1. 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
2. Advancing Long-Read Sequencing Analysis for Structural and Splice Variant Detection, and Post-Transcriptional Modification Identification Using Nanopore Technology
Supervisor: Mgr. Vojtěch Bystrý, Ph.D.
Annotation:
In recent years, the advent of long-read sequencing technology, particularly nanopore sequencing, has unlocked new horizons in the exploration of genomic and transcriptomic landscapes. Unlike short-read sequencing, long-read sequencing can span entire transcripts or large genomic regions in a single read, providing a comprehensive view of structural variants and splice variants which are often elusive in short-read data. The ability to sequence whole transcripts is pivotal for understanding complex genomic rearrangements and transcript isoforms, which are crucial for deciphering the underlying mechanisms of various diseases and biological processes.
However, despite the potential of long-read nanopore sequencing, the lack of specialized bioinformatics tools to accurately analyze this kind of data has been a significant bottleneck. Existing tools primarily tailored for short-read sequencing often fall short when applied to long-read data due to the inherent differences in error profiles and read lengths. This gap underscores the urgent need for the development and optimization of dedicated bioinformatics tools and methods capable of effectively analyzing long-read sequencing data to detect structural variants, estimate splice variants, and explore other genomic and transcriptomic phenomena.
This Ph.D. project is at the helm of addressing this need by engaging in the development and refinement of bioinformatics tools meticulously crafted for long-read sequencing analysis, leveraging the nanopore sequencing technology. The candidate will collaborate closely with the genomics core facility and RNA biology research groups at CEITEC to spearhead novel methodologies for mining long-read and direct RNA sequencing data. The primary objective is to establish a robust analytical framework that can accurately detect structural variants and splice variants, thereby maximizing the wealth of information that can be extracted from long-read sequencing data.
The envisioned outcome of this endeavor is a suite of bioinformatics tools capable of delving into the rich data generated by nanopore sequencing, leading to a first-author publication. Through this initiative, the candidate is set to make substantial contributions to the field, enabling a deeper understanding of genomic and transcriptomic complexities which are integral for advancing research in health and disease paradigms. The project is a stepping stone towards harnessing the full potential of long-read sequencing technology in genomic and transcriptomic research, and lays the groundwork for future explorations in this domain.
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 technology, structural variant detection, splice variant estimation, direct RNA sequencing, post-transcriptional modification detection, epitranscriptomics.
3. 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
4. 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
