Hamada I.

A results-driven bioinformatician dedicated to turning complex genomic and multi-omics datasets into clear, impactful discoveries. I support academic groups, biotech teams, and pharmaceutical researchers by streamlining their analyses and helping them move faster toward meaningful scientific outcomes. With strong expertise in computational biology, Nextflow workflow development, and advanced data analytics, I provide end-to-end solutions, from raw data processing to comprehensive insights. My work equips teams with the precise genomic insights they need to push projects forward and achieve their professional and research objectives. Skills: Bioinformatics • Computational Biology • RNA-seq Analysis • Single-Cell RNA-seq (scRNA-seq) • GEO Data Retrieval • Data Curation • Metadata Integration • Gene Expression Profiling • Differential Expression • Immune Signature Scoring • Stress Gene Analysis • Cancer Immunology • Tumor Microenvironment Analysis • Predictive Biomarker Discovery • Logistic Regression • Cox Regression • Statistical Modeling • Seurat (R) • Scanpy (Python) • GEOquery • GEOparse • R Programming • Python Programming • Immune Cell Profiling • T-cell Exclusion Analysis • Bulk & Single-cell Integration • Reproducible Bioinformatics Pipelines • Linux / Command-line Workflows Comprehensive Expertise in Computational Genomics and Machine Learning Genomics Data Analysis: Extensive experience in processing and analyzing high-throughput genomics data, including RNA-Seq, microarrays, whole genome sequencing (WGS), and targeted sequencing. Skilled in differential expression and variant analysis using tools such as DESeq2, edgeR, and DEXSeq within the R/Bioconductor ecosystem. Reproducible Pipeline Development: Advanced proficiency in designing and executing Nextflow workflows, including nf-core Sarek, RNAseq, and differential abundance pipelines, ensuring scalability and reproducibility across complex analyses. Variant Detection and Annotation: Expertise in identifying and annotating germline and somatic variants through robust pipelines encompassing data preprocessing, variant calling, and downstream analysis from WGS and targeted sequencing datasets. Epigenomics Analysis: Skilled in DNA methylation analysis leveraging both microarray and NGS data. Proficient with Bioconductor tools such as minfi, limma, bumphunter, and bsseq for differential methylation and regional analysis. Long-Read Sequencing (ONT): Proficient in the analysis of Oxford Nanopore Technologies (ONT) data using tools like Guppy, Minimap2, Porechop, Nanopolish, and Medaka for basecalling, alignment, and variant polishing. Regulatory Genomics: Strong expertise in transcription factor binding and chromatin interaction analyses using ChIP-Seq and related datasets. Functional Enrichment and Pathway Analysis: Experienced in gene set enrichment and pathway integration analyses (e.g., GSEA, KEGG, Reactome) using R/Bioconductor and Python-based frameworks. Single-Cell and Multi-Omics Integration: Skilled in single-cell RNA-Seq and multi-omic data analysis using R-based pipelines, with integration of machine learning techniques for cell-type classification, dimensionality reduction, and trajectory inference. Machine Learning Implementation: Hands-on experience applying supervised and unsupervised learning methods—including clustering, PCA, random forests, support vector machines, and neural networks—for biological pattern recognition, disease prediction, and feature selection. Competent in developing and deploying ML models for omics data integration and biomarker discovery using Python (scikit-learn, TensorFlow, PyTorch) and R (caret, tidymodels). Programming and Data Visualization: Proficient in Python (Biopython, Pysam, NumPy, Pandas, scikit-learn, Altair, Matplotlib) and R for end-to-end data analysis and visualization. Experienced with Google Colab and Jupyter Notebooks for collaborative research and reproducible workflows. MATLAB Applications: Experienced in leveraging MATLAB for modeling biological systems and simulating genetic and population dynamics experiments. Computational Infrastructure: Extensive experience in Linux/Bash environments, containerization with Docker, and cloud computing platforms including AWS (SageMaker), Google Cloud, and HPC clusters. Reproducible Research and Collaboration: Adheres to computational best practices, including version control (Git/GitHub), dynamic documentation (RMarkdown, R Notebooks, Jupyter), and workflow management for traceable and reproducible research outputs. Project Management: Demonstrates a lean, agile, and quality-driven approach to project execution, ensuring timely delivery and stakeholder alignment. PMP-certified professional with proven leadership in interdisciplinary scientific projects.