Biology subjects

Alsamman, A. M.

Publications and source records attributed to Alsamman, A. M..

6 recordsLinked to original sources

bioRxiv2020

The transcriptomic profiling of COVID-19 compared to SARS, MERS, Ebola, and H1N1

COVID-19 pandemic is a global crisis that threatens our way of life. As of April 29, 2020, COVID-19 has claimed more than 200,000 lives, with a global mortality rate of ~7% and recovery rate of ~30%. Understanding the interaction of cellular targets to the SARS-CoV2 infection is crucial for therapeutic development. Therefore, the aim of this study was to perform a comparative analysis of transcriptomic signatures of infection of COVID-19 compared to different respiratory viruses (Ebola, H1N1, MERS-CoV, and SARS-CoV), to determine unique anti-COVID1-19 gene signature. We identified for the first time molecular pathways for Heparin-binding, RAGE, miRNA, and PLA2 inhibitors, to be associated with SARS-CoV2 infection. The NRCAM and SAA2 that are involved in severe inflammatory response, and FGF1 and FOXO1 genes, which are associated with immune regulation, were found to be associated with a cellular gene response to COVID-19 infection. Moreover, several cytokines, most significantly the IL-8, IL-6, demonstrated key associations with COVID-19 infection. Interestingly, the only response gene that was shared between the five viral infections was SERPINB1. The PPI study sheds light on genes with high interaction activity that COVID-19 shares with other viral infections. The findings showed that the genetic pathways associated with Rheumatoid arthritis, AGE-RAGE signaling system, Malaria, Hepatitis B, and Influenza A were of high significance. We found that the virogenomic transcriptome of infection, gene modulation of host antiviral responses, and GO terms of both COVID-19 and Ebola are more similar compared to SARS, H1N1, and MERS. This work compares the virogenomic signatures of highly pathogenic viruses and provides valid targets for potential therapy against COVID-19.

genomics↗

bioRxiv2020

COVIDier: A Deep-learning Tool For Coronaviruses Genome And Virulence Proteins Classification

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.

bioinformatics↗

bioRxiv2020

Pathdict: Machine learning software predict Drug-Pathway interaction in human Based on drug simplified molecular-input line-entry system (SMILES)

Predicting the target of unknown or/and drugs under investigation from data of already identified drugs is very important not only for the understanding of various drug and molecular interaction processes but also for the development of novel drugs. Here we introduce TarDict, a RandomForestClassifier based-software predict the target pathway or protein based on SMILES of chemical. TarDict receives SMILES and returns a list of the possible similar drug, then export list to the user the target that drug contribute in. Training data set of 20442 entry and testing reveal %95 accuracy.

bioinformatics↗

bioRxiv2019

A rapid method for DNA extraction of cotton mature fiber suitable for PCR fingerprinting

Egyptian cotton with its extra-long staple fiber is the worlds finest textile crop, but it has been in a state of rapid decline for the past 20 years. One of the main problems in Egyptian cotton production that, some manufacturers are using inferior quality cotton or other cheaper cotton to cut costs. This practice impacts not only the brand owner but also the cotton farmers and producers. The ability to differentiate between different mature or processed cottons fibers according to its varietal origin is very complicated or highly expensive. We introduce the first non commercial procedure to isolate intact genomic DNA from cotton fiber with a modified CTAB protocol. This protocol was successfully used to retrieve DNA from cotton fabric and the DNA was efficient to perform PCR procedure using DNA-barcoding and SCoT assays. Additionally, we retrieved PCR markers using CAPs assay can differentiate between Egyptian and American cotton from a fiber source.

genomics↗

bioRxiv2019

KASP-Hedgehog: Efficient and routine tool for allele specific primer design using multidisciplinary data types.

The Integration of known nucleotide variations in breeding programs and medical assessments demands the ability to scan and validate thousands of gene-affected SNPs. Designing PCR primers targeting these SNPs require computational tools could generate accurate and target-specific primers using multidisciplinary data types with high accuracy. KASP-Hedgehog is a local-installation, simple and routine tool for allele-specific PCR primer design. KASP-Hedgehog gives user the ability to design KASP primer from a variety of genomic data, extract bi-allelic SNPs for genomic sequences with unknown nucleotide variations and select SNPs with potential effect depending on gene annotation. This tool can use user-provided or self-produced SNPs database to create allele-specific primers with degenerate structure in order to enhance PCR assay selectivity and increase amplification reaction. Additionally, it run an in silico PCR test for designed primers against provided FASTA sequences in order to increase primers selectivity and provide user more information. Moreover, it has interactive and user-friendly graphical user interface (GUI) in addition to a command-line package could be integrated in different bioinformatics pipelines.

bioinformatics↗

bioRxiv2019

Dowsing for salinity tolerance related genes in chickpea through genome wide association and in silico PCR analysis

Soil salinity is a major abiotic stress severely limits agricultural crop production throughout the world, and the stress is increasing particularly in the irrigated agricultural areas. Chickpea (Cicer arietinum L.) is an important grain legume that plays a significant role in the nutrition of the developing world. In this study, we used a chickpea subset collected from the genebank of the International Center for Agricultural Research in the Dry Area (ICARDA). This collection was selected by using the focused identification of germplasm strategy (FIGS). The subset included 138 genotypes which have been screened in the open field (Arish, Sinai, Egypt) and in the greenhouse (Giza, Egypt) by using the hydroponic system at 100 mM NaCl concentration. The experiment was laid out in randomized alpha lattice design in two replications. The molecular characterization was done by using sixteen SSR markers (collected from QTL conferred salinity tolerance in chickpea), 2,500 SNP and 3,031 DArT markers which have been developed and used for association study. The results indicated significant differences between the chickpea genotypes. Based on the average of the two hydroponic and field experiments, seven tolerant genotypes IGs (70782, 70430, 70764, 117703, 6057, 8447 and 70249) have been identified. The data analysis indicated one SSR (TAA170), three DArT (DART2393, DART769 and DART2009) and eleven SNP markers (SNP2021, SNP1268, SNP1451, SNP1487, SNP1667, SNP2095, SNP190, SNP2247 SNP1947, SNP2331 and SNP948) were associated with salinity tolerance. The flanking regions of these markers revealed genes with a known role in the salinity tolerance, which could be candidates for marker-assisted selection in chickpea breeding programs.

genomics↗