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TaxIt: An Iterative Computational Pipeline for Untargeted Strain-Level Identification Using MS/MS Spectra from Pathogenic Single-Organism Samples

  • 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 ourUntargeted 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.zeige mehrzeige weniger

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Metadaten
Autoren/innen:M. Kuhring, J. Doellinger, A. Nitsche, Thilo Muth, B. Y. Renard
Dokumenttyp:Zeitschriftenartikel
Veröffentlichungsform:Verlagsliteratur
Sprache:Englisch
Titel des übergeordneten Werkes (Englisch):Journal of Proteome Research
Jahr der Erstveröffentlichung:2020
Organisationseinheit der BAM:S Qualitätsinfrastruktur
S Qualitätsinfrastruktur / S.3 eScience
Verlag:ACS
Jahrgang/Band:19
Ausgabe/Heft:6
Erste Seite:2501
Letzte Seite:2510
DDC-Klassifikation:Naturwissenschaften und Mathematik / Chemie / Analytische Chemie
Freie Schlagwörter:Bioinformatics; MS/MS; Mass spectrometry; Microbial proteomics; Strain identification
Themenfelder/Aktivitätsfelder der BAM:Analytical Sciences
Analytical Sciences / Spurenanalytik und chemische Zusammensetzung
DOI:https://doi.org/10.1021/acs.jproteome.9b00714
Verfügbarkeit des Dokuments:Datei im Netzwerk der BAM verfügbar ("Closed Access")
Datum der Freischaltung:29.06.2020
Referierte Publikation:Ja
Datum der Eintragung als referierte Publikation:29.06.2020