Biology subjects

Hamwieh, A.

Publications and source records attributed to Hamwieh, A..

6 recordsLinked to original sources

bioRxiv2025

Metabolite-based genome-wide association studies enable the dissection of the genetic bases of bioactive compounds in Chickpea seeds

Chickpea, is the second most consumed food legume, and significantly contributes to the human diet. Chickpea seeds are rich in a wide range of metabolites including bioactive specialized metabolites influencing nutritional qualities and human health. However, the genetic basis underlying the metabolite-based nutrient quality in chickpea remains poorly understood. Here we dissected the genetic architecture of seed metabolic diversity and explored how domestication shaped the chickpea metabolome. Through UPLC-MS we quantify over 3400 metabolic features in 509 chickpea seeds accessions from three independent multi-location field trials. The metabolite genome-wide association study (mGWAS) detected around 130,000 leading SNPs corresponding to 1890 metabolites across different environments. We further found and functionally validated a gene cluster of three CabHLH transcription factors that regulate soyasaponin biosynthesis in chickpea seeds. Our results reveal new insights on the effects of domestication process on chickpea metabolome. and provide valuable resources for the genetic improvement of the bioactive compounds in chickpea seeds.

plant biology↗

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

SNPector: SNP inspecting tool to detect disease existence and drug response in direct naked sequencing depends on mutation index

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 the well-tailored therapies using discovered and previously known information. We introduce SNPector, which is a standalone SNP inspection software could 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 database, Awesome database, and PharmGKB and generates highly informative visualizations of the recovered information.

bioinformatics↗

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↗