Overview

Title: Integrative Methods for Genetic Variant Interpretation Using Multi-Omics Data

Study program: Biomedical Technology and Bioinformatics

Supervisor: prof. Valentýna Provazník

Topic description:

High-throughput sequencing technologies have fundamentally transformed the diagnosis of genetically driven diseases. While whole-exome sequencing (WES) enables comprehensive variant detection, its routine clinical use is still limited by challenges in data interpretation, particularly for variants of uncertain significance (VUS). At the same time, transcriptomic profiling by RNA sequencing (RNA-Seq) provides functional evidence that can substantially improve variant prioritization and pathogenicity assessment. However, robust computational frameworks for the systematic integration of genomic and transcriptomic data are still lacking.

This PhD project aims to develop advanced bioinformatics and machine-learning-based methods for integrative analysis of genomic and transcriptomic data to improve the interpretation of genetic variants and support personalized medicine. The research will focus on methodological development enabling a smooth transition from targeted gene panel sequencing to WES, while leveraging bulk RNA-Seq data to assess the functional consequences of genetic variants. Emphasis will be placed on algorithm design, in silico modeling, variant annotation, and multi-omics data integration in clinically relevant cohorts.

The project will be carried out in close collaboration with CIIRC CTU, the First Faculty of Medicine of Charles University, and University Hospital Ostrava, providing access to real-world sequencing data and clinically relevant research questions.

Your task:

  • Development of bioinformatics algorithms for integrated analysis of sequencing data.

  • Design of computational methods for interpretation and prioritization of variants of uncertain significance using transcriptomic evidence.

  • Integration of genomic and bulk RNA-Seq data, including advanced annotation.

  • Application of machine learning approaches for variant classification.

  • Scientific publishing in peer-reviewed international journals and at conferences.

Requirements:

  • Master’s degree in bioinformatics, biomedical engineering, computer science, computational biology, or a related field.

  • Strong programming skills, preferably in Python and/or R.

  • Basic knowledge of genomics, transcriptomics, and next-generation sequencing data analysis.

  • Ability to work independently as well as within an interdisciplinary research team.

  • English communication skills and strong motivation for research in biomedical data analysis.

We offer:

  • Participation in an interdisciplinary and clinically oriented research project with access to unique multi-omics datasets.

  • Collaboration with leading academic and clinical partners in the Czech Republic and abroad.

  • Opportunities for professional development, including international conferences, workshops, and specialized training in bioinformatics and data science.

For more information about this topic please contact Valentýna Provazník, provaznik@vut.cz.

Application portal: https://www.vut.cz/eprihlaska/

Application deadline: April 30, 2026

Related Jobs