Prioritization or ranking of different cell types in a single-cell RNA sequencing (scRNA-seq) framework can be performed in a variety of ways, some of these include: i) obtaining an indication of the proportion of cell types between the different conditions under study, ii) counting the number of differentially expressed genes (DEGs) between cell types and conditions in the experiment or, iii) prioritizing cell types based on prior knowledge about the conditions under study (i.e., a specific disease). These methods have drawbacks and limitations thus novel methods for improving cell ranking are required. Here we present a novel methodology that exploits prior knowledge in combination with expert-user information to accentuate cell types from a scRNA-seq analysis that yield the most biologically meaningful results with respect to a disease under study. Our methodology allows for ranking and prioritization of cell types based on how well their expression profiles relate to the molecular mechanisms and drugs associated with a disease. Molecular mechanisms, as well as drugs, are incorporated as prior knowledge in a standardized, structured manner. Cell types are then ranked/prioritized based on how well results from data-driven analysis of scRNA-seq data match the predefined prior knowledge. In additional cell-cell communication perturbations between disease and control networks are used to further prioritize/rank cell types. Our methodology has substantial advantages to more traditional cell ranking techniques and provides an informative complementary methodology that utilizes prior knowledge in a rapid and automated manner, that has previously not been attempted by other studies. The current methodology is also implemented as an R package entitled Single Cell Ranking Analysis Toolkit (scRANK) and is available for download and installation via GitHub (https://github.com/aoulas/scRANK).

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic is undeniably the most severe global health emergency since the 1918 Influenza outbreak. Depending on its evolutionary trajectory, the virus is expected to establish itself as an endemic infectious respiratory disease exhibiting seasonal flare-ups. Therefore, despite the unprecedented rally to reach a vaccine that can offer widespread immunization, it is equally important to reach effective prevention and treatment regimens for coronavirus disease 2019 (COVID-19). Contributing to this effort, we have curated and analyzed multi-source and multi-omics publicly available data from patients, cell lines and databases in order to fuel a multiplex computational drug repurposing approach. We devised a network-based integration of multi-omic data to prioritize the most important genes related to COVID-19 and subsequently re-rank the identified candidate drugs. Our approach resulted in a highly informed integrated drug shortlist by combining structural diversity filtering along with experts' curation and drug-target mapping on the depicted molecular pathways. In addition to the recently proposed drugs that are already generating promising results such as dexamethasone and remdesivir, our list includes inhibitors of Src tyrosine kinase (bosutinib, dasatinib, cytarabine and saracatinib), which appear to be involved in multiple COVID-19 pathophysiological mechanisms. In addition, we highlight specific immunomodulators and anti-inflammatory drugs like dactolisib and methotrexate and inhibitors of histone deacetylase like hydroquinone and vorinostat with potential beneficial effects in their mechanisms of action. Overall, this multiplex drug repurposing approach, developed and utilized herein specifically for SARS-CoV-2, can offer a rapid mapping and drug prioritization against any pathogen-related disease.

MOTIVATION: Understanding the underlying biological mechanisms and respective interactions of a disease remains an elusive, time consuming and costly task. Computational methodologies that propose pathway/mechanism communities and reveal respective relationships can be of great value as they can help expedite the process of identifying how perturbations in a single pathway can affect other pathways. RESULTS: We present a random-walks-based methodology called PathWalks, where a walker crosses a pathway-to-pathway network under the guidance of a disease-related map. The latter is a gene network that we construct by integrating multi-source information regarding a specific disease. The most frequent trajectories highlight communities of pathways that are expected to be strongly related to the disease under study.We apply the PathWalks methodology on Alzheimer's disease and idiopathic pulmonary fibrosis and establish that it can highlight pathways that are also identified by other pathway analysis tools as well as are backed through bibliographic references. More importantly, PathWalks produces additional new pathways that are functionally connected with those already established, giving insight for further experimentation. AVAILABILITY AND IMPLEMENTATION: https://github.com/vagkaratzas/PathWalks. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Systems Bioinformatics is a relatively new approach, which lies in the intersection of systems biology and classical bioinformatics. It focuses on integrating information across different levels using a bottom-up approach as in systems biology with a data-driven top-down approach as in bioinformatics. The advent of omics technologies has provided the stepping-stone for the emergence of Systems Bioinformatics. These technologies provide a spectrum of information ranging from genomics, transcriptomics and proteomics to epigenomics, pharmacogenomics, metagenomics and metabolomics. Systems Bioinformatics is the framework in which systems approaches are applied to such data, setting the level of resolution as well as the boundary of the system of interest and studying the emerging properties of the system as a whole rather than the sum of the properties derived from the system's individual components. A key approach in Systems Bioinformatics is the construction of multiple networks representing each level of the omics spectrum and their integration in a layered network that exchanges information within and between layers. Here, we provide evidence on how Systems Bioinformatics enhances computational therapeutics and diagnostics, hence paving the way to precision medicine. The aim of this review is to familiarize the reader with the emerging field of Systems Bioinformatics and to provide a comprehensive overview of its current state-of-the-art methods and technologies. Moreover, we provide examples of success stories and case studies that utilize such methods and tools to significantly advance research in the fields of systems biology and systems medicine.

SUMMARY: PathwayConnector is a web-tool that facilitates the construction of complementary pathway-to-pathway networks and subnetworks of them, based on a reference pathway network derived from the rich information available either in KEGG or Reactome database for pathway mapping. Specifically, for a given set of pathways, PathwayConnector (i) finds all the direct connections between them, (ii) adds a minimum set of complementary pathways required to achieve connectivity between the pathways, leading to informative fully connected networks and (ii) provides a series of clustering methods for the further grouping of pathways in to sub-clusters. The proposed web-tool is a simple yet informative tool towards identifying connected groups of pathways that are significantly related to specific diseases. AVAILABILITY AND IMPLEMENTATION: http://bioinformatics.cing.ac.cy/PathwayConnector. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The advancement of scientific and medical research over the past years has generated a wealth of experimental data from multiple technologies, including genomics, transcriptomics, proteomics, and other forms of -omics data, which are available for a number of diseases. The integration of such multisource data is a key component toward the success of precision medicine. In this paper, we are investigating a multisource data integration method developed by our group, regarding its ability to drive to clusters of connected pathways under two different approaches: first, a disease-centric approach, where we integrate data around a disease, and second, a gene-centric approach, where we integrate data around a gene. We have used as a paradigm for the first approach Huntington's disease (HD), a disease with a plethora of available data, whereas for the second approach the GBA2, a gene that is related to spastic ataxia (SA), a phenotype with sparse availability of data. Our paper shows that valuable information at the level of disease-related pathway clusters can be obtained for both HD and SA. New pathways that classical pathway analysis methods were unable to reveal, emerged as necessary "connectors" to build connected pathway stories formed as pathway clusters. The capability to integrate multisource molecular data, concluding to something more than the sum of the existing information, empowers precision and personalized medicine approaches.

For almost three decades, cancer was thought to result from changes in the structure and/or expression of protein coding genes. The discovery of thousands of genes that produce noncoding RNA (ncRNA) transcripts in the past few years suggested that the molecular biology of cancer is much more complex. MicroRNAs (miRNAs), an important group of ncRNAs, have recently been associated with tumorigenesis by acting either as tumor suppressors or oncogenes. Experimental prediction of miRNA genes is a slow process, because of the difficulties of cloning ncRNAs. Complementary to experimental approaches, a number of computational tools trained to recognize features of the biogenesis of miRNAs have significantly aided in the prediction of new miRNA candidates. By narrowing down the search space, computational approaches provide valuable clues as to which are the dominant features that characterize these regulatory units and which genes are their most likely targets. Moreover, through the use of high-throughput expression profiling methods, many molecular signatures of miRNA deregulation in human tumors have emerged. In this review, we present an overview of existing computational methods for identifying miRNA genes and assessing their expression levels, and analyze the contribution of such tools toward illuminating the role of miRNAs in cancer.