RNA-seq Experts in Germany
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Meet FRATCH Experts in Germany, who have recently used RNA-seq
Kevin Baßler
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 Ntiriakwa
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 Omranian
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 Karagöz
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 Ozturk
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 Usman
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 Atanjaoui
Last position:
Mentor at CyberMentoring Program
- Participated in an international mentoring and career development programme supporting girls in STEM
Nhu Loc Thuy Tran
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 Poharkar
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 Pawar
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
2.4 years
Positions per freelancer
4
Top business areas
Research and Development, Information Technology, Product Development
Top industries
Biotechnology, Education, Agriculture
Certification focus areas
Business Intelligence, Research and Development, Information Technology
Bachelor's degree or higher
100%
Master's degree or higher
100%
Doctorate
90%
Certifications per freelancer
1
Most common languages
German, English, Turkish
Speak two or more languages
100%
Based on our profile pool as of 30 Aug 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 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
What RNA-seq does
RNA-seq, or RNA sequencing, measures which genes are active in a sample and how strongly they are expressed. It is used to study cells, tissues, disease states, drug response, and time-course changes. Many teams also call it transcriptome sequencing or whole transcriptome sequencing.
Typical work
- Differential expression analysis
- Splice variant and isoform analysis
- Quality control and sample filtering
- Pathway and enrichment analysis
- Reporting results for biology teams
Core workflow
A strong project starts with the right experimental design, clean sample metadata, and careful handling of FASTQ, BAM, and count matrices. Experts then manage alignment or pseudoalignment, gene quantification, normalization, and statistical testing. Good RNA-seq work keeps the biology visible at every step.
Tools and ecosystem
RNA-seq specialists work with common stacks such as FastQC, STAR, HISAT2, Salmon, Kallisto, featureCounts, DESeq2, edgeR, and tximport. They also use R and Bioconductor for analysis and reproducible reporting. In Germany, companies often need experts who can fit into mixed local and remote research teams and communicate clearly with lab and data groups.
When to bring in help
Bring in freelance expertise when a study has stalled, the pipeline is hard to reproduce, or results need a second review before publication or internal decisions. It also helps when a team needs support for single-cell RNA-seq, custom annotation, batch effect correction, or integration with other omics data. Strong specialists can fix both analysis gaps and documentation gaps.
What strong specialists do
Strong RNA-seq professionals do more than run software. They check input quality, choose methods that fit the assay, explain trade-offs between alignment and pseudoalignment, and write clean, reusable analysis steps. They also know how to present transcriptome results in a way that scientists and stakeholders can use.
Frequently asked questions
Curious about RNA-seq? Here are the answers that come up again and again.
RNA-seq is used to see which transcripts are present in a sample and how expression changes across conditions. Companies use it for gene expression studies, disease research, biomarker work, and response-to-treatment analysis. It is also common in discovery projects where the next step depends on a clear view of the transcriptome.
RNA-seq gives a broader and more flexible view because it does not depend on a fixed probe set. That makes it better for discovering new transcripts, splice events, and low-abundance signals. Microarrays can still be useful in constrained settings, but RNA-seq is usually the stronger choice when analysis depth matters.
A good RNA-seq specialist usually knows statistics, experimental design, and scripting in R or Python. For many projects, Bioconductor, workflow tools, and careful metadata handling matter as much as the sequencing analysis itself. Domain knowledge in biology or translational research is also a plus.
A RNA-seq project works best when the team can share the sample design, read type, organism, annotation source, and the main biological question. That lets the specialist choose the right pipeline and avoid wasted analysis steps. Even if the study is not fully fixed, a clear goal and sample overview are enough to start.
Yes, RNA-seq analysis is often done remotely because the work is digital and results can be reviewed in shared documents and notebooks. For teams in Germany, remote collaboration is common when lab work stays on site and analysis happens across locations. The key is clear communication, access to files, and agreed review steps.
Ask which pipeline the RNA-seq specialist prefers, how they handle quality control, and how they report differential expression results. It is also smart to ask how they manage batch effects, annotation versions, and reproducibility. The best answers are specific and tied to your data type, not generic.
RNA-seq usually means bulk analysis unless the project says otherwise, where expression is measured across many cells at once. Single-cell RNA-seq adds cell-level resolution, different preprocessing, and more complex quality filtering. A freelancer should be able to explain which approach fits your question and why.
Look for clear quality control, defensible filtering, and results that match the experimental setup. A strong RNA-seq deliverable includes methods, assumptions, versioned tools, and an explanation of how conclusions were reached. If the analysis is solid, you should be able to trace every main result back to the input data.
The average hourly rate of freelancers in Germany who have used RNA-seq in their recent projects is 82 €, which corresponds to a daily rate of about 660 € 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 90% 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 2.4 years.
The most common languages among freelancers in Germany who have used RNA-seq in their recent projects are German (100%), English (100%), and Turkish (20%).
The most common industries among freelancers in Germany who have used RNA-seq in their recent projects are Biotechnology (100%), Education (80%), and Agriculture (40%).
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 (40%).
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
