1.     Development of bioinformatics methods for analyzing RNA modifications with long-read nanopore sequencing

Supervisor:  Mgr. Vojtěch Bystrý, Ph.D.                                  

Annotation:

Post-transcriptional RNA modification research, also known as epitranscriptomics, is a science field that recently became prominent during the covid pandemic for its role in immune response mediation. The role of epitranscriptomics in cancerogenesis is also very much studied.

In CEITEC MU, several research groups are trying to understand the biological role of RNA modifications.

An emerging method to study RNA modification is nanopore sequencing because the long-read sequencing can better capture the whole transcripts, but mainly because it allows direct RNA sequencing. However, novel algorithms and methods must be developed to utilize the potential of the method to its full potential.

The Ph.D. candidate will collaborate with RNA biology research groups, a genomics core facility to establish methods for long-read sequences and eventually direct RNA sequencing data analysis. The Ph.D. candidate will, through bioinformatics support, facilitate the research of RNA modification concerning the immune response and cancer.

Requirements on candidates:

bioinformatics, informatics, data science

Literature:

• Quin, Jaclyn, et al. "ADAR RNA modifications, the epitranscriptome and innate immunity." Trends in biochemical sciences 46.9 (2021): 758-771.
• Furlan, Mattia, et al. "Computational methods for RNA modification detection from nanopore direct RNA sequencing data." RNA biology 18.sup1 (2021): 31-40.

Keywords: bioinformatics, long-read sequencing, nanopore, epitranscriptome, direct RNA sequencing

2.     Development of bioinformatics methods for multi-omics approaches in cancer research

Supervisor:  Mgr. Vojtěch Bystrý, Ph.D.                                  

Annotation:

In the state-of-the-art personalized therapy planning for oncology patients, it is increasingly recognized the necessity to study the tumor molecular processes in their entirety to find better treatment strategies. This is where the multi-omics field is coming in. Multi-omics is a new approach where the data sets of different omic groups are combined during data analysis. The standard omic techniques explored during multi-omics are genome, proteome, transcriptome, epigenome, and metabolome. In the CEITEC Bioinformatics Core Facility, we are currently involved in several large-scale projects aiming to use multi-omics approaches to better characterize oncology patients.

The Ph.D. candidate will collaborate on these projects with the aim of developing bioinformatics methods and machine learning models to combine the various omics data into a better prediction model. The main focus would be on the connection of genomics, transcriptomics, and proteomics datasets.

Requirements on candidates:

bioinformatics, informatics, data science

Literature:

• Chen, Yuanyuan, Haitao Li, and Xiao Sun. "Construction and analysis of sample-specific driver modules for breast cancer." BMC genomics 23.1 (2022): 1-16.
• Hasin, Yehudit, Marcus Seldin, and Aldons Lusis. "Multi-omics approaches to disease." Genome biology 18.1 (2017): 1-15.
• Subramanian, Indhupriya, et al. "Multi-omics data integration, interpretation, and its application." Bioinformatics and biology insights 14 (2020): 1177932219899051.

Keywords: bioinformatics, multi-omics, cancer research, genomics, transcriptomics, proteomics

3.     Development of bioinformatics methods for radiogenomics approaches in cancer research

Supervisor:  Mgr. Vojtěch Bystrý, Ph.D.                                  

Annotation:

Genomic and transcriptomics biomarkers are becoming some of the oncology's most important prognostic factors. These prognostic markers can be helpful for the monitoring and selection of patients for a specific treatment. Radiomic features derived from nuclear medicine imaging, such as PET/CT scans, on the other hand, have the potential to provide functional information on the activity of oncogenic drivers at a holistic level. Radiogenomics is a novel developing field combining the strength of both technologies, which has the potential to raise currently underexplored synergies to advance the personalized management of cancer patients. In the CEITEC Bioinformatics Core Facility, we are currently involved in several radiogenomics projects involving comprehensive clinical studies and a state-of-the-art lab techniques such as spatial transcriptomics and liquid biopsies.

The Ph.D. candidate will collaborate on these projects with the aim of developing bioinformatics methods to analyze the genomics and transcriptomics data in order to combine them with nuclear medicine imagining data. The final objective of the study will be the development of clinically applicable workflows.

Requirements on candidates:

bioinformatics, informatics, data science

Literature:

• Spielvogel, Clemens P., et al. "Radiogenomic markers enable risk stratification and inference of mutational pathway states in head and neck cancer." European Journal of Nuclear Medicine and Molecular Imaging (2022): 1-13.
• Politi, Letterio S., and Riccardo Levi. "Editorial comment: Radiogenomics of glioblastoma: shifting the focus from tumor cells to immune microenvironment." European Radiology (2022): 1-2.

Keywords: bioinformatics, radiogenomics, cancer research, genomics, radiomics, spatial transcriptomics