The emergence of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which is the etiologic agent of the coronavirus disease 2019 (COVID-19) pandemic, has dominated all aspects of life for the better part of 2020 and into 2021. Research studies on the virus and exploration of therapeutic and preventive strategies has been moving at rapid rates to control the pandemic. In the field of computational and structural biology, recent research strategies have used multiple disciplines to compile large datasets to uncover statistical correlations and significance, visualize and model proteins, perform molecular dynamics simulations, and employ the help of artificial intelligence and machine learning to harness computational processing power to further the research on COVID-19, including drug screening, drug design, vaccine development, prognosis prediction, and outbreak prediction. These recent developments should help us better understand the viral disease and develop the much-needed therapies and strategies for the management of COVID-19.

Cancer genomics has been evolving rapidly, fueled by the emergence of numerous studies and public databases through next-generation sequencing technologies. However, the downstream programs used to preprocess and analyze data on somatic mutations are scattered in different tools, most of which require specific input formats. Here, we developed a user-friendly Python toolkit, MutScape, which provides a comprehensive pipeline of filtering, combination, transformation, analysis and visualization for researchers, to easily explore the cohort-based mutational characterization for studying cancer genomics when obtaining somatic mutation data. MutScape not only can preprocess millions of mutation records in a few minutes, but also offers various analyses simultaneously, including driver gene detection, mutational signature, large-scale alteration identification and actionable biomarker annotation. Furthermore, MutScape supports somatic variant data in both variant call format and mutation annotation format, and leverages caller combination strategies to quickly eliminate false positives. With only two simple commands, robust results and publication-quality images are generated automatically. Herein, we demonstrate the ability of MutScape to correctly reproduce known results using breast cancer samples from The Cancer Genome Atlas. More significantly, discovery of novel results in cancer genomic studies is enabled through the advanced features in MutScape. MutScape is freely available on GitHub, at https://github.com/anitalu724/MutScape.