Dissertations / Theses on the topic 'Open Bioinformation'

<p>The heart is a complex three-dimensional structure with mechanical properties that are inhomogeneous, non-linear, time-variant and anisotropic. These properties affect major physiological factors within the heart, such as the pumping performance of the ventricles, the oxygen demand in the tissue and the distribution of coronary blood flow.</p><p>During the cardiac cycle the heart muscle tissue is deformed as a consequence of the active contraction of the muscle fibers and their relaxation respectively. A mapping of this deformation would give increased understanding of the mechanical properties of the heart. The deformation induces strain and stress in the tissue which are both mechanical properties and can be described with a mathematical tensor object.</p><p>The aim of this master's thesis is to develop a visualization tool for the strain tensor objects that can aid a user to see and/or understand various differences between different hearts and spatial and temporal differences within the same heart. Preferably should the tool be general enough for use with different types of data.</p>

Microarrays are now an established tool for biological research and have a wide range of applications. In this thesis I investigate the BeadArray microarray technology developed by Illumina. The design of this technology is unique and gives rise to many computational and statistical challenges. However, I show how knowledge from other microarray technologies can be used to our advantage. I describe the beadarray software package, which is now used by researchers around the world. The development of this software was motivated by the fact that Illumina's software (BeadStudio) gives a summarised view of Illumina data and does not gives users any control over several processing steps that were found to be crucial for other microarray technologies. A main feature of beadarray is the ability to access raw data. The advantages of such data include the ability to perform more detailed quality assessment and greater control over the analysis at all stages. The analysis of a control experiment shows that the processing steps used in BeadStudio can be improved. In particular, utilising variances calculated from the raw data can increase the ability to detect genes which have different expression levels between samples, a common goal for microarray studies. The data from the control experiment are made available for other researchers to use and validate their own analysis methods. One issue discovered during the analysis of the control experiment was that only half of the intended genes could be reliably measured due to problems in the design of the probes targetting particular genes. By considering a large set of publicly available Illumina arrays, I show how such unreliable measurements can affect the analysis of Illumina data. I also show how potential problems can be identified in advance of an experiment and incorporated into an analysis pipeline.

This thesis describes the problems of storage and management of biological data, particularly of Haloalkane Dehalogenase enzymes. Furthermore, the thesis aims at project HADES (HAloalkane DEhalogenase databaSe) initiated by protein engineering group of Loschmidt Laboratories, Masaryk University in Brno. This is a project whose main goal is simply to store, preserve and display a wide variety of proteins data. The result of this work is a flexible database system allowing easy extensibility and maintainability, which is built on technologies Apache, PostgreSQL and PHP using the Zend Framework.

Deep sequencing of strand-specific cDNA libraries is now a ubiquitous tool for identifying and quantifying RNAs in diverse sample types. The accuracy of conclusions drawn from these analyses depends on precise and quantitative conversion of the RNA sample into a DNA library suitable for sequencing. Here, we describe an optimized method of preparing strand-specific RNA deep sequencing libraries from small RNAs and variably sized RNA fragments obtained from ribonucleoprotein particle footprinting experiments or fragmentation of long RNAs. Because all enzymatic reactions were optimized and driven to apparent completion, sequence diversity and species abundance in the input sample are well preserved. This optimized method was used in an adapted ribosome-profiling approach to sequence mRNA footprints protected either by 80S monosomes or polysomes in S. cerevisiae. Contrary to popular belief, we show that 80S monosomes are translationally active as demonstrated by strong three-nucleotide phasing of monosome footprints across open reading frames. Most mRNAs exhibit some degree of monosome occupancy, with monosomes predominating on upstream ORFs, canonical ORFs shorter than ~590 nucleotides and any ORF for which the total time required to complete elongation is substantially shorter than the time required for initiation. Additionally, endogenous NMD targets tend to be monosome-enriched. Thus, rather than being inactive, 80S monosomes are significant contributors to overall cellular translation.

Deep sequencing of strand-specific cDNA libraries is now a ubiquitous tool for identifying and quantifying RNAs in diverse sample types. The accuracy of conclusions drawn from these analyses depends on precise and quantitative conversion of the RNA sample into a DNA library suitable for sequencing. Here, we describe an optimized method of preparing strand-specific RNA deep sequencing libraries from small RNAs and variably sized RNA fragments obtained from ribonucleoprotein particle footprinting experiments or fragmentation of long RNAs. Because all enzymatic reactions were optimized and driven to apparent completion, sequence diversity and species abundance in the input sample are well preserved. This optimized method was used in an adapted ribosome-profiling approach to sequence mRNA footprints protected either by 80S monosomes or polysomes in S. cerevisiae. Contrary to popular belief, we show that 80S monosomes are translationally active as demonstrated by strong three-nucleotide phasing of monosome footprints across open reading frames. Most mRNAs exhibit some degree of monosome occupancy, with monosomes predominating on upstream ORFs, canonical ORFs shorter than ~590 nucleotides and any ORF for which the total time required to complete elongation is substantially shorter than the time required for initiation. Additionally, endogenous NMD targets tend to be monosome-enriched. Thus, rather than being inactive, 80S monosomes are significant contributors to overall cellular translation.

Prostate cancer is the second most common cancer in the United States that affects men today. To better treat this disease accurate biomarkers and successful therapeutic treatments are needed. A novel approach to understand the mechanisms behind prostate cancer tumor formation lies in identifying dysregulated micro-RNAs (miRNAs), which are a class of small (18-24 nucleotides) non-coding RNAs that regulate gene expression posttranscriptionally by either inhibiting protein synthesis or signaling messenger-RNA for degradation. Multiple miRNAs were discovered in our highly tumorigenic and metastatic prostate cancer progression model M12 cell line compared to its weakly tumorigenic P69 parental cell line. Various analyses such as human panel analyses, single-miR analyses and patient tumor biopsy samples were analyzed to determine dysregulated miRNAs that contributed to the progression and metastasis of prostate cancer. Together with performing experiments to identify miRNAs, a de novo next generation sequencing approach was applied to identify miRNAs naturally present in biological fluids of normal and healthy subjects. Since, these miRNAs are highly dysregulated in many diseases, including cancer, they can act as potential biomarkers or therapeutic targets to improve treatments for prostate cancer. Essential miRNAs studied for this research were miR-17-3p that is known to target the ErbB2 mRNA; miR-299-5p that directly targets osteopontin (OPN) mRNA, and miR-147b that directly targets many mRNAs, such as COL4A2, ALDH5A1, NDUFA4, SDHD, and IER5. A wide range of miRNAs were identified in six biological fluids: venous blood, menstrual blood, vaginal fluid, semen, saliva, and feces. There were some miRNAs that were common to all 6 body fluids, some unique to each body fluid, and some miRNAs that literature suggested could potentially be biomarkers or normalizers for body fluid characterization.

Chez les angiospermes, la floraison est un processus qui prend part en plusieurs étapes. Le méristème caulinaire, un réservoir de cellule souche d’où émergent la totalité des organes aériens de la plante, va d’abord se différencier en méristème d’inflorescence. Des méristèmes floraux vont alors émerger des flancs du méristème d’inflorescence pour donner naissance aux différents organes qui composent la fleur : les pétales, les sépales, les étamines et le carpelle. Chacune de ces phases est régulée avec finesse par des facteurs de transcription, une famille de protéines se liant à l’ADN pour induire l’activation ou la répression des gènes. Si cette thèse nous a permis de contribuer à la mise à jour de JASPAR, une base de données qui recense des profils liaison de facteurs de transcription, elle a avant tout pour but d’apporter un regard nouveau sur la compréhension des phénomènes qui contrôlent le développement des fleurs à travers l’étude d’une poignée de facteurs de transcription clé dans ce processus. Nous essayerons au mieux d’expliquer les paramètres qui influent sur la liaison de ces facteurs de transcription en utilisant des modèles bioinformatiques associés à des expériences de génomique.Nous nous pencherons d’abord sur les facteurs de réponse à l’auxine à travers l’étude de deux représentants de cette famille de 23 protéines : ARF2 et ARF5. Si les facteurs de transcription de cette famille sont connus pour se lier en dimères, nous avons montré que ARF2 et ARF5 préféraient des espacements différents entre les sites de liaison monomériques sur l’ADN. Nous avons également montré que certaines configurations semblent favoriser l’activation des gènes liés.Ensuite, nous avons étudié LFY, un facteur de transcription maître du développement floral. Nous avons amélioré un modèle de liaison existant et nous avons pu voir que l’intégration de données génomiques de natures diverse permettait de mieux comprendre la liaison du facteur de transcription in vivo.Enfin, nous avons analysé les préférences des facteurs de transcription à boîte MADS, connus pour lier les mêmes séquences d’ADN et dont le rôle est de déterminer l’identité des organes floraux. À travers l’étude du complexe SEP3/AG, qui contrôle la formation du carpelle, nous avons montré que le domaine de tétramérisation de ces facteurs confère une spécificité de liaison expliquant potentiellement que des groupes de facteurs de transcription à boîte MADS régulent la formation d’organes floraux différents en activant des gènes distincts<br>In angiosperms, the development of flowers takes place in several stages. The meristem, a stem cell reservoir from which all the plant’s aerial organs emerge, first differentiate into an inflorescence meristem. Floral meristems then emerge from the flanks of the inflorescence meristem to give birth to the different organs that compose the flower : the petals, sepals, stamens and carpel. Each of these phases is finely regulated by transcription factors, a family of proteins that bind to DNA to induce gene activation or repression. If this thesis allowed us to contribute to the JASPAR database, which gather transcription factor binding profiles, its main goal is to provide a new perspective on the understanding of the phenomena that control flower development through the study of a handful of key transcription factors in the regulation of floral development. We have tried to explain the parameters that influence the binding of these transcription factors using bioinformatics models associated with genomics experiments.We have analysed the auxin response factors (ARF) through the study of two representatives of this family of 23 proteins : ARF2 and ARF5. The transcription factors of this family are known to bind in dimers and we have shown that ARF2 and ARF5 prefer different spacings between monomeric binding sites on DNA. We have also shown that some configurations seem to favour the activation of bound genes.Then, we have studied LFY, a master transcription factor of floral development. We have improved an existing binding model and have seen that the integration of genomic data of various kinds provides a better understanding of the binding of the transcription factor in vivo.Finally, we have analyzed the preferences of MADS box transcription factors, known to bind the same DNA sequences and whose role is to determine the identity of floral organs. Through the study of the SEP3/AG complex, which controls the formation of the carpel, we have found that the tetramerization domain of these factors confers binding specificity, potentially explaining that groups of MADS box transcription factors regulate the formation of different floral organs by activating distinct genes

Le développement du séquençage à haut débit a suscité un regain d'intérêt pour l'étude de divers microbiomes. Parmi eux, le microbiome des légumes fermentés est particulièrement intéressant en raison de son établissement spontané et de sa grande diversité. Cela pose des défis en matière de contrôle de la production ainsi que des propriétés organoleptiques et nutritionnelles. La gestion de la fermentation des aliments repose encore largement sur des connaissances empiriques, car la compréhension des dynamiques des communautés microbiennes et des réseaux métaboliques produisant des produits sûrs et nutritifs reste incomplète. Le paradigme de la science ouverte, notamment la disponibilité de nombreux jeux de données, offre l'opportunité de caractériser ce microbiome de manière plus approfondie. Cette thèse présente diverses méthodes intégratives pour analyser le microbiome des légumes fermentés, en intégrant divers jeux de données publiques afin de caractériser et de comparer de manière exhaustive les microbiomes des légumes fermentés. Tout d'abord, nous avons étudié les dynamiques de succession des communautés bactériennes lors de la fermentation des légumes. Pour cela, nous avons conçu une approche basée sur les réseaux pour comparer dix jeux de données métataxonomiques 16S publiques, comprenant 931 échantillons, et ciblant différents légumes fermentés au fil du temps. Les réseaux construits à partir des jeux de données individuels sont intégrés dans un “core réseau”, mettant en évidence des associations significatives. Cette méthode a permis de mettre en lumière les dynamiques de la fermentation des légumes en caractérisant les processus de succession des communautés parmi différents assemblages bactériens. En second lieu, nous avons utilisé une approche métataxonomique multi-marqueurs pour étudier l'influence des facteurs de production sur les communautés bactériennes lors de la fermentation. Nous avons ensuite examiné la relation entre le core microbiote et les voies métaboliques des légumes fermentés. Pour cela, nous avons analysé de manière exhaustive la diversité microbienne, la composition taxonomique et les profils métaboliques de neuf jeux de données indépendants, incluant 142 échantillons. En utilisant un workflow reproductible, nous avons identifié un ensemble de core espèces et souches microbiennes. Nous les avons reliées à un ensemble de fonctions métaboliques partagées qui peuvent représenter un réseau d'activités importantes durant la fermentation des légumes<br>The development of high-throughput sequencing has sparked renewed interest in studying various microbiomes. Among them, the microbiome of fermented vegetables is particularly intriguing due to its spontaneous establishment and high diversity. This poses challenges in control of production and organoleptic and nutritional properties. The management of food fermentation still relies heavily on empirical knowledge, as understanding the dynamics of microbial communities and the metabolic networks producing safe and nutritious products still needs to be explored. The open science paradigm, especially the availability of numerous datasets, provides an opportunity to characterize this microbiome more in-depth. This thesis presents various integrative methods for analyzing the microbiome of fermented vegetables, incorporating diverse public data to characterize and compare microbiomes across fermented vegetables comprehensively. Firstly, we investigated the succession dynamics of bacterial communities during the fermentation of vegetables. Therefore, we designed a network-based approach to compare ten public metataxonomic 16S datasets, including 931 samples, and targeting different fermented vegetables throughout time. Networks for individual datasets are integrated into a core network, highlighting significant associations. This method sheds light on the dynamics of vegetable fermentation by characterizing the processes of community succession among different bacterial assemblages. On a secondary level, we used a multi-marker metataxonomic approach to study the influence of processing factors on the bacterial communities during the fermentation. We then investigated the relationship between the core microbiota and the core metabolic pathways of fermented vegetables. To this end, we comprehensively analyzed the microbial diversity, taxonomic composition, and metabolic profiles of nine independent datasets, including 142 samples. Using a reproducible analytical workflow with a read-based approach, we identified a core set of microbial species and strains. We linked them to a set of shared metabolic functions that may represent a network of activities important during the fermentation of vegetables

Technological advancements in mass spectrometry allowing quantification of almost complete proteomes make proteomics a key platform for generating unique functional molecular data. Furthermore, the integrative analysis of genomic and proteomic data, termed proteogenomics, has emerged as a new field revealing insights into gene expression regulation, cell signalling, and disease processes. However, the lack of software tools for high-throughput integration and unbiased modification and variant detection hinder efforts for large-scale proteogenomics studies. The main objectives of this work are to address these issues by developing and applying new software tools and data analysis methods. Firstly, I address mapping of peptide sequences to reference genomes. I introduce a novel tool for high-throughput mapping and highlight its unique features facilitating quantitative and post-translational modification mapping alongside accounting for amino acid substitutions. The performance is benchmarked. Furthermore, I offer an additional tool that permits generation of web accessible hubs of genome wide mappings. To enable unbiased identification of post-translational modifications and amino acid substitutions for high resolution mass spectrometry data, I present algorithmic updates the mass tolerant blind spectrum comparison tool ’MS SMiV’. I demonstrate the applicability of the changes by benchmarking against a published mass tolerant database search of a high resolution tandem mass spectrometry dataset. I then present the application of ‘MS SMiV’ on a panel of 50 colorectal cancer cell lines. I show that the adaption of ‘MS SMiV’ outperforms traditional sequence database based identification of single amino acid variants. Furthermore, I highlight the utility of mass tolerant spectrum matching in combination with isobaric labelled quantitative proteomics in distinguishing between post-translational modifications and amino acid variants of similar mass. In the last part of this work I integrate both tools with a high-throughput proteogenomic identification pipeline and apply it to a pilot study of chondrocytes derived from 12 osteoarthritic individuals. I show the value of this approach in identifying variation between individuals and molecular levels and highlight them with individual examples. I show that multi-plexed proteogenomics can be used to infer genotypes of individuals.

This thesis examines the interplay between national copyright and patent laws, and informal user generated norms in the governance of open source bioinformatics projects. Bioinformatics describes an interdisciplinary merger between computer science, statistics and molecular biology for quantitative biology analysis. As for many computationally driven scientific fields, there is a strong initiative both by researchers inside and outside the field to encourage collaborative research through open source software and data licensing. However, a trend towards seeking exclusive copyright and patent protection for bioinformatics algorithms could foster exclusivity and discourage collaboration in bioinformatics. Whether this effect exists within bioinformatics research, and if so is best resolved through national copyright and patent reform or private ordering strategies (which are already present through open source licensing) is a matter of open debate. This thesis explores these issues using a mixed methods, grounded theory framework that compares open licensing of bioinformatics software across the US, the EU, Australia and New Zealand. This framework operates on three levels: firstly, with a doctrinal analysis of copyright and patent laws (as well as related sui generis rights for data compilations); secondly, with a quantitative analysis of patent applications in bioinformatics and forward citation rates for patent publication pairs to determine whether the grant of these patents has a negative effect on citation rates; and thirdly, through semi-structured interviewing of bioinformaticians who release open source software and also seek patent protection. Each of these layers of analysis reveals that national patent laws do not appear to have a significant effect on the formation and governance of open source bioinformatics communities, but law reform targeted at encouraging private ordering strategies through copyright licensing might have a positive effect. This thesis concludes by offering recommendations on assessing how private ordering strategies in open source bioinformatics can be improved to encourage collaborative research.

<p>Chromatin structure plays an important role in gene regulation, especially in differentiating the diverse cell types in humans. In this dissertation, we analyze the nucleosome positioning and open chromatin profiles genome-wide and investigate the relationship with transcription initiation, the activity of regulatory elements, and expression levels. We mainly focus on the results of DNase-seq experiments, but also employ annotations from MNase-seq, FAIRE-seq, ChIP-seq, CAGE, and RNA microarrays. Our methods are based on computational approaches including managing large data sets, statistical analysis, and machine learning. We find that different transcription initiation patterns lead to distinct chromatin structures, suggesting diverse regulatory strategies. Moreover, we present a tool for comparing genome-wide annotation tracks and evaluate DNase-seq against a unique assay for detecting open chromatin. We also demonstrate how DNase-seq can be used to successfully predict rotationally stable nucleosomes that are conserved across cell types. We conclude that DNase-seq can be used to study genome-wide chromatin structure in an effort to better understand how it regulates gene expression.</p><br>Dissertation

abstract: It is increasingly common to see machine learning techniques applied in conjunction with computational modeling for data-driven research in neuroscience. Such applications include using machine learning for model development, particularly for optimization of parameters based on electrophysiological constraints. Alternatively, machine learning can be used to validate and enhance techniques for experimental data analysis or to analyze model simulation data in large-scale modeling studies, which is the approach I apply here. I use simulations of biophysically-realistic cortical neuron models to supplement a common feature-based technique for analysis of electrophysiological signals. I leverage these simulated electrophysiological signals to perform feature selection that provides an improved method for neuron-type classification. Additionally, I validate an unsupervised approach that extends this improved feature selection to discover signatures associated with neuron morphologies - performing in vivo histology in effect. The result is a simulation-based discovery of the underlying synaptic conditions responsible for patterns of extracellular signatures that can be applied to understand both simulation and experimental data. I also use unsupervised learning techniques to identify common channel mechanisms underlying electrophysiological behaviors of cortical neuron models. This work relies on an open-source database containing a large number of computational models for cortical neurons. I perform a quantitative data-driven analysis of these previously published ion channel and neuron models that uses information shared across models as opposed to information limited to individual models. The result is simulation-based discovery of model sub-types at two spatial scales which map functional relationships between activation/inactivation properties of channel family model sub-types to electrophysiological properties of cortical neuron model sub-types. Further, the combination of unsupervised learning techniques and parameter visualizations serve to integrate characterizations of model electrophysiological behavior across scales.<br>Dissertation/Thesis<br>Doctoral Dissertation Applied Mathematics 2020

<p>Course-based Undergraduate Research Experiences (CUREs) have been described in a range of educational contexts. Although various learning objectives, termed anticipated learning outcomes (ALOs) in this project, have been proposed, processes for identifying them may not be rigorous or well-documented, which can lead to inappropriate assessment and speculation about what students actually learn from CUREs. Additionally, evaluation of CUREs has primarily relied on student and instructor perception data rather than more reliable measures of learning.This dissertation investigated a novel biochemistry laboratory curriculum for a Course-based Undergraduate Research Experience (CURE) known as the Biochemistry Authentic Scientific Inquiry Lab (BASIL). Students participating in this CURE use a combination of computational and biochemical wet-lab techniques to elucidate the function of proteins of known structure but unknown function. The goal of the project was to evaluate the efficacy of the BASIL CURE curriculum for developing students’ research abilities across implementations. Towards achieving this goal, we addressed the following four research questions (RQs): <b>RQ1</b>) How can ALOs be rigorously identified for the BASIL CURE; <b>RQ2</b>) How can the identified ALOs be used to develop a matrix that characterizes the BASIL CURE; <b>RQ3</b>) What are students’ perceptions of their knowledge, confidence and competence regarding their abilities to perform the top-rated ALOs for this CURE; <b>RQ4</b>) What are appropriate assessments for student achievement of the identified ALOs and what is the nature of student learning, and related difficulties, developed by students during the BASIL CURE? To address these RQs, this project focused on the development and use of qualitative and quantitative methods guided by constructivism and situated cognition theoretical frameworks. Data was collected using a range of instruments including, content analysis, Qualtrics surveys, open-ended questions and interviews, in order to identify ALOs and to determine student learning for the BASIL CURE. Analysis of the qualitative data was through inductive coding guided by the concept-reasoning-mode (CRM) model and the assessment triangle, while analysis of quantitative data was done by using standard statistical techniques (e.g. conducting a parried t-test and effect size). The results led to the development of a novel method for identifying ALOs, namely a process for identifying course-based undergraduate research abilities (PICURA; RQ1; Irby, Pelaez, & Anderson 2018b). Application of PICURA to the BASIL CURE resulted in the identification and rating by instructors of a wide range of ALOs, termed course-based undergraduate research abilities (CURAs), which were formulated into a matrix (RQs 2; Irby, Pelaez, & Anderson, 2018a,). The matrix was, in turn, used to characterize the BASIL CURE and to inform the design of student assessments aimed at evaluating student development of the identified CURAs (RQs 4; Irby, Pelaez, & Anderson, 2018a). Preliminary findings from implementation of the open-ended assessments in a small case study of students, revealed a range of student competencies for selected top-rated CURAs as well as evidence for student difficulties (RQ4). In this way we were able to confirm that students are developing some of the ALOs as actual learning outcomes which we term VLOs or verified learning outcomes. In addition, a participant perception indicator (PPI) survey was used to gauge students’ perceptions of their gains in knowledge, experience, and confidence during the BASIL CURE and, therefore, to inform which CURAs should be specifically targeted for assessment in specific BASIL implementations (RQ3;). These results indicate that, across implementations of the CURE, students perceived significant gains with large effect sizes in their knowledge, experience, and confidence for items on the PPI survey (RQ3;). In our view, the results of this dissertation will make important contributions to the CURE literature, as well as to the biochemistry education and assessment literature in general. More specifically, it will significantly improve understanding of the nature of student learning from CUREs and how to identify ALOs and design assessments that reveal what students actually learn from such CUREs - an area where there has been a dearth of available knowledge in the past. The outcomes of this dissertation could also help instructors and administrators identify and align assessments with the actual features of a CURE (or courses in general), use the identified CURAs to ensure the material fits departmental or university needs, and evaluate the benefits of students participating in these innovative curricula. Future research will focus on expanding the development and validation of assessments so that practitioners can better evaluate the efficacy of their CUREs for developing the research competencies of their undergraduate students and continue to render improvements to their curricula.</p>