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

The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. . https://doi.org/10.1101/812313 doi: bioRxiv preprint proteome sequences. Thereby, a first untargeted search allows the selection of a relevant species and enables to focus a second search on a highly reduced but adequate choice of automatically downloaded strain proteomes, resulting in increased identification confidence and reduced taxonomic ambiguity. Moreover, strain-level identification relies on recent data, without the need of keeping a local comprehensive strain database up-to-date. The workflow takes advantage of a free, publicly available and continuously growing protein sequence resource (NCBI Protein) and is thus suitable for most common established MS experimental workflows.