Alves Gelio, Wang Guanghui, Ogurtsov Aleksey Y, Drake Steven K, Gucek Marjan, Suffredini Anthony F, Sacks David B, Yu Yi-Kuo
National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA.
Proteomics Core, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, 20892, USA.
J Am Soc Mass Spectrom. 2016 Feb;27(2):194-210. doi: 10.1007/s13361-015-1271-2. Epub 2015 Oct 28.
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.
正确且快速地鉴定微生物是健康与安全领域许多重要应用取得成功的关键，这些应用包括但不限于感染治疗、食品安全和生物防御。随着质谱（MS）技术的进步，鉴定速度可大幅提高。然而，由于可供选择的微生物数量众多，测序微生物数量的增加给正确的微生物鉴定带来了挑战。为了正确区分候选微生物，需要超越表面形态或简单的“指纹识别”；为了正确地对候选微生物进行优先级排序，需要在微生物鉴定中具有准确的统计显著性。我们通过利用微生物的肽组图谱来更好地分离它们，并设计一种能产生准确统计显著性的分析方法来应对这些挑战。在此，我们展示了一种使用串联质谱（MS/MS）谱进行微生物鉴定或分类的分析流程。我们利用81个样本的MS/MS数据（每个样本由单一已知微生物组成）证明，所提出的流程至少能在属和种水平上正确鉴定微生物。我们还表明，所提出的流程能计算出准确的统计显著性，即鉴定肽段的E值和鉴定微生物的统一E值。所提出的分析流程已在MiCId中实现，MiCId是一款免费提供的用于微生物分类和鉴定的软件。可从http://www.ncbi.nlm.nih.gov/CBBresearch/Yu/downloads.html下载MiCId。图形摘要ᅟ
