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

Abstract

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 improve candidate confidence. 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 license at https://gitlab.com/rki_bioinformatics.

Perspective on Proteomics for Virus Detection in Clinical Samples

“…However, some of them can still be directly applied to virus samples. For example, the recently published taxonomic identification tool TaxIt 34 was also tested on virus samples (e.g., cowpoxvirus and adenovirus strains) that have been measured by shotgun proteomics. TaxIt aims to overcome the problem of inaccurate species and strain resolution when proteomics data are searched against reference databases that lack taxonomic depth.…”

PepGM: A probabilistic graphical model for taxonomic inference of viral proteome samples with associated confidence scores

“…For untargeted identification of viral samples based on MS2 spectra only few bioinformatic workflows exist [19][20][21][22] and these focus on the analysis of samples with known organism source. 23 For the identification of virus strains of unknown taxonomic origin, specific challenges emerge.…”

“…This results in taxonomic identifications without confidence estimates and is inappropriate when strain-level resolution is required, as can be the case in clinical settings, where disease severity and therapeutic decisions strongly depend on strain information. A different approach, TaxIt, 19 reduces the search space while still taking into account as many reference proteomes as possible through an iterative approach that uses multiple identification steps. 19 Amongst all mentioned workflows, it is the most apt at achieving strain-level resolution, but lacks confidence estimates for its taxonomic assignments.…”

“…A different approach, TaxIt, 19 reduces the search space while still taking into account as many reference proteomes as possible through an iterative approach that uses multiple identification steps. 19 Amongst all mentioned workflows, it is the most apt at achieving strain-level resolution, but lacks confidence estimates for its taxonomic assignments.…”

The proteomics contribution to the counter-bioterrorism toolbox in the post-COVID-19 era

“…Figure 2 shows how taxonomic information can be derived from the peptides identified. Several bioinformatics programs have been proposed to handle the proteomics data garnered, and to produce lists of the agents present based on their discriminant peptides [15][16][17]. A recent modification, phylopeptidomics, proposed the use of discriminant and non-discriminant peptides to avoid inflation of annotated genome data which tends to limit the list of available discriminant peptides.…”