Identification of Microorganisms by High Resolution Tandem Mass Spectrometry with Accurate Statistical Significance.
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 Abstract ᅟ.
National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA.
Gelio
G
Alves
Proteomics Core, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, 20892, USA.
Guanghui
G
Wang
National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA.
Aleksey Y
AY
Ogurtsov
Critical Care Medicine Department, Clinical Center, National Institutes of Health, Bethesda, MD, 20892, USA.
Steven K
SK
Drake
Proteomics Core, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, 20892, USA.
Marjan
M
Gucek
Critical Care Medicine Department, Clinical Center, National Institutes of Health, Bethesda, MD, 20892, USA.
Anthony F
AF
Suffredini
Department of Laboratory Medicine, Clinical Center, National Institutes of Health, Bethesda, MD, 20892, USA.
David B
DB
Sacks
National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA. yyu@ncbi.nlm.nih.gov.
Yi-Kuo
YK
Yu
D001419:Bacteria / Q000737:chemistry / Q000145:classification*; D016208:Databases, Factual; D004926:Escherichia coli / Q000145:classification; D010455:Peptides / Q000032:analysis / Q000737:chemistry; D011550:Pseudomonas aeruginosa / Q000145:classification; D012984:Software; D053719:Tandem Mass Spectrometry / Q000379:methods* / Q000706:statistics & numerical data*
D010455:Peptides
Mass spectrometry; Microorganism classification; Pathogen identification; Proteomics; Statistical significance