i
irem_es

irem

@irem_es

Bioinformatics Developer and Genetics and Bioengineering

Turkey
English, German, Turkish
About me
As a Genetics and Bioengineering graduate, I bridge the gap between wet-lab bioprocesses and dry-lab computational pipelines. I build production-grade, modular Python workflows to clean, analyze, and visualize complex genomic data (NGS, RNA-Seq, and sequence analysis). I specialize in transforming raw datasets into clinical-grade, publication-ready reports (Volcano plots, Heatmaps) for biotech startups and research labs. Let's build reproducible, turnkey pipelines to accelerate your research.... Read more

Skills

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irem_es
irem
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See my services

Statistical Modeling & Analytics
I will do advanced rna seq and single cell data analysis
From$80 / projectMore details
Data Science Consultation
I will perform bioinformatics and genomic data analysis in r and python
From$70 / projectMore details

Portfolio

Work experience

Fiverr

Bioinformatics Analyst – Single-Cell RNA-seq Research

Fiverr • Freelance

Aug 2026 - Sep 2026 • 1 mo

Performed bioinformatics and statistical analyses for a single-cell RNA-seq study of human testicular ageing. Analyzed 41,000+ cells across multiple donors, evaluated age-associated expression patterns, and compared cell-level and donor-aware inference approaches. Applied donor-level aggregation, cluster-robust inference, sensitivity analyses, and leave-one-donor-out (LODO) checks to assess robustness and pseudoreplication. Contributed to result interpretation, figures, statistical reporting, and manuscript preparation.

Upwork

Upwork

Freelance • 2 mos

Freelance Bioinformatics Developer

Jun 2026 - Jun 2026 • 0 mos

• Processed and formatted Mitochondrial COI sequences to meet strict NCBI standards for official GenBank submissions. • Developed automated Python scripts for sequence curation, quality filtering (QC), and genomic data wrangling.

Freelance Bioinformatics Developer

Apr 2026 - Jun 2026 • 2 mos

Developed a diagnostic pipeline for breast cancer classification using the WBC repository. I engineered a query-optimized data infrastructure from raw clinical matrices to effectively isolate tumor phenotypes. By utilizing Python, Pandas, and Seaborn, I successfully modeled the spatial and volumetric variances between malignant and benign cohorts. This analysis identified "Mean Radius" as a high-weight diagnostic biomarker and established a robust baseline for implementing Logistic Regression and Random Forest predictive models