The coronavirus disease 2019 (COVID-19) has resulted as a global pandemic. The World Health Organization announced the most promising drugs in SOLIDARITY for the global trial, and several other drugs are under investigation through ongoing clinical trials to prove the effectiveness and safety of potential therapeutics. Here, we depicted the safety profile of these drugs and investigated their associated adverse events (AEs). We observed the associated AEs in different organs/systems, especially in skin and subcutaneous tissue, immune system and musculoskeletal and connective tissue. Furthermore, we observed strong bias of AEs in different groups of sex and age. Our study provides knowledge of the toxicity of potential COVID-19 drugs. While these drugs hold promise to fight the global pandemic, healthcare providers should pay attention to AEs to maximize the treatment benefit while minimizing toxicity.

Circular RNAs (circRNAs) are novel rising stars of noncoding RNAs, which are highly abundant and evolutionarily conserved across species. Number of publications related to circRNAs increased sharply in recent years, representing emerging focuses in the field. Therefore, tools, pipelines and databases have been developed to identify and store circRNAs. However, there is no existing tool to visualize and explore circRNAs. Therefore, we introduce CircView, a user-friendly visualization tool for circRNAs detected from existing tools. CircView enables users to visualize circRNAs and to quantify number of samples with detected circRNAs. CircView allows users to explore circRNAs detected by unique or multiple tools. Furthermore, CircView allows users to view the regulatory elements, such as microRNA response elements and RNA-binding protein binding sites. CircView is a unique tool to visualize and explore circRNAs, which helps users to better understand potential functions of circRNAs and design the functional experiments.

Abstract Motivation: Nonlinear dose–response models are primary tools for estimating the potency [e.g. half-maximum inhibitory concentration (IC) known as IC50] of anti-cancer drugs. We present drexplorer software, which enables biologists to evaluate replicate reproducibility, detect outlier data points, fit different models, select the best model, estimate IC values at different percentiles and assess drug–drug interactions. drexplorer serves as a computation engine within the R environment and a graphical interface for users who do not have programming backgrounds. Availability and implementation: The drexplorer R package is freely available from GitHub at https://github.com/nickytong/drexplorer. A graphical user interface is shipped with the package. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

MOTIVATION: Tissue samples of tumor cells mixed with stromal cells cause underdetection of gene expression signatures associated with cancer prognosis or response to treatment. In silico dissection of mixed cell samples is essential for analyzing expression data generated in cancer studies. Currently, a systematic approach is lacking to address three challenges in computational deconvolution: (i) violation of linear addition of expression levels from multiple tissues when log-transformed microarray data are used; (ii) estimation of both tumor proportion and tumor-specific expression, when neither is known a priori; and (iii) estimation of expression profiles for individual patients. RESULTS: We have developed a statistical method for deconvolving mixed cancer transcriptomes, DeMix, which addresses the aforementioned issues in array-based expression data. We demonstrate the performance of our model in synthetic and real, publicly available, datasets. DeMix can be applied to ongoing biomarker-based clinical studies and to the vast expression datasets previously generated from mixed tumor and stromal cell samples. AVAILABILITY: All codes are written in C and integrated into an R function, which is available at http://odin.mdacc.tmc.edu/∼wwang7/DeMix.html. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.