RNA-seq Experts in Germany
for transcriptome projects, matched in minutes with vetted and available freelancersHire experts who design RNA sequencing workflows, process single-cell and bulk transcriptome data, and interpret differential expression results. FRATCH matches you with precise, vetted and available freelance professionals quickly.
Meet FRATCH Experts in Germany, who have recently used RNA-seq
Nikolai G.
Last position:
Clinical Data Manager at Dr. Falk Pharma
- Used OpenCode and AI-assisted software engineering to design, implement, refactor, test, and document an end-to-end RAW/SDTM/ADaM pipeline in R for Dr. Falk Pharma (07/2026), including metadata-driven transformations, automated validation rules and QC, traceability, and reproducible clinical outputs.
Kevin B.
Last position:
Procurator and AI Lead at ValueData GmbH
- Serve as AI lead for life-science solutions, integrating advanced AI models directly into company workflows and ensuring seamless deployment.
- Design and implement deep learning architectures (PyTorch, Keras) for complex biomedical challenges, including cell segmentation, multimodal omics analysis, and prediction of point clouds.
- Develop and deploy robust LLM-based systems, including RAG architectures and agentic workflows using LangGraph, to facilitate natural-language interaction with complex medical data.
- Lead cross-functional initiatives to apply foundation models and explainable AI (xAI) to clinical and evolutionary algorithms.
Ebenezer N.
Last position:
Applied Data Science & AI Bootcamp
- Prototyped LLM/RAG document assistant; trained transcriptomics and proteomic data; used Git/Docker for reproducibility.
- Strengthened ML fundamentals applicable to omics (feature engineering, validation, leakage control).
Nooshin O.
Last position:
Senior Computational Biologist at Max-Planck-Institute for Molecular Genetics
- Conducting research at the interface of proteomics and artificial intelligence, focusing on the application of machine learning models (e.g., neural networks, clustering algorithms, and feature extraction) to analyze complex biological datasets.
- Developing and teaching AI-based analytical workflows for molecular and proteomic data, integrating tools such as Python (scikit-learn, TensorFlow, Pandas) for predictive modeling and data visualization.
- Collaborating with interdisciplinary teams to explore data-driven hypotheses in molecular genetics and enhance biological interpretation through AI-assisted pattern recognition.
- Implementing automated data processing pipelines to improve reproducibility and FAIR data management in high-throughput experiments.
Mustafa K.
Last position:
Postdoctoral Researcher at National Institutes of Health (NIH), NHLBI
- Led peptide inhibitor design against UPF1 using RFdiffusion, ProteinMPNN, and AlphaFold2/3; validated candidates with molecular dynamics simulations
- Screened over 1 million compounds for ataxia fusion proteins via AutoDock Vina; confirmed top hits using GROMACS MD simulations
- Developed RNA-seq analysis pipelines for nonsense-mediated decay (NMD) studies including isoform quantification and motif discovery
- Modeled protein-RNA interactions using AlphaFold3 multimers integrated with BioGRID/STRING databases
- Established computational workflows for transcriptomics and RNA regulation in iPSC-derived human models
Kaan O.
Last position:
Research Associate at Uniklinik Münster
- Led a research project on investigating factors modulating HPV16 infection in keratinocytes by using microscopy and omics approaches.
- Curated and analysed RNAseq and mass-spec data and performed pathway analysis to identify target proteins.
- Responsible for the operation, proper maintenance, functionality and user training of microscopes and FACS equipment.
- Mentored master students on their thesis on how cell-ECM interactions play a role in HPV susceptibility in keratinocytes.
Muhammad U.
Last position:
Research Assistant at Saarland University
- Applied AI-driven CADD methodologies for biosynthetic pathway optimization and molecule screening.
- Integrated synthetic biology with computational chemistry workflows for rapid in-silico experimentation.
- Automated ML pipelines using Python, PyTorch, and Scikit-learn on Linux, improving model testing and reproducibility.
Fatiha A.
Last position:
Mentor at CyberMentoring Program
- Participated in an international mentoring and career development programme supporting girls in STEM
Pawandeep S.
Last position:
Research Technician at QIMA Monasterium GmbH
- Establishing flow cytometry experiments, including NGS workflow antibody panel design, to investigate how various drug treatments affect TNF receptor expression and influence activation and exhaustion of human-derived T cells.
- Conducting projects on the effects of various drug treatments on a human ex vivo wound healing skin model, performing PAS staining and quantifying the length of the epithelial tongue following injury.
- Investigating effects of drug treatments on hair follicles and ex vivo wound healing skin samples at the transcriptomic level by analyzing expression of specific target genes using quantitative PCR (qPCR).
- Managing daily ordering and inventory of laboratory consumables and equipment, following ISO 9001 standards.
Nhu Loc Thuy T.
Last position:
Ph.D. Researcher in Quantitative Genetics & Computational Biology at University of Cologne (CEPLAS – Cluster of Excellence in Plant Sciences)
- Generated and analysed large-scale RNA-seq data (>800 samples) using R, Python, and high-performance computing (HPC/Linux) systems.
- Integrated multi-omics data (genomic, transcriptomic, and phenotypic); applied Bayesian approaches and machine learning to study gene expression variation and inheritance of complex traits.
- Mentored B.Sc. and M.Sc. students in experimental design, programming in R/Python/Bash, biostatistics, data visualisation and scientific presentations.
Krupali P.
Last position:
Research Associate at Institute of Anatomy and Cell Biology
- Led single-cell and bulk RNA-seq analyses to identify rare airway and immune cell populations
- Designed and deployed multiple interactive R Shiny and Streamlit dashboards for real-time data exploration and communication with clinical partners
- Developed machine learning models for immunotherapy response prediction using single-cell transcriptomics and immune profiling data
- Built reproducible pipelines for single-cell and bulk RNA-seq analyses aligned with clinical and research standards
- Analyzed patient-derived datasets for biological marker identification using reproducible pipelines aligned with research and clinical standards
- Translated computational outputs into clear biological insights for mixed technical and non-technical audiences
Nitin P.
Last position:
Visiting Researcher at University of Strasbourg
- Computational and experimental analysis of jasmonate signaling pathways
Discover over 15,000 top freelancers
Statistics of experts using RNA-seq
Aggregated from the professional profiles of matched freelancers.
Experience
11 years
Position duration
2.3 years
Positions per freelancer
4
Top business areas
Research and Development, Information Technology, Product Development
Top industries
Biotechnology, Education, Healthcare
Certification focus areas
Business Intelligence, Research and Development, Information Technology
Bachelor's degree or higher
100%
Master's degree or higher
100%
Doctorate
83%
Certifications per freelancer
1
Most common languages
German, English, French
Speak two or more languages
100%
Based on our profile pool as of 19 Sep 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Germany are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows how many freelancers charge within that range.
Average rates of experts in Germany using RNA-seq
Rates are based on recent contracts and do not include FRATCH margin.
The average daily rate is the mean of all daily rates from recent contracts of comparable freelancers on our platform.
The median daily rate is the middle value of all daily rates — half of comparable freelancers charge less, half charge more. Unlike the average, it is barely affected by outliers.
Calculated based on our freelancers’ daily rates as of 19 Sep 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
RNA-seq experts industry focus
See which industries our matched freelancers work in most often — every figure is calculated live from the freelancers on FRATCH.
- Biotechnology (100%)
- Education (67%)
- Healthcare (42%)
- Pharmaceutical (42%)
- Agriculture (33%)
- Banking and Finance (8%)
- Food and Beverage (8%)
- Information Technology (8%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What RNA-seq does
RNA-seq, short for RNA sequencing, measures RNA molecules in a biological sample and reveals which genes are active. It supports transcriptome profiling, differential expression analysis, isoform discovery, fusion detection and investigation of non-coding RNA. The method can work with bulk samples or resolve expression at single-cell level.
Core workflow
A complete project connects experimental design with sequencing and computational analysis. Experts assess sample quality, choose library preparation and sequencing strategies, then build reproducible workflows for raw reads, alignment or pseudo-alignment, quantification and statistical interpretation. They also document assumptions, controls and sources of variation.
Ecosystem and tools
RNA-seq work often spans FASTQ quality control, reference genomes, transcript annotations and workflow automation. Relevant tools and environments include FastQC, MultiQC, STAR, Salmon, featureCounts, DESeq2, edgeR, Scanpy, Seurat, R, Python, Snakemake and Nextflow. Strong professionals select tools based on the organism, assay, read design and research question rather than applying a fixed recipe.
Where companies use it
RNA-seq supports research and product decisions across life sciences, diagnostics, agriculture and biotechnology. Common deliverables include:
- Bulk RNA-seq differential expression and pathway analysis
- Single-cell expression matrices, clustering and cell-type annotation
- Transcriptome quality reports and reproducible pipelines
- Biomarker, isoform or gene-fusion investigations
When to bring in experts
Companies often seek freelance expertise when internal teams need a validated workflow, have a backlog of sequencing data or must connect results to a biological decision. Support is also useful when samples span batches, species or platforms, or when an analysis must be transferred into a regulated or auditable setting. In Germany, remote collaboration is common, while laboratory coordination may require clear on-site arrangements.
What strong professionals deliver
Reliable RNA-seq professionals combine molecular biology, statistics and software practice. They check metadata, experimental design, read quality, batch effects and annotation choices before interpreting findings. They explain uncertainty in plain language, preserve provenance from raw data to result and leave behind documented, repeatable workflows that another team can run and review.
Frequently asked questions
Curious about RNA-seq? Here are the answers that come up again and again.
RNA-seq is used to measure gene activity across a sample and compare transcriptomes between conditions. Companies use RNA sequencing for biomarker research, pathway analysis, isoform discovery, single-cell studies and investigation of gene fusions or non-coding RNA.
RNA sequencing can survey known and previously uncharacterized transcripts, while microarrays measure a defined probe set and qPCR usually tests selected targets. qPCR is useful for focused validation, whereas RNA-seq provides broader discovery but requires more substantial computational and quality-control work.
A strong RNA-seq expert usually understands experimental design, molecular sample preparation, statistics, reference annotations and workflow automation. Experience with R or Python, containerized environments, cloud or high-performance computing, and tools such as DESeq2, STAR, Salmon or Scanpy is often valuable.
The right level of experience depends on the assay, organism, data quality and decision the analysis must support. A focused bulk transcriptome comparison may need a different specialist profile than a multi-batch single-cell study, a de novo transcriptome or a regulated deliverable.
RNA-seq analysis is often well suited to remote collaboration because data, workflows and reports can be shared securely. Projects involving sample handling, sequencing-provider coordination or laboratory troubleshooting may benefit from planned on-site work in Germany alongside remote analysis.
Look for clear treatment of experimental design, read quality, alignment or quantification choices, batch effects, normalization and multiple-testing control. A capable RNA sequencing professional should provide reproducible workflows, interpretable reports, documented provenance and a candid explanation of limitations.
Single-cell RNA-seq reveals expression patterns across individual cells and can expose rare populations or cell states. Bulk RNA-seq averages signals across the sample and is often simpler to interpret, so the choice depends on biological heterogeneity, sample quality, budget and the question being asked.
Before beginning RNA-seq, clarify the organism, reference assembly, library type, read files, metadata, biological replicates and intended deliverables. Also confirm data access, computing constraints, privacy expectations, validation plans and whether the work covers only analysis or the full sequencing workflow.
The average hourly rate of freelancers in Germany who have used RNA-seq in their recent projects is 78 €, which corresponds to a daily rate of about 620 € based on an 8-hour working day.
Of the freelancers in Germany who have used RNA-seq in their recent projects, 100% hold at least a Bachelor's degree, 100% hold at least a Master's degree, and 83% hold a doctorate.
On average, freelancers in Germany who have used RNA-seq in their recent projects have 11 years of professional experience, with a single engagement typically lasting around 2.3 years.
The most common languages among freelancers in Germany who have used RNA-seq in their recent projects are German (100%), English (100%), and French (17%).
The most common industries among freelancers in Germany who have used RNA-seq in their recent projects are Biotechnology (100%), Education (67%), and Healthcare (42%).
The most common business areas among freelancers in Germany who have used RNA-seq in their recent projects are Research and Development (100%), Information Technology (50%), and Product Development (42%).
Main locations of FRATCH Experts, who have recently used RNA-seq
Our freelancers and interim experts are at home across the DACH region — available on-site in the major business hubs or fully remote. Choose a location to discover matched specialists, local market insights and up-to-date availability.
Berlin Hamburg Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg Request a free demo
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Philipp Thomaschewski
FRATCH CEO
