapidly answering the central question in microbiology – what is the species or strains that we are dealing with – without the need of knowing the molecular identities of the analytes profiled. Specifically, pattern recognition is the algorithmic workhorse powering the analysis of the large datasets typical of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) microbial typing, where a range of mass peaks belonging to proteins or metabolites provide the biological basis for species identification. Nevertheless, the complexity of the mathematics underpinning the statistic-based approach as well as the generally inaccessible and ineffective method of using mathematics for explaining the conceptual basis of pattern recognition meant that most students lack understanding of the principles of the approach and, more important, caveats important for avoiding misidentification. Using a simple graphical illustration encapsulating three prototypical examples common in MALDI-TOF microbial typing, a simple pedagogical tool has been developed for explaining the concepts underlying pattern recognition and the caveats critical for avoiding false-positives and –negatives in microbial identification.