čas: 19.4.2021 05:10:00
Obnovit | RAW
Department of Informatics and Chemistry
List of available PhD theses
Biological machine learning
Our lab combines cutting-edge experimental (e.g., LC-MS, metabolomics, RNA-seq) and computational (e.g., bioinformatics, molecular networking, machine learning) approaches to develop rapid, generally applicable workflows for the discovery and utilization of bioactive molecules derived from plants. We are looking for talented and motivated computational researchers to join our team. The successful candidate for this position will be developing models for the prediction of enzymatic activities of enzymes in biosynthetic pathways. Owing to the interdisciplinary nature of the lab, this project will be conducted in close collaboration with experimental researchers who will be generating data for model training and verification.
Cellular heterogeneity of the tumour microenvironment
The tumour microenvironment significantly influences the behaviour of tumours from their origin, through proliferation, to the development of metastases. The dissertation will deal with the heterogeneity of cell types present in the tumour microenvironment and the heterogeneity of cells of individual types, such as cancer-associated fibroblasts, using functional genomic tools at the whole genome level and at the level of individual cells. The results of the analyzes will be statistically processed and interpreted in the context of cellular signaling pathways in order to find new tumour markers or therapeutic targets.
Complex characterization of transcriptome and proteome in tissues of human newborns
Early postnatal period is critical with respect to development of the key physiological functions and homeostatic mechanism of the newborn, as well as for imprinting the metabolic features that could be manifested during the adolescence and adulthood. A unique biobank of the autopsy samples of various is available that had been collected from human newborns, mostly very premature newbors. Characterization of the transcriptome (RNAseq) and proteome of the tissues is ongoing. The project will focus on the data analysis in order to reveal various aspects of early human development and its control. It will proceed in close collaboration between the Laboratorty of Adipose Tissue Biology of the Institute of Physiology of the Czech Academy of Sciences (CAS) (http://www.fgu.cas.cz/en/departments/adipose-tissue-biology), the Institute for Inherited Metabolic Disorders, First Faculty of Medicine, Charles University (http://udmp.If1.cuni.cz/en/genomics-and-bioinformatics-laboratory) in Prague and the Laboratory of Genomics and Bioinformatics of the Institute of Molecular Genetics CAS (https://www.img.cas.cz/research/michal-kolar/ ).
Computational mass spectrometry
Our lab combines cutting-edge experimental (e.g., LC-MS, metabolomics, RNA-seq) and computational (e.g., bioinformatics, molecular networking, machine learning) approaches to develop rapid, generally applicable workflows for the discovery and utilization of bioactive molecules derived from plants. We are looking for talented and motivated computational researchers to join our team. The successful candidate for this position will be developing the next generation of the MZmine platform (https://mzmine.github.io) for mass spectrometry data processing in metabolomics. Among other things, we are aiming to add full support for ion mobility spectroscopy (IMS) to MZmine, and to enhance its molecular networking capabilities. Experience with Java programming is recommended.
Does the lack of sex affect our genomes and phenotypes? The impact of asexual reproduction on genomes, populations and fitness of clonal individuals.
Epigenetic changes in malignancies
The tumour microenvironment significantly influences the behaviour of tumours from their origin, through proliferation to the development of metastases. Epigenetic changes, which are very often observed in tumour cells, are very likely to affect the behavior of other components of the tumour microenvironment, such as cancer-associated fibroblasts. The proposed work will deal with epigenetic changes in cells of the tumour microenvironment, description of changes in chromatin accessibility at the whole genome level, their statistical processing and interpretation of changes in the context of cellular signaling pathways in order to find new tumour markers or therapeutic targets.
Genetic recombination and reproductive isolation on Mus musculus model
The aim of the proposed dissertation project is to elucidate the epistatic interaction of the PRDM9 histone methyltransferase gene with the X-linked Hstx2 genetic factor in meiotic recombination and male infertility of intersubspecific hybrids. Our laboratory identified the Prdm9 as the first gene in vertebrates engaged in reproductive isolation between species. PRDM9 protein predetermines the meiotic recombination hotspots within species to ensure meiotic cross-overs, chromosome pairing and differentiation of germ cells, but in intersubspecific hybrids the same gene product causes meiotic arrest and hybrid sterility due to persistence of DNA double-strand breaks, recombination failure and subsequent failure of chromosome pairing. The process is modulated by the Hstx2 genetic factor, localized in a 2.7 Mb interval on the chromosome X. The main task of the project is to identify the genomic sequence responsible for the Hstx2 effect using a panel of bioinformatics tools for mRNA expression profiling using next generation RNA sequencing (RNA-seq), for chromatin immunoprecipitation sequencing (ChIP-seq) and for quantitative trait loci (QTL) mapping.
Genome-wide mapping of loci forming genotoxic intermediates associated with collisions between replication and transcription complexes
Recent studies have shown that in human precancerous lesions, activated oncogenes induce stalling and collapse of replication forks, leading to genomic instability, a driving force of cancer. The proposed project addresses the hypothesis that oncogene-induced replication stress arises from interference between transcription and replication, which is associated with the formation of genotoxic RNA:DNA hybrids, referred to as R-loops. The project has the following objectives: (i) to identify on genome-wide scale the loci that are prone to R-loop formation under conditions of oncogene-induced replication stress; (ii) to determine basic charateristics of these loci; (iii) to assess whether oncogene activation is associated with R-loop formation at common fragile sites that are preferred target of oncogene-induced replication stress; (iv) to dermine whether R-loop forming loci overlap with the breakpoints of chromosomal rearangements found in cancers.
Integration of metabolomics data into metabolic pathways
Metabolomics provides information on metabolite concentrations and is functionally closest to the biological manifestations of the genome and proteome. Although the individual omics follow each other logically, each of these scientific fields is at a different level of knowledge.
The aim is to develop a specific approach by which metabolomic data (metabolite concentrations) will be processed into the format of metabolic pathways (Wikipathways, SMPDB, etc.), and their integration with proteomic and genomic data. Part of the work is understanding the concept of LC-MS metabolomics, annotation of metabolites according to databases, work with metabolite identifiers at various levels of identification (summary formula, exact structure, ...), scripts and applications in Python and R, visualization of pathways from databases and based on own designs.
Using the resulting methodology, omic projects based on clinical studies and animal models will be processed. The work will be carried out at the FGÚ AV ČR, where the metabolomics and proteomics service laboratory is located. The work is financially secured.
Prerequisite for success is knowledge of programming languages for data mining (Python, R), basics of biochemistry (metabolites, pathways, cell compartments) and basics in omic disciplines.
Integration of phenotyping and functional genomic data
The position of bioinformatician is becoming necessary for every scientific group. Generating large datasets of omic data makes it necessary to develop new computational algorithms using tools such as machine learning and artificial intelligence, which will also allow the processing of diverse unstructured data. Our group is part of the research infrastructure Czech Centre for Phenogenomics, involved in the systematic annotation of the mouse genome within the International Mouse Phenotyping Consortium (IMPC). We produce mouse lines with one gene deactivated. These lines are further characterized by a standard phenotyping pipeline. The data set from each animal tested has over 700 parameters from different fields. These parameters contain numeric, categorical and image data. We are also collecting metabolomic data for selected lines. The Ph.D. project aims to integrate every data generated both in our center and within the whole IMPC. Linking individual parameters and finding correlations and causality between them and their possible semantic analysis will help to better understand the phenotype. At the same time, knowledge of a given gene function will enable mathematical modeling of the phenotype of genes involved in similar or overlapping regulatory networks.
Transcriptome analysis of acute injuries of the central nervous system
The function of the central nervous system (CNS) is defined by the complexity of interactions between hundreds types of neurons, glial and vascular cells. The latest improvements in high-throughput gene expression technologies together with computational analyses allow to study the CNS complexity at unprecedented resolution. In this thesis, we aim to use the latest data collections and analysis techniques to conduct detailed transcriptomic description of ischemic brain injury and spinal cord injury in mice and rats. Firstly, bulk analysis will serve for coarse functional annotation of processes after injury. It will be complemented by deconvolution analysis to estimate cell type proportion changes and unsupervised co-expression analysis for identification of gene modules governing the response to injury. Network analyses will identify key drivers of the response and predict the mechanism of injury. Followed by generating single-cell and spatial transcriptomic datasets, coupled with the latest data integration tools, we aim to acquire detailed view on heterogeneity of response to ischemic and spinal cord injury at the single-cell level. Noteworthy, the observations will be related to various emerging cell atlases and thus complement incentives of the field.
Virtual screening in chemical biology
Chemical biology is a scientific discipline that attempts to answer biological questions by directly probing living systems at the chemical level. The main application area of chemical biology is the development and characterization of organic compounds that are used for the study of various biological systems. The key experimental method used in chemical biology is high-throughput screening (HTS) that enables to quickly assess the biological activity of thousands of compounds at once. Because of its high time and cost demands, it is usually preceded by virtual screening, a computational technique used to search libraries of small molecules in order to identify those compounds which are most likely to bind to a given molecular target. The dissertation is focused on the application of advanced virtual screening methods for the identification of ligands active against various molecular targets (e.g., juvenile hormone receptor) with the aim to gain deeper insight into their biological behavior.