Systems-level approaches require access to comprehensive\ud genome-wide and proteome-wide databases. A comprehensive\ud resource that archives all published protein-protein interactions\ud (PPIs) is not available. In fact, primary PPI databases capture\ud only a fraction of published data

Viral infections often cause diseases by perturbing several cellular processes in the infected host. Viral proteins target host proteins and either form new complexes or modulate the formation of functional host complexes. Describing and understanding the perturbation of the host interactome following viral infection is essential for basic virology and for the development of antiviral therapies. In order to provide a general overview of such interactions, a few years ago we developed VirusMINT. We have now extended the scope and coverage of VirusMINT and established VirusMentha, a new virus–virus and virus–host interaction resource build on the detailed curation protocols of the IMEx consortium and on the integration strategies developed for mentha. VirusMentha is regularly and automatically updated every week by capturing, via the PSICQUIC protocol, interactions curated by five different databases that are part of the IMEx consortium. VirusMentha can be freely browsed at http://virusmentha.uniroma2.it/ and its complete data set is available for download.

Recent advances in large datasets analysis offer new insights to modern biology allowing system-level investigation of pathologies. Here we describe a novel computational method that exploits the ever-growing amount of “omics” data to shed light on Alzheimer’s and Parkinson’s diseases. Neurological disorders exhibit a huge number of molecular alterations due to a complex interplay between genetic and environmental factors. Classical reductionist approaches are focused on a few elements, providing a narrow overview of the etiopathogenic complexity of multifactorial diseases. On the other hand, high-throughput technologies allow the evaluation of many components of biological systems and their behaviors. Analysis of Parkinson’s Disease (PD) and Alzheimer’s Disease (AD) from a network perspective can highlight proteins or pathways common but differently represented that can be discriminating between the two pathological conditions, thus highlight similarities and differences. In this work we propose a strategy that exploits network community structure identified with a state-of-the-art network community discovery algorithm called InfoMap, which takes advantage of information theory principles. We used two similarity measurements to quantify functional and topological similarities between the two pathologies. We built a Similarity Matrix to highlight similar communities and we analyzed statistically significant GO terms found in clustered areas of the matrix and in network communities. Our strategy allowed us to identify common known and unknown processes including DNA repair, RNA metabolism and glucose metabolism not detected with simple GO enrichment analysis. In particular, we were able to capture the connection between mitochondrial dysfunction and metabolism (glucose and glutamate/glutamine). This approach allows the identification of communities present in both pathologies which highlight common biological processes. Conversely, the identification of communities without any counterpart can be used to investigate processes that are characteristic of only one of the two pathologies. In general, the same strategy can be applied to compare any pair of biological networks.

Summary: The visualization of molecular interactions annotated in web resources is useful to offer to users such information in a clear intuitive layout. These interactions are frequently represented as binary interactions that are laid out in free space where, different entities, cellular compartments and interaction types are hardly distinguishable. Signaling Pathway Visualizer is a free open source JavaScript library, which offers a series of pre-defined elements, compartments and interaction types meant to facilitate the representation of signaling pathways consisting of causal interactions without neglecting simple protein–protein interaction networks. Availability and implementation: Freely available under Apache version 2 license; Source code: https://github.com/Sinnefa/SPV_Signaling_Pathway_Visualizer_v1.0. Language: JavaScript; Web technology: Scalable Vector Graphics; Libraries: D3.js.

Despite the large quantity of information available, thorough researches in various biological databases are still needed in order to reconstruct and understand the steps that lead to known or new phenomena. By using protein-protein interaction networks and algorithms to extract relevant interconnections among proteins of interest, it is possible to assemble subnetworks from global interactomes. Using these extracted networks it is possible to use algorithms to predict signal directions while activation and inhibition effects can be predicted using RNA interference screenings. The result of this approach is the automatic generation of boolean networks. This way of modelling dynamical systems allows the discovery of steady states and the prediction of stimuli response.

The amount of scientific papers published every day is daunting and constantly increasing. Keeping up with literature represents a challenge. If one wants to start exploring new topics it is hard to have a big picture without reading lots of articles. Furthermore, as one reads through literature, making mental connections is crucial to ask new questions which might lead to discoveries. In this work, I present a web tool which uses a Text Mining strategy to transform large collections of unstructured biomedical articles into structured data. Generated results give a quick overview on complex topics which can possibly suggest not explicitly reported information. In particular, I show two Data Science analyses. First, I present a literature based rare diseases network build using this tool in the hope that it will help clarify some aspects of these less popular pathologies. Secondly, I show how a literature based analysis conducted with PubSqueezer results allows to describe known facts about SARS-CoV-2. In one sentence, data generated with PubSqueezer make it easy to use scientific literate in any computational analysis such as machine learning, natural language processing etc. Availability: http://www.pubsqueezer.com