Abstract
Correct and rapid identification of microorganisms is the key to the success of many important applications in health and safety, including, but not limited to, infection treatment, food safety, and biodefense. With the advance of mass spectrometry (MS) technology, the speed of identification can be greatly improved. However, the increasing number of microbes sequenced is challenging correct microbial identification because of the large number of choices present. To properly disentangle candidate microbes, one needs to go beyond apparent morphology or simple ‘fingerprinting’; to correctly prioritize the candidate microbes, one needs to have accurate statistical significance in microbial identification. We meet these challenges by using peptidome profiles of microbes to better separate them and by designing an analysis method that yields accurate statistical significance. Here, we present an analysis pipeline that uses tandem MS (MS/MS) spectra for microbial identification or classification. We have demonstrated, using MS/MS data of 81 samples, each composed of a single known microorganism, that the proposed pipeline can correctly identify microorganisms at least at the genus and species levels. We have also shown that the proposed pipeline computes accurate statistical significances, i.e., E-values for identified peptides and unified E-values for identified microorganisms. The proposed analysis pipeline has been implemented in MiCId, a freely available software for Microorganism Classification and Identification. MiCId is available for download at http://www.ncbi.nlm.nih.gov/CBBresearch/Yu/downloads.html.Graphical ᅟElectronic supplementary materialThe online version of this article (doi:10.1007/s13361-015-1271-2) contains supplementary material, which is available to authorized users.
Highlights
Correct and rapid identification of microorganisms is the key to the success of many important applications in health and safety, including, but not limited to, infection treatment, food safety, and biodefense
We have proposed a pipeline for microbial identification/classification by processing mass spectrometry (MS)/MS data acquired in a high resolution mass spectrometer
Using a large number of samples from the Pacific Northwest National Laboratory (PNNL) dataset, we have shown that the proposed pipeline is able to confidently identify microorganisms at the genus and species levels when the sample preparation was optimized
Summary
Correct and rapid identification of microorganisms is the key to the success of many important applications in health and safety, including, but not limited to, infection treatment, food safety, and biodefense. There are different methods that employ MS-based technology to identify pathogens Of these methods, matrix-assisted laser desorption/ionization (MALDI)-based systems [23,24,25] and polymerase chain reaction electrospray ionization mass spectrometry (PCR-ESI-MS)-based systems have been the focus of most research in this direction [26,27,28,29]. Matrix-assisted laser desorption/ionization (MALDI)-based systems [23,24,25] and polymerase chain reaction electrospray ionization mass spectrometry (PCR-ESI-MS)-based systems have been the focus of most research in this direction [26,27,28,29] Comparison between these two systems in terms of their ability to accurately identify microorganisms has been performed with no significant difference found, both having about 95% identification accuracy at the species level [4]. In the two paragraphs, we briefly summarize the PCR-ESI-MS- and MALDI-based systems; the listed citations therein provide more detailed and comprehensive descriptions of both systems
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
