We have found evidence that points to the association of severe allergic inflammation with platelet functions alteration, together with reduced protein synthesis, and switch of immune cells to aerobic glycolysis.

Omics data integration is already a reality. However, few omics-based algorithms show enough predictive ability to be implemented into clinics or public health domains. Clinical/epidemiological data tend to explain most of the variation of health-related traits, and its joint modeling with omics data is crucial to increase the algorithm’s predictive ability. Only a small number of published studies performed a “real” integration of omics and non-omics (OnO) data, mainly to predict cancer outcomes. Challenges in OnO data integration regard the nature and heterogeneity of non-omics data, the possibility of integrating large-scale non-omics data with high-throughput omics data, the relationship between OnO data (i.e., ascertainment bias), the presence of interactions, the fairness of the models, and the presence of subphenotypes. These challenges demand the development and application of new analysis strategies to integrate OnO data. In this contribution we discuss different attempts of OnO data integration in clinical and epidemiological studies. Most of the reviewed papers considered only one type of omics data set, mainly RNA expression data. All selected papers incorporated non-omics data in a low-dimensionality fashion. The integrative strategies used in the identified papers adopted three modeling methods: Independent, conditional, and joint modeling. This review presents, discusses, and proposes integrative analytical strategies towards OnO data integration.

The emergence of high-throughput data in biology has increased the need for functional in silico analysis and prompted the development of integrative bioinformatics tools to facilitate the obtainment of biologically meaningful data. In this paper, we present DoriTool, a comprehensive, easy, and friendly pipeline integrating biological data from different functional tools. The tool was designed with the aim to maximize reproducibility and reduce the working time of the researchers, especially of those with limited bioinformatics skills, and to help them with the interpretation of the results. DoriTool is based upon an integrative strategy implemented following a modular design pattern. Using scripts written in Bash, Perl and R, it performs a functional in silico analysis annotation at mutation/variant level, gene level, pathway level and network level by combining up-to-date functional and genomic data and integrating also third-party bioinformatics tools in a pipeline. DoriTool uses GRCh37 human assembly and online mode. DoriTool provides nice visual reports including variant annotation, linkage disequilibrium proxies, gene annotation, gene ontology analysis, expression quantitative trait loci results from Genotype-Tissue Expression (GTEx) and coloured pathways. Here, we also show DoriTool functionalities by applying it to a dataset of 13 variants associated with prostate cancer. Project development, released code libraries, GitHub repository (https://github.com/doritool) and documentation are hosted at https://doritool.github.io/. DoriTool is, to our knowledge, the most complete bioinformatics tool offering functional in silico annotation of variants previously associated with a trait of interest, shedding light on the underlying biology and helping the researchers in the interpretation and discussion of the results.

To the Editor, Coronaviruses (CoV) are large, enveloped, positive-strand RNA viruses and until the first outbreak of SARS in 2002 had long been considered pathogens with low hospitalization incidence for healthy people. SARS-CoV-2 is a novel pathogenic CoV responsible for a new type of pneumonia. Initial reports placed the initial outbreak in Wuhan (China) in December 2019, and it has since spread