Summary Many methods allow us to extract biological activities from omics data using information from prior knowledge resources, reducing the dimensionality for increased statistical power and better interpretability. Here, we present decoupleR, a Bioconductor and Python package containing computational methods to extract these activities within a unified framework. decoupleR allows us to flexibly run any method with a given resource, including methods that leverage mode of regulation and weights of interactions, which are not present in other frameworks. Moreover, it leverages OmniPath, a meta-resource comprising over 100 databases of prior knowledge. Using decoupleR, we evaluated the performance of methods on transcriptomic and phospho-proteomic perturbation experiments. Our findings suggest that simple linear models and the consensus score across top methods performs better than other methods at predicting perturbed regulators. Availability and Implementation decoupleR’s open source code is available in Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/decoupleR.html) for R and in GitHub (https://github.com/saezlab/decoupler-py) for Python. The code to reproduce the results is in GitHub (https://github.com/saezlab/decoupleR_manuscript) and the data in Zenodo (https://zenodo.org/record/5645208).

SummaryMany methods allow us to extract biological activities from omics data using information from prior knowledge resources, reducing the dimensionality for increased statistical power and better interpretability. Here, we present decoupleR, a Bioconductor package containing computational methods to extract these activities within a unified framework. decoupleR allows us to flexibly run any method with a given resource, including methods that leverage mode of regulation and weights of interactions. Using decoupleR, we evaluated the performance of methods on transcriptomic and phospho-proteomic perturbation experiments. Our findings suggest that simple linear models and the consensus score across methods perform better than other methods at predicting perturbed regulators.Availability and ImplementationdecoupleR is open source available in Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/decoupleR.html). The code to reproduce the results is in Github (https://github.com/saezlab/decoupleR_manuscript) and the data in Zenodo (https://zenodo.org/record/5645208).ContactJulio Saez-Rodriguez at pub.saez@uni-heidelberg.de.

Human norovirus infections are a major disease burden. In this study, we analyzed three new norovirus-specific Nanobodies that interacted with the prototype human norovirus (i.e., genogroup I genotype 1 [GI.1]). We showed that the Nanobodies bound on the side (Nano-7 and Nano-62) and top (Nano-94) of the capsid-protruding (P) domain using X-ray crystallography. Nano-7 and Nano-62 bound at a similar region on the P domain, but the orientations of these two Nanobodies clashed with the shell (S) domain and neighboring P domains on intact particles. This finding suggested that the P domains on the particles should shift in order for Nano-7 and Nano-62 to bind to intact particles. Interestingly, both Nano-7 and Nano-94 were capable of blocking norovirus virus-like particles (VLPs) from binding to histo-blood group antigens (HBGAs), which are important cofactors for norovirus infection. Previously, we showed that the GI.1 HBGA pocket could be blocked with the soluble human milk oligosaccharide 2-fucosyllactose (2′FL). In the current study, we showed that a combined treatment of Nano-7 or Nano-94 with 2′FL enhanced the blocking potential with an additive (Nano-7) or synergistic (Nano-94) effect. We also found that GII Nanobodies with 2′FL also enhanced inhibition. The Nanobody inhibition likely occurred by different mechanisms, including particle aggregation or particle disassembly, whereas 2′FL blocked the HBGA binding site. Overall, these new data showed that the positive effect of the addition of 2′FL was not limited to a single mode of action of Nanobodies or to a single norovirus genogroup. IMPORTANCE The discovery of vulnerable regions on norovirus particles is instrumental in the development of effective inhibitors, particularly for GI noroviruses that are genetically diverse. Analysis of these GI.1-specific Nanobodies has shown that similar to GII norovirus particles, the GI particles have vulnerable regions. The only known cofactor region, the HBGA binding pocket, represents the main target for inhibition. With a combination treatment, i.e., the addition of Nano-7 or Nano-94 with 2′FL, the effect of inhibition was increased. Therefore, combination drug treatments might offer a better approach to combat norovirus infections, especially since the GI genotypes are highly diverse and are continually changing the capsid landscape, and few conserved epitopes have so far been identified.