Krisztián Papp, Phd

Portfolio

My portfolio highlights examples of reproducible bioinformatics and data science work, spanning automation, rigorous analysis, and clear visualization from raw data to final results.

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Reproducible Data Analysis

Transforming raw data into reliable results is a journey where the process matters as much as the outcome. My analyses are fully reproducible, with every step clearly documented and verifiable by other experts. I use dockerized environments to ensure consistent execution, cloud computing (e.g. AWS ) for scalable and secure computation, and Quarto R notebooks to combine code, results, and documentation into a single HTML report. This approach keeps data, methods, and results together in a transparent and reproducible workflow.

Next-Generation Sequencing (NGS) Data Analysis

I provide end-to-end analysis of next-generation sequencing (NGS) data, transforming raw sequencing outputs into biologically meaningful results. My work includes the analysis of RNA-seq and other high-throughput sequencing data, covering data processing, quality control, statistical analysis, and clear, publication-ready visualizations. I emphasize reproducible workflows and transparent methodology to ensure reliable and interpretable results.

Automated Workflow Management

I design automated, reproducible workflows for bioinformatics and data science, enabling reliable analyses from raw data to final results. For next-generation sequencing (NGS) and other high-throughput data, I build end-to-end pipelines using workflow management systems such as Snakemake, ensuring scalability, traceability, and reproducible execution across local, server, and HPC environments. Beyond data processing, I also build fully automated reporting solutions. In past projects, I developed R-based analysis pipelines that take raw input data, perform statistical analysis and visualization, and automatically generate client-ready reports. These reports can be delivered in standardized formats (e.g. HTML or PDF) and customized with company branding, headers, and consistent visual identity.

Interactive Data Visualization

I develop interactive data visualization solutions that enable users to explore, filter, and interpret complex datasets intuitively. Using R Shiny, I create custom web applications tailored to project and client needs, allowing dynamic interaction through responsive dashboards, plots, and tables. These applications can connect directly to PostgreSQL databases, enabling real-time data access without manual data exports.

Machine Learning

I apply machine learning techniques to model complex relationships, make predictions, and extract actionable insights from data. My work focuses on regression and classification approaches, implemented within reproducible and well-documented workflows. I identify key drivers and patterns in data and assess model performance using robust validation strategies.

Statistical Analysis & Data Visualization

I provide rigorous statistical analysis and data visualization using the R environment to extract clear, interpretable insights from data. My work focuses on exploratory data analysis, hypothesis testing, descriptive and inferential statistics, and the creation of high-quality, publication-ready visualizations. I place strong emphasis on methodological soundness, reproducibility, and clarity, ensuring that analyses are reliable and accessible to diverse audiences.