An iterative and automated computational pipeline for untargeted strain-level identification using MS/MS spectra from pathogenic samples

Mathias Kuhring; Joerg Doellinger; Andreas Nitsche; Thilo Muth; Bernhard Y. Renard

information given is often limited to species or higher taxonomic levels. In addition, the approaches rely on fully sequenced genomes (and thus uniform proteome coverage) or other forms of targeted, curated, inhouse or validated databases and are thus restricted to a predefined limited set of organisms, often with definite focus on bacteria. Furthermore, some methods do not scale well with immoderate large databases (i.e. high number of proteomes) with respect to computational or analytical performance. Previous multi-step procedures illustrate the effect of database size on identification confidence and the advantage of applying concise, prefiltered or specialized databases. Thus, we transfer the general concept of multi-step procedures to approach the increased search space necessary for untargeted and detailed taxonomic classification. We present TaxIt, an iterative workflow for untargeted strain-level identification of protein samples. By applying two separate identification steps for species-and strain-level classification, we circumvent the immediate need for a comprehensive database containing strain All rights reserved. No reuse allowed without permission.