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Bioinformatics

Bioinformatics

Doctoral programme, Faculty of Chemical Technology
CHYBI CHARAKTERISTIKA PROGRAMU

The aim of the DSP programme is to educate specialists in bioinformatics, which is a combination of molecular and cell biology, biochemistry, statistics and computer science. Bioinformatics deals with the development of tools for the management of biological databases, algorithms for the processing of molecular-biological data and methods for the analysis and interpretation of relationships in these data. Most of the projects are biologically oriented with the aim to understand the complex context in the studied biological phenomena. However, we also run IT-oriented topics that include the development of algorithms or data processing methods.

Careers

The combination of the education in natural sciences and informatics qualifies graduates to work in interdisciplinary teams. Graduates will find employment in a wide range of areas where data obtained by instrumental analysis of biological samples are processed. Graduates can also rely on a broad knowledge of informatics and can be successful in the development of software technologies, especially for data analytics. Graduates will further find employment in scientific infrastructures built in the Czech Republic within the framework of European operational programs. Due to the persisting lack of experts with an interdisciplinary education, graduates will also be easily employable outside the Czech Republic. Typical positions that DSP Bioinformatics graduate can hold: - researcher in basic or applied research in the public or private sector in the fields of biomedicine, clinical medicine, medical and pharmaceutical chemistry, food, agriculture, biotechnology or forensic science. Typical positions are postdoc, programmer, research associate, research fellow, project leader, project manager. - university lecturer in bioinformatics, computational biology, computational chemistry or applied informatics. Typical positions are assistant professor, assistant, lecturer. - software developer or data analyst in IT companies. - professional positions that require organizational and analytical skills and expertise not only in bioinformatics. Typical job positions include state administration at the highest management levels, organizations that are methodologically and organizationally engaged in science and research or non-profit and educational organizations.

Programme Details

Language of instruction English
Standard length of study 4 years
Form of study Full time
Guarantor of study programme doc. Mgr. Daniel Svozil, Ph.D.
Programme Code AD104
Place of study Prague
Capacity 8 students
Number of available PhD theses 5

List of available PhD theses

Biological machine learning

Department: Department of Informatics and Chemistry, Faculty of Chemical Technology
Also available in programme: Bioinformatika
Theses supervisor: Ing. Tomáš Pluskal, Ph.D.

Annotation

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.

Computational mass spectrometry

Department: Department of Informatics and Chemistry, Faculty of Chemical Technology
Also available in programme: Bioinformatika
Theses supervisor: Ing. Tomáš Pluskal, Ph.D.

Annotation

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.

Genetic recombination and reproductive isolation on Mus musculus model

Department: Department of Informatics and Chemistry, Faculty of Chemical Technology
Also available in programme: Bioinformatika

Annotation

he 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

Department: Department of Informatics and Chemistry, Faculty of Chemical Technology
Also available in programme: Bioinformatika
Theses supervisor: RNDr. Jana Dobrovolná, Ph.D.

Annotation

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 phenotyping and functional genomic data

Department: Department of Informatics and Chemistry, Faculty of Chemical Technology
Theses supervisor: Ing. Vendula Novosadová, Ph.D.

Annotation

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.


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