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Motivation: Inferring taxonomy in mass spectrometry-based shotgun proteomics is a complex task. In multi-species or viral samples of unknown taxonomic origin, the presence of proteins and corresponding taxa must be inferred from a list of identified peptides, which is often complicated by protein homology: many proteins do not only share peptides within a taxon but also between taxa. However, the correct taxonomic inference is crucial when identifying different viral strains with high-sequence homology—considering, e.g., the different epidemiological characteristics of the various strains of severe acute respiratory syndrome-related coronavirus-2. Additionally, many viruses mutate frequently, further complicating the correct identification of viral proteomic samples.
Results: We present PepGM, a probabilistic graphical model for the taxonomic assignment of virus proteomic samples with strain-level resolution and associated confidence scores. PepGM combines the results of a standard proteomic database search algorithm with belief propagation to calculate the marginal distributions, and thus confidence scores, for potential taxonomic assignments. We demonstrate the performance of PepGM using several publicly available virus proteomic datasets, showing its strain-level resolution performance. In two out of eight cases, the taxonomic assignments were only correct on the species level, which PepGM clearly indicates by lower confidence scores.
Availability and implementation: PepGM is written in Python and embedded into a Snakemake workflow. It is available at https://github.com/BAMeScience/PepGM.
Bees are considered to be threatened globally, with severe overwinter losses of the most important commercial pollinator, the Western honeybee, a major concern in the Northern Hemisphere. Emerging infectious diseases have risen to prominence due to their temporal correlation with colony losses. Among these is Deformed wing virus (DWV), which has been frequently linked to colony mortality. We now provide evidence of a strong statistical association between overwintering colony decline in the field and the presence of DWV genotype-B (DWV-B), a genetic variant of DWV that has recently been shown to be more virulent than the original DWV genotype-A. We link the prevalence of DWV-B directly to a quantitative measure of overwinter decline (workforce mortality) of honeybee colonies in the field. We demonstrate that increased prevalence of virus infection in individual bees is associated with higher overwinter mortality. We also observed a substantial reduction of infected colonies in the spring, suggesting that virus-infected individuals had died during the winter. Our findings demonstrate that DWV-B, plus possible A/B recombinants exhibiting DWV-B at PCR primer binding sites, may be a major cause of elevated overwinter honeybee loss. Its potential emergence in naïve populations of bees may have far-reaching ecological and economic impacts.