Hire the Best Genomic Data Analysis Professionals

I help genomics companies, academic research labs, and PhD researchers turn complex sequencing and clinical datasets into publication-ready results, grant-winning insights, and actionable biological discoveries. With hands-on expertise in bulk RNA-Seq, single-cell RNA-Seq, multi-omics integration, and clinical biostatistics, I deliver more than analysis; I provide scientific interpretation, reproducible pipelines, and clear visualisations that accelerate decision-making. Whether you're: Identifying novel therapeutic targets Dissecting cellular heterogeneity Integrating transcriptomics with epigenomics Validating biomarkers for clinical studies Preparing figures for high-impact journals I ensure your data becomes a defensible scientific asset — not just processed output. Let's transform your raw data into meaningful biological insight. 🧬 Core Services 1️⃣ Bulk RNA-Seq & Transcriptome Analysis Tools: HISAT2, STAR, DESeq2, edgeR Deliverables: Differential expression analysis Pathway & enrichment analysis Publication-ready plots Reproducible pipelines Best For: Mechanistic studies, biomarker discovery, therapeutic target identification. 2️⃣ Single-Cell RNA-Seq (scRNA-Seq) Tools: Cell Ranger, Seurat, Monocle Deliverables: Clustering & annotation Trajectory analysis Rare cell population detection Integration of multiple datasets Best For: Tumour microenvironment, immune profiling, tissue heterogeneity. 3️⃣ Multi-Omics Data Integration Transcriptomics + Epigenomics RNA-Seq + Clinical Metadata Cross-platform data harmonisation Outcome: Systems-level biological insight and stronger mechanistic conclusions. 4️⃣ Clinical Biostatistics & Modelling Logistic regression Survival analysis Bayesian models ML classifiers (Random Forest, XGBoost, LASSO) Clinical dataset interpretation Outcome: Robust statistical validation for publications, diagnostics, and trials. 🧪 Technical Expertise Programming: Python, R, Bash, SQL Bioinformatics: GATK, STAR, DESeq2, Seurat, QIIME, MACS2 Statistics & ML: Regression, Bayesian modelling, AI/ML pipelines Visualization: ggplot2, Matplotlib Infrastructure: HPC environments, reproducible workflows, Git 🎯 Stronger Call to Action If you need reliable bioinformatic support for your lab, startup, or thesis, let's discuss your dataset and research goals. Prioritise clarity, reproducibility, and scientific rigour.

**************Genomics & Informatics Lab (GIL) ***************** **************Your Trusted Partner in Advanced Bioinformatics & AI-Driven Multi-Omics****** I lead a distinguished team at Genomics & Informatics Lab (GIL), specializing in Biotechnology, Bioinformatics, Computational Biology, and AI-powered Multi-Omics. At GIL, we offer a comprehensive suite of cutting-edge services tailored to meet the evolving demands of genomics, transcriptomics, proteomics, metagenomics, pharmacogenomics, and precision medicine. Our Expertise: AI-Powered Bioinformatics & Multi-Omics Data Analysis 🔬 1. Bioinformatics & Genomics: AI/ML-powered Next-Generation Sequencing (NGS) data analysis Comparative & Population Genomics for evolutionary insights Genetic variant detection & annotation Functional Genomics (Gene Ontology, KEGG Pathway analysis) Phylogenetics & Evolutionary Genomics miRNA structure prediction & target analysis 🧬 2. AI-Driven Transcriptomics & Epigenomics: RNA-Seq & Single-Cell Transcriptomics Differential Expression & Alternative Splicing Analysis Epigenomics: DNA Methylation & Histone Modification Analysis 💊 3. Computational Pharmacogenomics & Precision Medicine: AI-assisted drug-gene interaction prediction Pharmacogenomic modeling for personalized medicine Toxicogenomics & Drug Response Prediction 🦠 4. Metagenomics & Microbiome Data Science: Whole-genome & 16S rRNA sequencing-based microbiome analysis Microbiome-host interaction modeling AI-powered taxonomic & functional profiling ⚛ 5. AI in Structural & Systems Biology: Protein-Protein Interaction Analysis & Docking Molecular Dynamics (MD) & Simulation Studies AI-driven protein structure prediction (AlphaFold, Rosetta, etc.) 🤖 6. AI & Machine Learning in Multi-Omics Integration: Deep learning for biomarker discovery Multi-omics data integration (Genomics, Proteomics, Metabolomics, Epigenomics) Network-based systems biology approaches 🚀 State-of-the-Art Computing Infrastructure GIL is equipped with high-performance computing (HPC) clusters, multi-core processing, and cloud-based analytics, ensuring scalable, fast, and accurate bioinformatics solutions. 💡 Why Choose GIL? ✅ Expert Team: Decades of experience in computational biology & AI ✅ Cutting-Edge Technologies: AI, ML, and HPC-powered analytics ✅ Proven Track Record: Successful projects in academia & industry ✅ Custom Solutions: Tailored pipelines for diverse research needs Let GIL be your trusted partner in advancing your research! Contact us today to explore how we can support your next breakthrough in computational biology and AI-driven bioinformatics. 🔍💻🧬

I'm a Top Rated freelancer and Computer Science graduate who works at the intersection of three fields most people treat separately: Statistics & Machine Learning, Bioinformatics, and AI. That combination is exactly what messy, high-dimensional data needs — the statistical rigor to trust the result, the ML to find the pattern, and the biological context to know what it means. With 3+ years of experience and 24 successful projects (4.98/5 avg. rating), here's what I bring: 📊 Statistics & Machine Learning Rigorous statistical inference and predictive modeling — hypothesis testing, causal inference (Mendelian Randomization), feature engineering, and ensemble models (XGBoost, LightGBM, Random Forest). I don't just build models that score well; I build models you can defend. Example: a coronary artery disease prediction model reaching 0.956 AUC, with feature ablation to prove what actually drives it. 🧬 Bioinformatics & Omics End-to-end analysis of complex biological data — scRNA-seq, RNA-seq, WGS/WES — using reproducible pipelines (Nextflow, Snakemake). From raw reads to normalized matrices to differential expression and biomarker discovery. Example: cut analysis time by 40% on large genomic projects through custom automated pipelines. 🤖 AI & Deep Learning Deep learning frameworks for real diagnostic problems — computer vision, medical image analysis, NLP, and LLM-based tools. ✨ What ties it together Most freelancers do one of these. I connect them — applying AI and ML to biological and clinical data with statistical discipline, then communicating the findings in publication-ready visuals that both scientists and stakeholders can act on. 🛠️ Tech Stack Python (Pandas, Scikit-learn, TensorFlow, PyTorch) · R (Tidyverse, Bioconductor) · Bash · SQL · Linux · Git · Docker · Cloud 💡 Ready to turn your raw data into discoveries you can trust? Let's talk.

I turn raw sequencing data into results you can publish or act on. RNA-seq, variant calling, GWAS, genome assembly, and reproducible pipelines, delivered with documented code and submission-ready figures. I have done this across 65+ projects on Upwork plus a PhD and postdoc, so I know what a clean analysis looks like and the shortcuts that cause problems later. Quick background: postdoc in population genetics. PhD in bioinformatics and molecular evolution at Paris-Saclay (CNRS), where I worked on plant-bacteria interactions. Master's in bioinformatics from Sorbonne, and bachelor's in computer science from Descartes. I've been programming for over 10 years, mostly Python, R, and Bash, though I can work in Java, C, or JavaScript if a project calls for it. Eight years of building data pipelines specifically. What I do: Genomics and population genetics. GWAS, co-GWAS (PLINK2, Firth regression), population structure analysis (PCA, ADMIXTURE, pairwise FST), ancient DNA work (qpAdm, qpWave, ADMIXTOOLS2), variant calling and annotation. I've run these on human, plant, pathogen, and ancient samples. Genome assembly and annotation: De novo assembly from Nanopore or Illumina, gene prediction, functional annotation, genome comparison. Bacteria, plants, insects, human. I've assembled all of them at some point. RNA-seq: Differential expression from raw reads, co-expression networks, pathway enrichment. Full pipeline or just the analysis part, depending on what you need. Structural bioinformatics: AlphaFold2 batch runs, FoldSeek for structural comparisons, molecular docking with ClusPro, AutoDock Vina, or HADDOCK. I do the visualization too (PyMOL, ChimeraX), and create publication-ready figures, not just screenshots. Phylogenetics and molecular evolution: IQ-TREE2, RAxML, selection tests, gene family evolution, protein evolution. Proteomics and lipidomics: Mass spec data processing through to statistical analysis and biological interpretation. Pipelines: I build workflows, documented and tested with example data. Your team should be able to rerun them without me. Writing and code review: Methods sections, technical reports, manuscript figures. I also debug and review existing pipelines. If your code or script doesn't work, I'll fix it and tell you why it broke. Everything comes with commented code and R Markdown or Jupyter reports. Figures are made in ggplot2 or matplotlib and ready for submission. 100% Job Success score across 65+ projects. I reply the same day. "If you need a bioinformatics expert who's not only insanely skilled but also great to work with, Dr. Amira is the one. Would 100% work with her again!" (Client) "Amira delivered a thorough and well-structured technical review, including worked examples and code snippets that were very useful for demonstrating enablement. Her analysis was detailed, thoughtful, and delivered on time. I highly recommend her for bioinformatics and technical review projects." (Recent client) Drop me a message with your data and what you're trying to do. I'll get back to you with a plan and timeline.

I help research teams and biotech companies turn complex clinical and genomic data into clear, publication-ready insights. With a unique background as a former dentist, I don't just see the numbers; I understand the clinical context behind them. I specialize in designing and executing hypothesis-driven research projects. My work begins with processed genomic or clinical data (e.g., from TCGA, GEO, or your own lab), from which I generate deep biological insights using advanced statistical and machine learning models in R. My Core Services: Bioinformatics & Genomics: Downstream analysis of large-scale datasets (TCGA, GEO), gene expression & RNA-Seq analysis, and biomarker discovery. Clinical Biostatistics: Survival analysis (Kaplan-Meier, Cox Models), clinical trial data analysis, longitudinal studies, and analysis of real-world data (EHR). Machine Learning: Building and validating predictive models in R (tidymodels, caret) to stratify patients or predict clinical outcomes. Data Visualization & Reporting: Creating clear, publication-quality figures (ggplot2) and interactive dashboards (Power BI) that tell a compelling story. If you need a research partner who can handle the entire high-level data analysis portion of your project—from hypothesis to publication—let's connect.

I am a bioinformatician, biostatistician, and academic writer freelancer. My main expertise are genomics, transcriptomics, ecogenomics, molecular evolution, population genetics, biogeography, marine ecology, and academic biomedical and biology writing. I have worked for more than 20 years in the academic field, and I have authored more than 20 scientific papers published in high IF international journals. I am proficient in R coding and data visualization. I have participated in many national and international scientific projects.