TY - JOUR A1 - Kuhring, M. A1 - Doellinger, J. A1 - Nitsche, A. A1 - Muth, Thilo A1 - Renard, B. Y. T1 - TaxIt: An Iterative Computational Pipeline for Untargeted Strain-Level Identification Using MS/MS Spectra from Pathogenic Single-Organism Samples N2 - Untargeted accurate strain-level classification of a priori unidentified organisms using tandem mass spectrometry is a challenging task. Reference databases often lack taxonomic depth, limiting peptide assignments to the species level. However, the extension with detailed strain information increases runtime and decreases statistical power. In addition, larger databases contain a higher number of similar proteomes. We present TaxIt, an iterative workflow to address the increasing search space required for MS/MS-based strain-level classification of samples with unknown taxonomic origin. TaxIt first applies reference sequence data for initial identification of species candidates, followed by automated acquisition of relevant strain sequences for low level classification. Furthermore, proteome similarities resulting in ambiguous taxonomic assignments are addressed with an abundance weighting strategy to increase the confidence in candidate taxa. For benchmarking the performance of our method, we apply our iterative workflow on several samples of bacterial and viral origin. In comparison to noniterative approaches using unique peptides or advanced abundance correction, TaxIt identifies microbial strains correctly in all examples presented (with one tie), thereby demonstrating the potential for untargeted and deeper taxonomic classification. TaxIt makes extensive use of public, unrestricted, and continuously growing sequence resources such as the NCBI databases and is available under open-source BSD license at https://gitlab.com/rki_bioinformatics/TaxIt. KW - Bioinformatics KW - Mass spectrometry KW - Microbial proteomics KW - Strain identification KW - MS/MS PY - 2020 U6 - https://doi.org/10.1021/acs.jproteome.9b00714 VL - 19 IS - 6 SP - 2501 EP - 2510 PB - ACS AN - OPUS4-50942 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Grossegesse, M. A1 - Hartkopf, F. A1 - Nitsche, A. A1 - Schaade, L. A1 - Doellinger, J. A1 - Muth, Thilo T1 - Perspective on Proteomics for Virus Detection in Clinical Samples N2 - One of the most widely used methods to detect an acute viral infection in clinical specimens is diagnostic real-time polymerase chain reaction. However, because of the COVID-19 pandemic, mass-spectrometry-based proteomics is currently being discussed as a potential diagnostic method for viral infections. Because proteomics is not yet applied in routine virus diagnostics, here we discuss its potential to detect viral infections. Apart from theoretical considerations, the current status and technical limitations are considered. Finally, the challenges that have to be overcome to establish proteomics in routine virus diagnostics are highlighted. KW - COVID-19 KW - Mass spectrometry KW - Virus diagnostics KW - Virus detection KW - Targeted mass spectrometry KW - Proteomics PY - 2020 U6 - https://doi.org/10.1021/acs.jproteome.0c00674 SN - 1535-3907 VL - 19 IS - 11 SP - 4380 EP - 4388 PB - ACS AN - OPUS4-51633 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -