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Detection of subpopulation-specific neuronal membrane molecules using single-cell expression data
Zátko, Matěj ; Modrák, Martin (advisor) ; Kubovčiak, Jan (referee)
Single-cell RNA sequencing is a powerful technology that allows the investigation of gene expression at an unprecedented level. Insights into gene expression in individual cells can help biologists uncover cellular heterogeneity and identify previously unknown cell types. Here, we use single-cell RNA sequencing datasets that reveal subtypes of mouse neurons to find population-specific membrane proteins. These proteins could potentially serve as entry points for targeted drug distribution, allowing for drugs to act only on se- lected neuronal populations. We start by identifying five suitable single-cell mouse neuron datasets. Next, we present an overview and a comparison of currently available methods for differential gene expression analysis, an approach that involves quantifying variations in gene expression between groups and/or conditions, based on previous benchmarks. Lastly, we apply the Wilcoxon rank-sum test to selected datasets in order to identify population-specific membrane proteins. 1
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A tool for prediction of small RNA in RNA-Seq data
Pomykalová, Barbora ; Čejková, Darina (referee) ; Jurečková, Kateřina (advisor)
This diploma thesis focuses on the detection of small RNA (sRNA) in the bacterial genome. sRNAs are short non-coding transcripts that play a key role in gene expression. To date, there are several algorithms focusing on the detection of sRNAs from RNA-Sequencing (RNA-Seq) data that can be obtained by some of the sequencing platforms. The most frequently used platforms are Illumina and Ion Torrent belonging to the next generation sequencing and PacBio with Oxford Nanopore belonging to the third generation of sequencing. In this work, the workflow of sRNA detection using freely available tools was described and then an own unique tool for sRNA detection – the SEARCHsRNA tool – was designed. Two open-source software tools – Rockhopper and DETR'PROK, together with newly created tool, were tested on RNA-Seq data for bacteria Vibrio atlanticus LGP32.
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Quality assurance of RNA-Seq workflows with spike-ins controls
Drozd, Tomáš ; Turk, Andreas (referee) ; Mehnen, Lars (advisor)
Spike-in controls, jako je External RNA Controls Consortium (ERCC) nebo Spike-In RNA Variants (SIRV) od firmy Lexogen, se staly nezbytnými v odhadování technické variability. Protože SIRV E0 transcripty mají identickou koncentraci, jejich variabilita po sekvenaci lze použít k odvození technické variability z jednotlivých replikovaných vzorků. To je ekonomičtější než standardní přístup, který více replikovaných vzorků pro odhadování technické variability vyžaduje. Model DESeq, což je standardní nástroj pro analýzu rozdílné exprese genu, byl upraven na základě informací ze SIRV nebo ERCC pro odhad variability. Následně se odhadla pravděpodobnost změny na základě technické variability. Vysoká variabilita mezi SIRV transkripty byla zjištěna, což vedlo k jinému přístupu založenému na odhadu variability pro každý transkript samostatně. Tento inovativní přístup odhalil lepší výkon u datasetů, kde byla pro danou analýzu přítomna pouze technická variabilita pro určitý počet replikovaných vzorků pro danou fyziologickou skupinu (například nádorová a zdravá tkáň). Bylo pozorováno, že při zvýšení počtu vzorků vede k vyšší spolehlivosti pro odhad. Spike-ins, zejména SIRV, však zlepšil výkon analýzy než odhadu založeného na endogenních genech pro určitý počet replikovaných vzorků. Pro normalizaci technické variability je nutné provést další výzkum, aby bylo možné odhadnout významné změny v biologické variabilitě při sekvenaci RNA.
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Gene regulatory network inference based on mutual information in non-model organisms
Pirkl, Petr ; Sedlář, Karel (referee) ; Musilová, Jana (advisor)
The thesis is focused on summary of laboratory methods for determining gene expression, data preprocessing procedures and possible tools used to infere gene regulatory networks. Furthermore, the thesis handles with the pre-processing of data. It means create count table and normalize it. It was use data from the non-model organism Clostridium beijerinckii NRRL B-598. The main parts of the thesis are designed an algorithm for the creation of a gene regulatory network using mutual information and its implementation in the R language. This include testing the algorithm on data from the non-model organism and the gold standard.
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Application of next-generation sequencing for phylogenetic reconstruction of polyploid plants
Skopalíková, Jana ; Fér, Tomáš (advisor) ; Šrámková, Gabriela (referee)
This bachelor thesis summarizes available information about currently used next- generation sequencing (NGS) methods where a big progress was achieved during last few years. Great advantage of NGS is the ability to gain huge amount of data at much lower cost per base compared to the Sanger sequencing. However, there are various pitfalls in data analysis. Nowadays it is possible to sequence the entire genomes of individuals. Nevertheless, this approach remains challenging when studying many individuals, e.g. in phylogenetics. Recently, several approaches for effective reduction of genome complexity arose: transcriptome sequencing (RNA-Seq), target enrichment, restriction digest-based methods (RAD-Seq, RLL, GBS), genome skimming (shallow sequencing), etc. Each method has both advantages and disadvantages that affect its utility in phylogenetics. Furthermore, the thesis deals with polyploid speciation and particularity of phylogenetics in polyploid plants - selection of suitable markers followed by data processing and phylogenetic analyzes. The last part of the thesis is devoted to my future research of polyploid genus Curcuma L.
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Experimental verification of in silico predicted protein binder to FOXO4 transcription factor and transcriptome analysis of bladder cancer
Tauš, Petr ; Drbal, Karel (advisor) ; Převorovský, Martin (referee)
This diploma thesis includes an experimental and a bioinformatic part. The two parts are linked together through the subject of transcription factors of 'forkhead box O' (FOXO) family. FOXO transcription factors have a key role in many cellular processes including cell cycle regulation, apoptosis and metabolism. For a long time, they have been considered strictly as the tumor-suppressors yet a growing number of evidence is pointing out to their pro-tumorigenic role. In consequence FOXO transcription factors are studied intensively as potential therapeutic targets in cancer. In the past decade, in silico prediction of protein-protein interactions has become popular in basic research as well as in drug development. Nonetheless, the predicted structures are still far from fitting to the expected behavior of the respective biomolecules. In the experimental part of this thesis, I verified the interaction of four in silico predicted protein binders based on naturally occurring PDZ domain with FOXO4 using microscale thermophoresis. Non-invasive bladder tumors represent a heterogeneous disease where reliable prediction of tumor aggressiveness is still lacking despite an intensive research. In the bioinformatic part of this thesis, I described the cellular composition of the tumor microenvironment and demonstrated...
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The use of parallel sequencing methods in microbiology.
Pavlíková, Magdaléna ; Najmanová, Lucie (advisor) ; Vopálenský, Václav (referee)
The thesis describes the history of development of sequencing methods with special focus on the modern effective parallel sequencing methods and their application in microbiology. The development and improvements of sequencing systems lead to the acceleration of the process and considerable decrease of price, which consequently allow wider spectrum of applications. Each of the sequencing systems has its characteristic features including drawbacks stemming from the principle of the respective method. Not every method suitable for all the applications. In the thesis the sequencing methods are compared and examined with respect to their appropriateness for certain application fields in microbiology. The currently available sequencing methods are usually categorized into three "generations", distinguished by sets of typical features. First generation methods include the systems of Sanger and Maxam-Gilbert; "next generation" is represented by methods 454, Illumina, SOLiD and Helicos; and finally SMRT, Ion Torrent and the commercially not yet available nanopore sequencing are usually called "next-next generation". Now the sequencing becomes a standard technology of molecular biology, not only in the basic microbiological research, but it is also widely applied in medicine (quick identification of patogenes,...
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De novo transcriptomics and its use in non-model organisms
Čalounová, Tereza ; Pluskal, Tomáš (advisor) ; Svatoňová, Petra (referee)
The rise of second generation sequencing enabled the study of non-model organisms. Without the requirement of having a reference genome, de novo transcriptomics allows the study of functional elements of their genomes. That way, the great complexity of non-model organisms can be explored. This thesis gives a comprehensive overview of the de novo transcriptomics experiment workflow from a bioinformatics perspective. The emphasis was placed on both theoretical background and practical approaches. This work also highlights new methods in de novo transcriptomics which may start to dominate in the near future. The practical part of the work presents transXpress - a de novo transcriptome assembly and annotation pipeline. Its use is demonstrated on a non-model plant long pepper (Piper longum) with medicinal potential. Keywords: transcriptomics, de novo transcriptomics, transcriptome, RNA-Seq, non-model organism, assembly
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Operon-Expresser: The Innovated Gene Expression-Based Algorithm For Operon Structures In-Ference
Schwarzerová, Jana
Current biotechnological research of bacterial genomes has huge potential due to the useof next-generation sequencing (NGS) platforms. NGS era opens paths for analysis of data for sufficientdescription of microorganisms with ecology and biotechnology potential in the future. Althoughsome tools for inference of specific structures in bacterial genomes exist, their pipelines andmethodologies are often based only on searching bacterial genome databases and comparison withmodel microorganisms. This paper deals with the design of a new algorithm for operon structuresinference in bacterial genomes. The algorithm combines searching in bacterial databases and geneexpression information processing. The algorithm was implemented in R language and tested onClostridium beijerinckii NRRL B-598. The bacterium is a typical performer in the field of biofuelsproduction due to its ability to produce butanol. Thanks to that this bacterial organism can be ofgreat potential from an ecological and biotechnological point of view. The paper also provides acomparison of operon structures derived by Operon-mapper and its extending by Operon-expresser.
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Reproducible Analytical Pipeline For Using Raw Rna-Seq Data From Non-Model Organisms
Schwarzerová, Jana
Current biotechnological research of bacterial or archaeal genomes has huge potential due to the use of the next generation sequencing (NGS) platforms. NGS era unravelled huge analysis data for sufficiency description microorganisms with ecology potential in future. Nowadays, efforts lie in creating comprehensive pipelines that can be used for pre-processing analysis to enable effective following steps of high throughput data processing. This paper deals with design of data analysis pipeline for using raw RNA-Seq data that was applied to the Clostridium beijerinckii NRRL B-598. The bacterium is typical performer in the field of biofuels production thanks to its ability to produce butanol. Unfortunately, it is non-model organism as many other microorganisms which can be of great potential from ecological point of view. The proposed pipeline offers to take necessary steps in initial data processing that produces data of comparable quality to widely studied model organisms. Therefore, it can be combined with following pipelines for gene regulatory network inference, which was up to date matter of non-model organisms.
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