Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Applying the digital data and the bioinformatics tools in SARS-CoV-2 research.

ABSTRACT: Bioinformatics has been playing a crucial role in the scientific progress to fight against the pandemic of the coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The advances in novel algorithms, mega data technology, artificial intelligence and deep learning assisted the development of novel bioinformatics tools to analyze daily increasing SARS-CoV-2 data in the past years. These tools were applied in genomic analyses, evolutionary tracking, epidemiological analyses, protein structure interpretation, studies in virus-host interaction and clinical performance. To promote the in-silico analysis in the future, we conducted a review which summarized the databases, web services and software applied in SARS-CoV-2 research. Those digital resources applied in SARS-CoV-2 research may also potentially contribute to the research in other coronavirus and non-coronavirus viruses.

SUBMITTER: Tan M

PROVIDER: S-EPMC10568291 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Applying the digital data and the bioinformatics tools in SARS-CoV-2 research.

Tan Meng M Xia Jiaxin J Luo Haitao H Meng Geng G Zhu Zhenglin Z

Computational and structural biotechnology journal 20231001

Bioinformatics has been playing a crucial role in the scientific progress to fight against the pandemic of the coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The advances in novel algorithms, mega data technology, artificial intelligence and deep learning assisted the development of novel bioinformatics tools to analyze daily increasing SARS-CoV-2 data in the past years. These tools were applied in genomic analyses, evolutionary tr ...[more]

PMID: 37841328

Similar Datasets

The impact of structural bioinformatics tools and resources on SARS-CoV-2 research and therapeutic strategies.

Project description:SARS-CoV-2 is the causative agent of COVID-19, the ongoing global pandemic. It has posed a worldwide challenge to human health as no effective treatment is currently available to combat the disease. Its severity has led to unprecedented collaborative initiatives for therapeutic solutions against COVID-19. Studies resorting to structure-based drug design for COVID-19 are plethoric and show good promise. Structural biology provides key insights into 3D structures, critical residues/mutations in SARS-CoV-2 proteins, implicated in infectivity, molecular recognition and susceptibility to a broad range of host species. The detailed understanding of viral proteins and their complexes with host receptors and candidate epitope/lead compounds is the key to developing a structure-guided therapeutic design. Since the discovery of SARS-CoV-2, several structures of its proteins have been determined experimentally at an unprecedented speed and deposited in the Protein Data Bank. Further, specialized structural bioinformatics tools and resources have been developed for theoretical models, data on protein dynamics from computer simulations, impact of variants/mutations and molecular therapeutics. Here, we provide an overview of ongoing efforts on developing structural bioinformatics tools and resources for COVID-19 research. We also discuss the impact of these resources and structure-based studies, to understand various aspects of SARS-CoV-2 infection and therapeutic development. These include (i) understanding differences between SARS-CoV-2 and SARS-CoV, leading to increased infectivity of SARS-CoV-2, (ii) deciphering key residues in the SARS-CoV-2 involved in receptor-antibody recognition, (iii) analysis of variants in host proteins that affect host susceptibility to infection and (iv) analyses facilitating structure-based drug and vaccine design against SARS-CoV-2.

| S-EPMC7799268 | biostudies-literature

Comparative analysis of bioinformatics tools to characterize SARS-CoV-2 subgenomic RNAs.

Project description:During the replication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), positive-sense genomic RNA and subgenomic RNAs (sgRNAs) are synthesized by a discontinuous process of transcription characterized by a template switch, regulated by transcription-regulating sequences (TRS). Although poorly known about makeup and dynamics of sgRNAs population and function of its constituents, next-generation sequencing approaches with the help of bioinformatics tools have made a significant contribution to expand the knowledge of sgRNAs in SARS-CoV-2. For this scope to date, Periscope, LeTRS, sgDI-tector, and CORONATATOR have been developed. However, limited number of studies are available to compare the performance of such tools. To this purpose, we compared Periscope, LeTRS, and sgDI-tector in the identification of canonical (c-) and noncanonical (nc-) sgRNA species in the data obtained with the Illumina ARTIC sequencing protocol applied to SARS-CoV-2-infected Caco-2 cells, sampled at different time points. The three software showed a high concordance rate in the identification and in the quantification of c-sgRNA, whereas more differences were observed in nc-sgRNA. Overall, LeTRS and sgDI-tector result to be adequate alternatives to Periscope to analyze Fastq data from sequencing platforms other than Nanopore.

| S-EPMC10520259 | biostudies-literature

Digital open science-Teaching digital tools for reproducible and transparent research.

Project description:An important hallmark of science is the transparency and reproducibility of scientific results. Over the last few years, internet-based technologies have emerged that allow for a representation of the scientific process that goes far beyond traditional methods and analysis descriptions. Using these often freely available tools requires a suite of skills that is not necessarily part of a curriculum in the life sciences. However, funders, journals, and policy makers increasingly require researchers to ensure complete reproducibility of their methods and analyses. To close this gap, we designed an introductory course that guides students towards a reproducible science workflow. Here, we outline the course content and possible extensions, report encountered challenges, and discuss how to integrate such a course in existing curricula.

| S-EPMC6095603 | biostudies-other

Applying mixture model methods to SARS-CoV-2 serosurvey data from Geneva.

Project description:Serosurveys are an important tool to estimate the true extent of the current SARS-CoV-2 pandemic. So far, most serosurvey data have been analyzed with cutoff-based methods, which dichotomize individual measurements into sero-positives or negatives based on a predefined cutoff. However, mixture model methods can gain additional information from the same serosurvey data. Such methods refrain from dichotomizing individual values and instead use the full distribution of the serological measurements from pre-pandemic and COVID-19 controls to estimate the cumulative incidence. This study presents an application of mixture model methods to SARS-CoV-2 serosurvey data from the SEROCoV-POP study from April and May 2020 in Geneva (2766 individuals). Besides estimating the total cumulative incidence in these data (8.1% (95% CI: 6.8%-9.9%)), we applied extended mixture model methods to estimate an indirect indicator of disease severity, which is the fraction of cases with a distribution of antibody levels similar to hospitalized COVID-19 patients. This fraction is 51.2% (95% CI: 15.2%-79.5%) across the full serosurvey, but differs between three age classes: 21.4% (95% CI: 0%-59.6%) for individuals between 5 and 40 years old, 60.2% (95% CI: 21.5%-100%) for individuals between 41 and 65 years old and 100% (95% CI: 20.1%-100%) for individuals between 66 and 90 years old. Additionally, we find a mismatch between the inferred negative distribution of the serosurvey and the validation data of pre-pandemic controls. Overall, this study illustrates that mixture model methods can provide additional insights from serosurvey data.

| S-EPMC9076579 | biostudies-literature

Generation of human bronchial organoids for SARS-CoV-2 research

Project description:Coronavirus disease 2019 (COVID-19) is a disease that causes fatal disorders including severe pneumonia. To develop a therapeutic drug for COVID-19, a model that can reproduce the viral life cycle and can evaluate the drug efficacy of anti-viral drugs is essential. In this study, we established a method to generate human bronchial organoids (hBO) from commercially available cryopreserved primary human bronchial epithelial cells (hBEpC) and examined whether they could be used as a model for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) research. The hBO were found to contain basal, club, ciliated, and goblet cells. Also, angiotensin-converting enzyme 2 (ACE2), which is a receptor for SARS-CoV-2, and transmembrane serine proteinase 2 (TMPRSS2), which is an essential serine protease for priming spike protein of SARS-CoV-2, were highly expressed. After hBO were infected with SARS-CoV-2, remarkable amplification of the viral genome and the expression of spike protein of the virus was confirmed. In addition, cytotoxicity and pyknosis cells were observed due to the virus infection. Furthermore, treatment with camostat, an inhibitor of TMPRSS2, reduced the viral copy number to 2% of the control group. RNA-seq analyses revealed genes whose expression was altered by SARS-CoV-2 infection and camostat treatment. These results suggest that our hBO are acceptable for SARS-CoV-2 infection and replication, but also can be used as a model for COVID-19 drug discovery.

2020-05-26 | GSE150819 | GEO

Computational strategies to combat COVID-19: useful tools to accelerate SARS-CoV-2 and coronavirus research.

Project description:SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) is a novel virus of the family Coronaviridae. The virus causes the infectious disease COVID-19. The biology of coronaviruses has been studied for many years. However, bioinformatics tools designed explicitly for SARS-CoV-2 have only recently been developed as a rapid reaction to the need for fast detection, understanding and treatment of COVID-19. To control the ongoing COVID-19 pandemic, it is of utmost importance to get insight into the evolution and pathogenesis of the virus. In this review, we cover bioinformatics workflows and tools for the routine detection of SARS-CoV-2 infection, the reliable analysis of sequencing data, the tracking of the COVID-19 pandemic and evaluation of containment measures, the study of coronavirus evolution, the discovery of potential drug targets and development of therapeutic strategies. For each tool, we briefly describe its use case and how it advances research specifically for SARS-CoV-2. All tools are free to use and available online, either through web applications or public code repositories. Contact:evbc@unj-jena.de.

| S-EPMC7665365 | biostudies-literature

SARS-CoV genome polymorphism: a bioinformatics study.

Project description:A dataset of 103 SARS-CoV isolates (101 human patients and 2 palm civets) was investigated on different aspects of genome polymorphism and isolate classification. The number and the distribution of single nucleotide variations (SNVs) and insertions and deletions, with respect to a "profile", were determined and discussed ("profile" being a sequence containing the most represented letter per position). Distribution of substitution categories per codon positions, as well as synonymous and non-synonymous substitutions in coding regions of annotated isolates, was determined, along with amino acid (a.a.) property changes. Similar analysis was performed for the spike (S) protein in all the isolates (55 of them being predicted for the first time). The ratio Ka/Ks confirmed that the S gene was subjected to the Darwinian selection during virus transmission from animals to humans. Isolates from the dataset were classified according to genome polymorphism and genotypes. Genome polymorphism yields to two groups, one with a small number of SNVs and another with a large number of SNVs, with up to four subgroups with respect to insertions and deletions. We identified three basic nine-locus genotypes: TTTT/TTCGG, CGCC/TTCAT, and TGCC/TTCGT, with four subgenotypes. Both classifications proposed are in accordance with the new insights into possible epidemiological spread, both in space and time.

| S-EPMC5172477 | biostudies-literature

Structures from "Engineering Neutralizing SARS-CoV Antibodies to Bind SARS-CoV-2"

Project description: Not available

| S-BSST379 | biostudies-other

Allocating Resources for Cancer Care in Times of the Sars-CoV-2 Outbreak. Health Care Research

Project description:The focus on treatment of COVID19 patients during the Sars-CoV-2 outbreak has most likely implications for the extent and quality of diagnosis and treatment of non-COVID19 patients. Medical care of cancer patients is a particularly sensitive area. To draw holistic conclusions, it is necessary to analyze the provision of healthcare as broadly as possible with regard to the dimensions of access, processes and outcome during the Sars-CoV-2 pandemic. This will be implemented exemplarily for the early detection, diagnosis and treatment of colorectal cancer (CRC) and pancreatic cancer (PaCa) in Saxony. Patients with diagnosis, treatment or early detection measures for type 2 diabetes (T2D), coronary heart disease (CHD) and multiple sclerosis (MS) serve as comparison groups.

| 2743388 | ecrin-mdr-crc

Reducing barriers to accessing administrative data on SARS-CoV-2 vaccination for research.

Project description: Not available

| S-EPMC9299746 | biostudies-literature