Comprehensive analyses of bioinformatics applications in the fight against COVID-19 pandemic.

Novel coronavirus disease 2019 (COVID-19) is a global pandemic caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), which can be transmitted from person to person. As of September 21, 2021, over 228 million cases were diagnosed as COVID-19 infection in more than 200 countries and regions worldwide. The death toll is more than 4.69 million and the mortality rate has reached about 2.05% as it has gradually become a global plague, and the numbers are growing. Therefore, it is important to gain a deeper understanding of the genome and protein characteristics, clinical diagnostics, pathogenic mechanisms, and the development of antiviral drugs and vaccines against the novel coronavirus to deal with the COVID-19 pandemic. The traditional biology technologies are limited for COVID-19-related studies to understand the pandemic happening. Bioinformatics is the application of computational methods and analytical tools in the field of biological research which has obvious advantages in predicting the structure, product, function, and evolution of unknown genes and proteins, and in screening drugs and vaccines from a large amount of sequence information. Here, we comprehensively summarized several of the most important methods and applications relating to COVID-19 based on currently available reports of bioinformatics technologies, focusing on future research for overcoming the virus pandemic. Based on the next-generation sequencing (NGS) and third-generation sequencing (TGS) technology, not only virus can be detected, but also high quality SARS-CoV-2 genome could be obtained quickly. The emergence of data of genome sequences, variants, haplotypes of SARS-CoV-2 help us to understand genome and protein structure, variant calling, mutation, and other biological characteristics. After sequencing alignment and phylogenetic analysis, the bat may be the natural host of the novel coronavirus. Single-cell RNA sequencing provide abundant resource for discovering the mechanism of immune response induced by COVID-19. As an entry receptor, angiotensin-converting enzyme 2 (ACE2) can be used as a potential drug target to treat COVID-19. Molecular dynamics simulation, molecular docking and artificial intelligence (AI) technology of bioinformatics methods based on drug databases for SARS-CoV-2 can accelerate the development of drugs. Meanwhile, computational approaches are helpful to identify suitable vaccines to prevent COVID-19 infection through reverse vaccinology, Immunoinformatics and structural vaccinology.

Spatial and Temporal Distributions of Air Pollutants in Nanchang, Southeast China during 2017–2020

In response to COVID-19 in December 2019, China imposed a strict lockdown for the following two months, which led to an unprecedented reduction in industrial activities and transportation. However, haze pollution was still recorded in many Chinese cities during the lockdown period. To explore temporal and spatial variations in urban haze pollution, concentrations of air pollutants (PM2.5, PM10, SO2, CO, NO, NO2, and O3) from April 2017 to March 2020 were observed at 23 monitoring stations throughout Nanchang City (including one industrial site, sixteen urban central sites, two mountain sites, and four suburban sites). Overall, the highest concentrations of PM2.5, PM10, and SO2 were observed at industrial sites and the highest CO and NOx (NO and NO2) concentrations were recorded at urban sites. The air pollutants at mountain sites all showed the lowest concentrations, which indicated that anthropogenic activities are largely responsible for air pollutants. Concentrations of PM2.5, PM10, CO, NO, and NO2 showed similar season trends, that is, the highest levels in winter and lowest concentrations in summer, but an opposite season pattern for O3. Except for a sharply dropping pattern from January to May 2018, there were no seasonal patterns for SO2 concentration in all the observed sites. Daily PM2.5, PM10, CO, NOx, and SO2 concentrations showed a peak during the morning commute, which indicated the influences of anthropogenic activities on PM2.5, PM10, CO, NOx, and SO2. PM2.5, PM10, NOx, and CO concentrations at industrial, urban, and suburban sites were higher during nighttime than during daytime, but they showed the opposite pattern at mountain sites. In addition, PM2.5, PM10, CO, and NOx concentrations were lower during the lockdown period (D2) than those before the lockdown (B1). After the lockdown was lifted (A3), PM2.5, PM10, CO, and NOx concentrations showed a slowly increasing trend. However, O3 concentrations continuously increased from B1 to A3.