COVID-19, caused by SARS-CoV-2 infection, has already reached pandemic proportions in a matter of a few weeks. At the time of writing this manuscript, the unprecedented public health crisis caused more than 2.5 million cases with a mortality range of 5-7%. The SARS-CoV-2, also called novel Coronavirus, is related to both SARS-CoV and bat SARS. Great efforts have been spent to control the pandemic that has become a significant burden on the health systems in a short time. Since the emergence of the crisis, a great number of researchers started to use the AI tools to identify drugs, diagnosing using CT scan images, scanning body temperature, and classifying the severity of the disease. The emergence of variants of the SARS-CoV-2 genome is a challenging problem with expected serious consequences on the management of the disease. Here, we introduce COVIDier, a deep learning-based software that is enabled to classify the different genomes of Alpha coronavirus, Beta coronavirus, MERS, SARS-CoV-1, SARS-CoV-2, and bronchitis-CoV. COVIDier was trained on 1925 genomes, belonging to the three families of SARS retrieved from NCBI Database to propose a new method to train deep learning model trained on genome data using Multi-layer Perceptron Classifier (MLPClassifier), a deep learning algorithm, that could blindly predict the virus family name from the genome of by predicting the statistically similar genome from training data to the given genome. COVIDier able to predict how close the emerging novel genomes of SARS to the known genomes with accuracy 99%. COVIDier can replace tools like BLAST that consume higher CPU and time.
Nipah virus (NiV) is a zoonotic paramyxovirus of the Henipavirus genus first identified in Malaysia in 1998. Henipavirus have bat reservoir hosts and have been isolated from fruit bats found across Oceania, Asia, and Africa. Bat-to-human transmission is thought to be the primary mode of human NiV infection, although multiple intermediate hosts are described. Human infections with NiV were originally described as a syndrome of fever and rapid neurological decline following contact with swine. More recent outbreaks describe a syndrome with prominent respiratory symptoms and human-to-human transmission. Nearly annual outbreaks have been described since 1998 with case fatality rates reaching greater than 90%. To prevent the spreading of the Nipah virus and turning it into a new pandemic, we must be armed with a ready-made vaccine to save the time consuming that vaccine takes until production. Here we in this paper, we analyzed the whole Nipah virus proteome to find out the most antigenic, non-allergic, and immune inducing epitopes to construct different vaccines that undergone deep investigation to reveal the most appropriate vaccine to immunize humanity from this probably pandemic.
Since its emergence as a pandemic in March 2020, coronavirus disease (COVID-19) outcome has been explored via several predictive models, using specific clinical or biochemical parameters. In the current study, we developed an integrative non-linear predictive model of COVID-19 outcome, using clinical, biochemical, immunological, and radiological data of patients with different disease severities. Initially, the immunological signature of the disease was investigated through transcriptomics analysis of nasopharyngeal swab samples of patients with different COVID-19 severity versus control subjects (exploratory cohort, n=61), identifying significant differential expression of several cytokines. Accordingly, 24 cytokines were validated using a multiplex assay in the serum of COVID-19 patients and control subjects (validation cohort, n=77). Predictors of severity were Interleukin (IL)-10, Programmed Death-Ligand-1 (PDL-1), Tumor necrosis factors-α, absolute neutrophil count, C-reactive protein, lactate dehydrogenase, blood urea nitrogen, and ferritin; with high predictive efficacy (AUC=0.93 and 0.98 using ROC analysis of the predictive capacity of cytokines and biochemical markers, respectively). Increased IL-6 and granzyme B were found to predict liver injury in COVID-19 patients, whereas interferon-gamma (IFN-γ), IL-1 receptor-a (IL-1Ra) and PD-L1 were predictors of remarkable radiological findings. The model revealed consistent elevation of IL-15 and IL-10 in severe cases. Combining basic biochemical and radiological investigations with a limited number of curated cytokines will likely attain accurate predictive value in COVID-19. The model-derived cytokines highlight critical pathways in the pathophysiology of the COVID-19 with insight towards potential therapeutic targets. Our modeling methodology can be implemented using new datasets to identify key players and predict outcomes in new variants of COVID-19.
The massive extension in biological data induced a need for user-friendly bioinformatics tools could be used for routine biological data manipulation. Bioanalyzer is a simple analytical software implements a variety of tools to perform common data analysis on different biological data types and databases. Bioanalyzer provides general aspects of data analysis such as handling nucleotide data, fetching different data formats information, NGS quality control, data visualization, performing multiple sequence alignment and sequence BLAST. These tools accept common biological data formats and produce human-readable output files could be stored on local computer machines. Bioanalyzer has a user-friendly graphical user interface to simplify massive biological data analysis and consume less memory and processing power. Bioanalyzer source code was written through Python programming language which provides less memory usage and initial startup time. Bioanalyzer is a free and open source software, where its code could be modified, extended or integrated in different bioinformatics pipelines. Bioinformatics Produce huge data in FASTA and Genbank format which can be used to produce a lot of annotation information which can be done with Python programming language that open the door form bioinformatics tool due to their elasticity in data analysis and simplicity which inspire us to develop new multiple tool software able to manipulate FASTA and Genbank files. The goal Develop new software uses Genomic data files to produce annotated data. Software was written using python programming language and biopython packages.
Due to the ability to diagnose diseases early and evaluate the effectiveness of medicinal drugs, single nucleotide polymorphism (SNP) identification receives significant interest. Detection and diagnosis of genetic variation through skill-less computational tools would help researchers reducing the severity of such health complications and improving well-tailored therapies using discovered and previously known information. We introduce SNPector, which is a standalone SNP inspection software, which can be used to diagnose gene pathogenicity and drug reaction in naked genomic sequences. It identifies and extracts gene-related SNPs, and reports their genomic position, associated phenotype disorder, associated diseases, linkage disequilibrium, in addition to various drug reaction information. SNPector detects and verifies the existence of an SNP in a given DNA sequence based on different clinically relevant SNP databases, such as NCBI ClinVar, AWESOME, and PharmGKB, and generates highly informative visualizations of the recovered information.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.