Despite the volume of experiments performed and data available, the complex biology of coronavirus SARS-COV-2 is not yet fully understood. Existing molecular profiling studies have focused on analysing functional omics data of a single type, which captures changes in a small subset of the molecular perturbations caused by the virus. As the logical next step, results from multiple such omics analysis may be aggregated to comprehensively interpret the molecular mechanisms of SARS-CoV-2. An alternative approach is to integrate data simultaneously in a parallel fashion to highlight the inter-relationships of disease-driving biomolecules, in contrast to comparing processed information from each omics level separately. We demonstrate that valuable information may be masked by using the former fragmented views in analysis, and biomarkers resulting from such an approach cannot provide a systematic understanding of the disease aetiology. Hence, we present a generic, reproducible and flexible open-access data harmonisation framework that can be scaled out to future multi-omics analysis to study a phenotype in a holistic manner. The pipeline source code, detailed documentation and automated version as a R package are accessible. To demonstrate the effectiveness of our pipeline, we applied it to a drug screening task. We integrated multi-omics data to find the lowest level of statistical associations between data features in two case studies. Strongly correlated features within each of these two datasets were used for drug–target analysis, resulting in a list of 84 drug–target candidates. Further computational docking and toxicity analyses revealed seven high-confidence targets, amsacrine, bosutinib, ceritinib, crizotinib, nintedanib and sunitinib as potential starting points for drug therapy and development.

Phenotypes are driven by regulated gene expression, which in turn are mediated by complex interactions between diverse biological molecules. Protein–DNA interactions such as histone and transcription factor binding are well studied, along with RNA–RNA interactions in short RNA silencing of genes. In contrast, lncRNA-protein interaction (LPI) mechanisms are comparatively unknown, likely directed by the difficulties in studying LPI. However, LPI are emerging as key interactions in epigenetic mechanisms, playing a role in development and disease. Their importance is further highlighted by their conservation across kingdoms. Hence, interest in LPI research is increasing. We therefore review the current state of the art in lncRNA-protein interactions. We specifically surveyed recent computational methods and databases which researchers can exploit for LPI investigation. We discovered that algorithm development is heavily reliant on a few generic databases containing curated LPI information. Additionally, these databases house information at gene-level as opposed to transcript-level annotations. We show that early methods predict LPI using molecular docking, have limited scope and are slow, creating a data processing bottleneck. Recently, machine learning has become the strategy of choice in LPI prediction, likely due to the rapid growth in machine learning infrastructure and expertise. While many of these methods have notable limitations, machine learning is expected to be the basis of modern LPI prediction algorithms.

Phenotypes are driven by regulated gene expression, which in turn are mediated by complex interactions between diverse biological molecules. Protein-DNA interactions such as histone and transcription factor binding are well studied, along with RNA-RNA interactions in short RNA silencing of genes. In contrast, lncRNA-protein interaction (LPI) mechanisms are comparatively unknown, likely driven by the difficulties in studying LPI. However, LPI are emerging as key interactions in epigenetic mechanism, playing a role in development and disease. Their importance is further highlighted by their conservation across kingdoms. Hence, interest in LPI research is increasing. We therefore review the current state of the art in lncRNA-protein interactions. We specifically surveyed recent computational methods and databases which researchers can exploit for LPI investigation. We discovered that algorithm development is heavily reliant on a few generic databases containing curated LPI information. We show that early methods predict LPI using molecular docking, have limited scope and are slow, creating a data processing bottleneck. Recently, machine learning has become the strategy of choice in LPI prediction, likely due to the rapid growth in machine learning infrastructure and expertise. While many of these methods have notable limitations, machine learning is expected to be the basis of modern LPI prediction algorithms.

Background: Coronavirus 2019 was declared as a pandemic by the World Health Organization in March 2020. Bereft of specific treatment for the disease, vaccinations and COVID appropriate behavior have come to be the main approaches to combat the pandemic. A number of vaccines have been approved after clearing clinical trials. Hence, it is essential to evaluate the safety profile of each vaccine for ensuring optimum health of the general population. This study was conducted to evaluate the adverse events following CoviShield vaccination in a tertiary care center. Aims and Objectives: The aim of the study was to describe the pattern of adverse effects, treatment given, and comorbidities seen in healthcare workers (HCW) who reported to the adverse drug reaction (ADR) monitoring center in the department of pharmacology Government T.D. Medical College, Alappuzha, following CoviShield vaccination from January 2021 to October 2021. Materials and Methods: A retrospective and descriptive study was carried out at Department of Pharmacology, GTDMCA involving all HCW who reported side effects following CoviShield vaccination in the ADR monitoring centre (AMC) in the Department of Pharmacology, GTDMCA from January 2021 to Oct 2021. Results: Out of 620 HCWs who reported adverse event following vaccination, majority (45%) were from the age group 21–30 years. About 83% of HCWs who reported adverse effect were women. Majority of the respondents (96%) experienced the adverse effects within 24 h. About 88% of respondents experienced these adverse effects after the initial dose alone. Commonly encountered adverse effects were fever (57%), headache (43%), myalgia (38%) etc. Hypertension (7%) was the most common comorbidity seen. Majority of the beneficiaries (70%) took paracetamol for the treatment of the adverse effect. Conclusion: Majority of the vaccinated HCWs experienced minor and self-limiting adverse event following immunization (AEFI) with Chimpanzee Adenovirus Oxford novel CoronaVirus-19. No serious AEFI were reported to the AMC. Despite the record speed at which the vaccine has been developed, it has shown to have a good safety profile considering the millions of doses that have been administered.

No abstract