Saifeldeen M. Profile

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cairo,

egypt

$50 USD/oră

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S-a înregistrat pe mai 4, 2021

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Saifeldeen M.

@saifeldeenmib99

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$50 USD/oră

cairo,

egypt

$50 USD/oră

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Bioinformatician

Saifeldeen M. Ibrahim is a dedicated Research Assistant at the Bioinformatics Department within the Agriculture Genetic Engineering Research Institute (AGERI) | ARC. Additionally, he serves as a Bioinformatics Assistant (Consultant) at the International Center for Agricultural Research in the Dry Areas (ICARDA) | CGIAR. He earned his BSc degree in Agricultural Biotechnology from the Biotechnology Department at the Faculty of Agriculture, Al-Azhar University. Currently, Saifeldeen is pursuing his MSc degree in Biotechnology with a specialization in Bioinformatics at the same institution. Alongside his academic pursuits, he holds the position of Bioinformatics Instructor at the Agriculture Genetic Engineering Research Institute (AGERI). He has actively contributed to multiple projects in the field of plant biology. His primary focus lies in plant genomics and transcriptomics. In genomics, his keen interest centers around Genome-Wide Identification studies, Genome-Wide Association Studies (GWAS), and the comprehensive process of Whole Genome Sequencing, including assembly and annotation. Within the realm of transcriptomics, he demonstrates a strong inclination towards identifying biomolecular markers and investigating expression patterns through RNA-seq Data Analysis and Differential Gene Expression (DGE) analysis. Furthermore, his expertise extends to single-cell RNA sequencing (scRNA-seq). In metagenomics, he holds experience in amplicon sequencing, particularly in the analysis of 16S ribosomal RNA (16S rRNA) data.

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Portfoliu

These studies involve comprehensive analyses aimed at identifying and characterizing all members of a specific gene family across an entire genome. A gene family is a group of genes with related functions, often arising through gene duplication events during evolution.

The genome-wide identification of gene families typically includes several key steps. First, researchers conduct a thorough search of the entire genome to identify genes that share sequence similarities or structural features indicative of membership in a particular gene family. This process may involve bioinformatics tools and databases that help in comparing and classifying gene sequences.

Once potential members of the gene family are identified, further analyses are performed to assess their functions, expression patterns, and any relevant biological roles. This might include examining gene expression data under different conditions, predicting protein structures, and investigating potential interactions with other

Genome Wide Identification Studies

Single-cell RNA sequencing (scRNA-seq) data analysis involves processing and interpreting gene expression profiles at the individual cell level. The process begins with quality control and normalization, addressing technical variations and filtering out low-quality cells. Dimensionality reduction techniques such as Principal Component Analysis (PCA) and visualization methods like t-Distributed Stochastic Neighbor Embedding (t-SNE) or Uniform Manifold Approximation and Projection (UMAP) help reveal the inherent structure of the data. Clustering algorithms group cells based on similar gene expression patterns, and differential expression analysis identifies genes associated with specific cell types. Functional analysis, including pathway enrichment, provides insights into the biological processes at play. Visualization tools such as heatmaps and violin plots aid in interpreting gene expression patterns and cell relationships. Integration and batch correction may be applied to merge datas

scRNA-seq Data analysis (Toutrial Data)

11489554

CovertGenID

RNA-seq data analysis involves several key steps. Initially, researchers design experiments to address specific biological questions and define experimental conditions. Following RNA extraction, the quality of the RNA is assessed, and cDNA libraries are prepared for sequencing. High-throughput sequencing generates millions of short reads, which undergo preprocessing steps such as quality control, trimming, and alignment to a reference genome. Quantification involves counting the reads that map to each gene, followed by normalization to ensure comparability between samples. The crux of RNA-seq analysis lies in the differential expression analysis, where statistical methods are applied to identify genes that exhibit significant expression changes under different experimental conditions. This process helps researchers pinpoint key genes involved in biological processes and pathways of interest, offering valuable insights into the underlying molecular mechanisms.

RNA-seq Data analysis Projects

Genome Wide Identification Studies

scRNA-seq Data analysis (Toutrial Data)

11489554

CovertGenID

RNA-seq Data analysis Projects

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Experiență

The International Center for Agricultural Research in the Dry Areas (ICARDA)

sept. 2023 - Prezent

A Bioinformatics Assistant/Consultant occupies a pivotal role at the crossroads of biology and computational science. Responsible for the analysis and interpretation of diverse biological data, such as genomics, transcriptomics and proteomics data, this professional employs computational techniques to extract meaningful insights. Their duties encompass selecting and developing bioinformatics tools, managing databases, and collaborating with researchers to address specific research goals.

Educație

BS.c degree in Agricultural Biotechnology

Al-Azhar University,

Egypt

2018 – 2022

(4 ani)

Publicații

Unlocking the Genetic Basis of Abiotic Stress Tolerance in Wheat

Highlights in BioScience 6

Abiotic stresses such as heat and cold temperatures, salinity, and drought are threat- ening global food security by a ff ecting crop quality and reproductivity. Wheat is the most essential staple crop in the world, its complex genome is the main barrier to find- ing valuable genes responsive to di ff erent stresses. Thus, in our study we conducted di ff erential RNA-seq analysis to identify Di fferentially Expressed Genes (DEGs)