Hire the Best Genetic Engineering Specialists

**************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 work on genomics and biology projects, the kind where you have raw sequencing data, mass spec output, or protein sequences and you need someone to turn it into results you can publish or act on. I've done this across more than 67 projects on Upwork and through my PhD and postdoc, so I know what a clean analysis looks like and I know the shortcuts that cause problems later. Quick background: Actually 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 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.

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.

Bioengineer and former Founding R&D Engineer at BioCompute Inc., with ~4 years of experience across startups and international research labs in computational biology, bioinformatics, and genomic data analysis. Named inventor on a US patent filing. I build computational pipelines and analytical systems for biological data: NGS, WGS, long-read sequencing, and high-dimensional experimental outputs. My work combines bioinformatics, statistical modeling, signal processing, and machine learning to extract interpretable patterns from complex biological systems, often within larger pipelines that integrate APIs, containerized environments, and multiple software components. My experience spans genome assembly, variant analysis, methylation detection, structural bioinformatics, raw signal analysis from Oxford Nanopore platforms, and physiological signal analysis. Because I work across both experimental genomics and computational analysis, I can quickly diagnose issues at the interface between sequencing workflows and downstream data interpretation. I also take on biostatistics consulting work including experimental design, hypothesis testing, and grant-level analytical support. At BioCompute, I built a sequencing-based proof-of-concept system integrating native DNA workflows with a custom bioinformatics decoding pipeline developed from scratch, validated on real sequencing runs and contributing to a US patent filing where I am a named inventor. I also delivered genome assembly, methylation analysis, and structured analytical reporting to external customers. What I work with: - Next-generation sequencing (short- and long-read, WGS) - Oxford Nanopore (MinION Mk1B/Mk1D, PromethION; wet lab + analysis) - Genome assembly and annotation (bacterial and small genomes) - Variant analysis and mutation interpretation - Structural bioinformatics (AlphaFold, DSSP, molecular docking) - Modified-base and methylation analysis (pysam, modkit, SAMtools) - Time series and signal processing (FFT, ML-based approaches) - Physiological and biosignal data analysis - Biological image analysis (cell segmentation, microscopy, CV quantification) - Statistical modeling and biostatistics (regression, hypothesis testing, experimental data interpretation) - Lab automation (Opentrons-style JSON protocol systems) - Python bioinformatics pipelines (Linux, Bash) - Containerized and reproducible workflows (Docker, Snakemake) - API-driven data pipelines and backend integrations Tools and technologies: - Python scientific stack (NumPy, SciPy, Pandas, scikit-learn) - Deep learning (PyTorch, TensorFlow) - Image/video analysis (OpenCV, scikit-image) - Docker, Snakemake - Linux, Bash, REST APIs, JSON-based services Wet lab and experimental consulting: Experimental design for sequencing-based studies. Troubleshooting PCR, cloning, library prep, and sequencing workflows. Guidance on protein expression and molecular cloning workflows. Connecting wet lab outputs with downstream computational analysis. Genomics research and analytical consulting: Technical and market research on genomics and sequencing technologies. Variant impact assessment and interpretation workflows. Literature analysis and publication mapping. Translating computational outputs into structured reports and slide decks for both technical and non-technical stakeholders. What you get: - Reproducible, well-documented bioinformatics workflows - Systems-level thinking across wet lab and computational layers - Clear outputs with strong attention to data integrity - Fast turnaround If you're working with sequencing data, complex biological signals, clinical or experimental datasets, or need grant-level analytical support, let's talk.

🧠 𝗔𝗜 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 + 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁 + 𝗕𝗶𝗼𝗹𝗼𝗴𝗶𝘀𝘁 PhD Biologist turned AI Engineer. Based in Germany. 60+ peer-reviewed publications in Gut (IF>25), JAMA Cardiology, and Neurology. Led international GWAS consortia. 20+ production ML projects in computer vision, NLP, time series, and MLOps. 5.0 stars across 100+ clients. 15+ years leading genetics, genomics, and clinical AI research across top European institutions. 🚀 𝗪𝗵𝗮𝘁 𝗜 𝗗𝗼 - Build RAG systems and LLM agents for medical, scientific, and enterprise workflows - Build ML models: classification, computer vision, NLP, time series, recommendation - Analyse genomes: RNA-seq, scRNA-seq, microbiome, GWAS, NGS variant calling - Run biostatistics and epidemiology: survival analysis, meta-analysis, causal inference - Write manuscripts, systematic reviews, grants, and scientific reports - Build Excel dashboards, Power BI reports, and automated data pipelines - Tutor in R, Python, biostatistics, bioinformatics, and ML fundamentals 🤖 𝗔𝗜 𝗦𝗲𝗿𝘃𝗶𝗰𝗲𝘀 - RAG pipelines: LangChain, ChromaDB, Pinecone, Supabase Vector, GraphRAG, RAGAS - Agents: LangGraph, Claude Code WAT framework (40+ Python tools), n8n, Make, VAPI - Full-stack: FastAPI, Streamlit, React, TypeScript, Supabase, Docker - 89 live platforms across AI, genomics, automation, and healthcare (see Portfolio section) 📈 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 & 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 - Classification/regression: scikit-learn, XGBoost, LightGBM, GNN - Computer vision: pneumonia detection, COVID X-ray, steel defect segmentation - Time series: energy forecasting (167M rows), fire response, retail sales - NLP: supply chain satisfaction, form NER, multimodal classification (27 classes) - MLOps: federated learning, model registry, CI/CD, anomaly detection pipelines - Data engineering: OHLCV pipelines, OCR extraction, production ETL 🧬 𝗕𝗶𝗼𝗹𝗼𝗴𝘆 & 𝗕𝗶𝗼𝗶𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗰𝘀 - RNA-seq and scRNA-seq: STAR, DESeq2, Seurat, Scanpy, trajectory analysis - Spatial transcriptomics, ATAC-seq, multiomics (SIB Swiss Institute trained) - Microbiome: QIIME2, DADA2, 16S/ITS amplicon, shotgun metagenomics - GWAS: PLINK, PRSice, Mendelian randomization - led COURAGE-PD consortium - NGS: GATK, VCF, WES/WGS, pharmacogenomics, methylation (EPIC array) - Pipelines: Snakemake, Nextflow, HPC/SLURM, Docker, Conda 📊 𝗕𝗶𝗼𝘀𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 & 𝗘𝗽𝗶𝗱𝗲𝗺𝗶𝗼𝗹𝗼𝗴𝘆 - Cox regression, mixed models, survival analysis, logistic, causal inference - Meta-analysis, PRISMA systematic reviews, health economics, QALY - R, Python, SPSS, SAS, Stata - Lecturer in Applied Regression at Masters level (Lübeck) 📋 𝗘𝘅𝗰𝗲𝗹, 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱𝘀 & 𝗥𝗲𝗽𝗼𝗿𝘁𝗶𝗻𝗴 - Advanced Excel, VBA, Power BI, Tableau, Google Sheets, Looker Studio - Clinical KPI dashboards, automated XLSX pipelines via Python, financial modeling 📝 𝗪𝗿𝗶𝘁𝗶𝗻𝗴, 𝗚𝗿𝗮𝗻𝘁𝘀 & 𝗗𝗲𝘀𝗶𝗴𝗻 - Manuscripts, systematic reviews, grant proposals (NIH, EU Horizon, DFG) - Scientific figures, posters, graphical abstracts, slides from papers ⭐ "Best researcher and data analyst I have ever worked with." ⭐ "Professional, understands both the science and the data." ⭐ "Great at both writing and R, brings creative AI solutions." Send your goal, timeline, and data. I will reply with exactly what I can deliver. SKILLS TAGS Artificial Intelligence RAG Claude Code Medical Writing Biostatistics Epidemiology Systematic Review Genetics Bioinformatics Scientific Writing Python React Supabase Grant Writing Web Development