Dissertations / Theses on the topic 'Metabolite set enrichment analysis'

Background With the current technological advances in high-throughput biology, the necessity to develop tools that help to analyse the massive amount of data being generated is evident. A powerful method of inspecting large-scale data sets is gene set enrichment analysis (GSEA) and investigation of protein structural features can guide determining the function of individual genes. However, a convenient tool that combines these two features to aid in high-throughput data analysis has not been developed yet. In order to fill this niche, we developed the user-friendly, web-based application, PhenoFam. Results PhenoFam performs gene set enrichment analysis by employing structural and functional information on families of protein domains as annotation terms. Our tool is designed to analyse complete sets of results from quantitative high-throughput studies (gene expression microarrays, functional RNAi screens, etc.) without prior pre-filtering or hits-selection steps. PhenoFam utilizes Ensembl databases to link a list of user-provided identifiers with protein features from the InterPro database, and assesses whether results associated with individual domains differ significantly from the overall population. To demonstrate the utility of PhenoFam we analysed a genome-wide RNA interference screen and discovered a novel function of plexins containing the cytoplasmic RasGAP domain. Furthermore, a PhenoFam analysis of breast cancer gene expression profiles revealed a link between breast carcinoma and altered expression of PX domain containing proteins. Conclusions PhenoFam provides a user-friendly, easily accessible web interface to perform GSEA based on high-throughput data sets and structural-functional protein information, and therefore aids in functional annotation of genes.

Background With the current technological advances in high-throughput biology, the necessity to develop tools that help to analyse the massive amount of data being generated is evident. A powerful method of inspecting large-scale data sets is gene set enrichment analysis (GSEA) and investigation of protein structural features can guide determining the function of individual genes. However, a convenient tool that combines these two features to aid in high-throughput data analysis has not been developed yet. In order to fill this niche, we developed the user-friendly, web-based application, PhenoFam. Results PhenoFam performs gene set enrichment analysis by employing structural and functional information on families of protein domains as annotation terms. Our tool is designed to analyse complete sets of results from quantitative high-throughput studies (gene expression microarrays, functional RNAi screens, etc.) without prior pre-filtering or hits-selection steps. PhenoFam utilizes Ensembl databases to link a list of user-provided identifiers with protein features from the InterPro database, and assesses whether results associated with individual domains differ significantly from the overall population. To demonstrate the utility of PhenoFam we analysed a genome-wide RNA interference screen and discovered a novel function of plexins containing the cytoplasmic RasGAP domain. Furthermore, a PhenoFam analysis of breast cancer gene expression profiles revealed a link between breast carcinoma and altered expression of PX domain containing proteins. Conclusions PhenoFam provides a user-friendly, easily accessible web interface to perform GSEA based on high-throughput data sets and structural-functional protein information, and therefore aids in functional annotation of genes.

The DNA microarray biotechnology simultaneously monitors the expression of thousands of genes and aims to identify genes that are differently expressed under different conditions. From the statistical point of view, it can be restated as identify genes strongly associated with the response or covariant of interest. The Gene Set Enrichment Analysis (GSEA) method is one method which focuses the analysis at the functional related gene sets level instead of single genes. It helps biologists to interpret the DNA microarray data by their previous biological knowledge of the genes in a gene set. GSEA has been shown to efficiently identify gene sets containing known disease-related genes in the real experiments. Here we want to evaluate the statistical power of this method by simulation studies. The results show that the the power of GSEA is good enough to identify the gene sets highly associated with the response or covariant of interest.

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.
Includes bibliographical references (p. 89-90).
Manually curated gene sets related to a biological function often contain genes that are not tightly co-regulated transcriptionally. which obscures the evidence of coordinated differential expression of these gene sets in relevant experiments. To address this problem, we explored strategies to refine the manually curated subcollection of the Molecular Signatures Database (MSigDB) for use with Gene Set Enrichment Analysis (GSEA). We examined the manually curated gene sets in context of an atlas of gene expression of many normal human tissues. To refine gene sets, we clustered the genes in each set based on co-expression across the tissues to produce more tightly co-regulated children gene sets that are also likely more accurate representations of the biological process or processes described by the gene set. We evaluated the performance of the clustering algorithms by refining gene sets in the context of several published GSEA analyses and verifying that the children gene sets score higher with GSEA than do the parents. We created and annotated a new, refined version of a large portion of the manually curated component of MSigDB, which we hope will be a resource for the GSEA community.
by Yuliya Kodysh.
M.Eng.

Microarray technology allows measurement of the expression levels of thousand of genes simultaneously. Several gene set analysis (GSA) methods are widely used for extracting useful information from microarrays, for example identifying differentially expressed pathways associated with a particular biological process or disease phenotype. Though GSA methods like Gene Set Enrichment Analysis (GSEA) are widely used for pathway analysis, these methods are solely based on statistics. Such methods can be awkward to use if knowledge of specific pathways involved in particular biological processes are the aim of the study. Here we present a novel method (Knowledge Based Gene Set Analysis: KB-GSA) which integrates knowledge about user-selected pathways that are known to be involved in specific biological processes. The method generates an easy to understand graphical visualization of the changes in expression of the genes, complemented with some common statistics about the pathway of particular interest.

Pathway analysis is an important task for gaining novel insights into the molecular architecture of many complex diseases. With the advancement of new sequencing technologies, a large amount of quantitative gene expression data have been continuously acquired. The springing up omics data sets such as proteomics has facilitated the investigation on disease relevant pathways. Although much work has previously been done to explore the single omics data, little work has been reported using multi-omics data integration, mainly due to methodological and technological limitations. While a single omic data can provide useful information about the underlying biological processes, multi-omics data integration would be much more comprehensive about the cause-effect processes responsible for diseases and their subtypes. This project investigates the combination of miRNAseq, proteomics, and RNAseq data on seven types of muscular dystrophies and control group. These unique multi-omics data sets provide us with the opportunity to identify disease-specific and most relevant biological pathways. We first perform t-test and OVEPUG test separately to define the differential expressed genes in protein and mRNA data sets. In multi-omics data sets, miRNA also plays a significant role in muscle development by regulating their target genes in mRNA dataset. To exploit the relationship between miRNA and gene expression, we consult with the commonly used gene library - Targetscan to collect all paired miRNA-mRNA and miRNA-protein co-expression pairs. Next, by conducting statistical analysis such as Pearson's correlation coefficient or t-test, we measured the biologically expected correlation of each gene with its upstream miRNAs and identify those showing negative correlation between the aforementioned miRNA-mRNA and miRNA-protein pairs. Furthermore, we identify and assess the most relevant disease-specific pathways by inputting the differential expressed genes and negative correlated genes into the gene-set libraries respectively, and further characterize these prioritized marker subsets using IPA (Ingenuity Pathway Analysis) or KEGG. We will then use Fisher method to combine all these p-values derived from separate gene sets into a joint significance test assessing common pathway relevance. In conclusion, we will find all negative correlated paired miRNA-mRNA and miRNA-protein, and identifying several pathophysiological pathways related to muscular dystrophies by gene set enrichment analysis. This novel multi-omics data integration study and subsequent pathway identification will shed new light on pathophysiological processes in muscular dystrophies and improve our understanding on the molecular pathophysiology of muscle disorders, preventing and treating disease, and make people become healthier in the long term.
Master of Science

In the last decade, molecular biology has expanded from a reductionist view to a systems-wide view that tries to unravel the complex interactions of cellular components. Owing to the emergence of high-throughput technology it is now possible to interrogate entire genomes at an unprecedented resolution. The dimension and unstructured nature of these data made it evident that new methodologies and tools are needed to turn data into biological knowledge. To contribute to this challenge we exploited the wealth of publicly available high-throughput genomics data and developed bioinformatics methodologies focused on extracting information at the pathway rather than the single gene level. First, we developed Gene Set Variation Analysis (GSVA), a method that facilitates the organization and condensation of gene expression profiles into gene sets. GSVA enables pathway-centric downstream analyses of microarray and RNA-seq gene expression data. The method estimates sample-wise pathway variation over a population and allows for the integration of heterogeneous biological data sources with pathway-level expression measurements. To illustrate the features of GSVA, we applied it to several use-cases employing different data types and addressing biological questions. GSVA is made available as an R package within the Bioconductor project. Secondly, we developed a pathway-centric genome-based strategy to reposition drugs in type 2 diabetes (T2D). This strategy consists of two steps, first a regulatory network is constructed that is used to identify disease driving modules and then these modules are searched for compounds that might target them. Our strategy is motivated by the observation that disease genes tend to group together in the same neighborhood forming disease modules and that multiple genes might have to be targeted simultaneously to attain an effect on the pathophenotype. To find potential compounds, we used compound exposed genomics data deposited in public databases. We collected about 20,000 samples that have been exposed to about 1,800 compounds. Gene expression can be seen as an intermediate phenotype reflecting underlying dysregulatory pathways in a disease. Hence, genes contained in the disease modules that elicit similar transcriptional responses upon compound exposure are assumed to have a potential therapeutic effect. We applied the strategy to gene expression data of human islets from diabetic and healthy individuals and identified four potential compounds, methimazole, pantoprazole, bitter orange extract and torcetrapib that might have a positive effect on insulin secretion. This is the first time a regulatory network of human islets has been used to reposition compounds for T2D. In conclusion, this thesis contributes with two pathway-centric approaches to important bioinformatic problems, such as the assessment of biological function and in silico drug repositioning. These contributions demonstrate the central role of pathway-based analyses in interpreting high-throughput genomics data.
En l'última dècada, la biologia molecular ha evolucionat des d'una perspectiva reduccionista cap a una perspectiva a nivell de sistemes que intenta desxifrar les complexes interaccions entre els components cel•lulars. Amb l'aparició de les tecnologies d'alt rendiment actualment és possible interrogar genomes sencers amb una resolució sense precedents. La dimensió i la naturalesa desestructurada d'aquestes dades ha posat de manifest la necessitat de desenvolupar noves eines i metodologies per a convertir aquestes dades en coneixement biològic. Per contribuir a aquest repte hem explotat l'abundància de dades genòmiques procedents d'instruments d'alt rendiment i disponibles públicament, i hem desenvolupat mètodes bioinformàtics focalitzats en l'extracció d'informació a nivell de via molecular en comptes de fer-ho al nivell individual de cada gen. En primer lloc, hem desenvolupat GSVA (Gene Set Variation Analysis), un mètode que facilita l'organització i la condensació de perfils d'expressió dels gens en conjunts. GSVA possibilita anàlisis posteriors en termes de vies moleculars amb dades d'expressió gènica provinents de microarrays i RNA-seq. Aquest mètode estima la variació de les vies moleculars a través d'una població de mostres i permet la integració de fonts heterogènies de dades biològiques amb mesures d'expressió a nivell de via molecular. Per il•lustrar les característiques de GSVA, l'hem aplicat a diversos casos usant diferents tipus de dades i adreçant qüestions biològiques. GSVA està disponible com a paquet de programari lliure per R dins el projecte Bioconductor. En segon lloc, hem desenvolupat una estratègia centrada en vies moleculars basada en el genoma per reposicionar fàrmacs per la diabetis tipus 2 (T2D). Aquesta estratègia consisteix en dues fases: primer es construeix una xarxa reguladora que s'utilitza per identificar mòduls de regulació gènica que condueixen a la malaltia; després, a partir d'aquests mòduls es busquen compostos que els podrien afectar. La nostra estratègia ve motivada per l'observació que els gens que provoquen una malaltia tendeixen a agrupar-se, formant mòduls patogènics, i pel fet que podria caldre una actuació simultània sobre múltiples gens per assolir un efecte en el fenotipus de la malaltia. Per trobar compostos potencials, hem usat dades genòmiques exposades a compostos dipositades en bases de dades públiques. Hem recollit unes 20.000 mostres que han estat exposades a uns 1.800 compostos. L'expressió gènica es pot interpretar com un fenotip intermedi que reflecteix les vies moleculars desregulades subjacents a una malaltia. Per tant, considerem que els gens d'un mòdul patològic que responen, a nivell transcripcional, d'una manera similar a l'exposició del medicament tenen potencialment un efecte terapèutic. Hem aplicat aquesta estratègia a dades d'expressió gènica en illots pancreàtics humans corresponents a individus sans i diabètics, i hem identificat quatre compostos potencials (methimazole, pantoprazole, extracte de taronja amarga i torcetrapib) que podrien tenir un efecte positiu sobre la secreció de la insulina. Aquest és el primer cop que una xarxa reguladora d'illots pancreàtics humans s'ha utilitzat per reposicionar compostos per a T2D. En conclusió, aquesta tesi aporta dos enfocaments diferents en termes de vies moleculars a problemes bioinformàtics importants, com ho son el contrast de la funció biològica i el reposicionament de fàrmacs "in silico". Aquestes contribucions demostren el paper central de les anàlisis basades en vies moleculars a l'hora d'interpretar dades genòmiques procedents d'instruments d'alt rendiment.

Le prolapsus de la valve mitrale (MVP) est une valvulopathie fréquente qui touche près de 1 personne sur 40 dans la population générale. Il s'agit de la première indication de réparation et / ou de remplacement de la valve. De nombreux gènes comme FLNA, DCHS1 pour les formes familiales et TNS1 et LMCD1 pour les formes sporadiques ont récemment été décrit comme associés au MVP. Cependant, les défauts génétiques touchant ces gènes n'expliquent pas tous les cas du MVP. De plus, les mécanismes biologiques expliquant la susceptibilité génétique au MVP, notamment pour les formes sporadiques les plus fréquentes restent mal compris. Dans cette thèse, mon objectif était 1) de caractériser globalement les mécanismes biologiques impliqués dans le risque génétique du MVP dans le contexte des études d'association pangénomique (GWAS), et 2) d'améliorer la résolution du génotypage par l'imputation génétique et par l'addition d'une nouvelle étude cas témoins, (UKBioBank) afin de permettre la découverte de nouveaux loci de prédisposition. Dans la première partie, j'ai appliqué des outils d'enrichissement de voies biologiques ou sets de gènes (i-GSEA4GWAS, DEPICT) aux données GWAS. J'ai pu montrer que les gènes présents autour des loci GWAS sont impliqués dans l'organisation des filaments d'actine, l'organisation du cytosquelette et le développement cardiaque. Nous avons également décrits de nombreux régulateurs de la transcription impliqués le développement, la prolifération cellulaire et la migration, comme le gène GLIS1 qui joue un rôle dans les transitions morphologiques cellulaires (EndoMT, MET). Afin de confirmer le rôle de GLIS1 dans l'association avec le MVP, j'ai réalisé une analyse génétique dans UKBiobank et, en combinaison avec les données françaises, l'association a atteint le seuil de significativité génomique. Des expériences d'immunohistochimie ont indiqué que Glis1, la protéine orthologue de la souris est exprimée au cours du développement embryonnaire principalement dans les noyaux des cellules endothéliales et interstitielles des valves mitrales. D'autre part, l'inactivation de Glis1 à l'aide d'oligonucléotides de type Morpholinos ont été l'origine d'une régurgitation atrio-ventriculaire chez le poisson zèbre. Dans la deuxième partie, j'ai généré des données de génotypage plus dense à l'aide d'une imputation basée sur Haplotype Reference Consortium (HRC) et TOPMed. J'ai d'abord comparé la précision d'imputation entre les données utilisant les différents panels et constaté qu'aucun panel n'atteignait une précision optimale pour les variants rares (MAF <0,01) dans nos échantillons. La précision d'imputation s'améliorait pour les variants fréquents (MAF> 0,05), en particulier pour les cohortes dont le génotypage étaient réalisé avec des puces identiques. J'ai pu ainsi cartographier avec plus de précision les loci déjà confirmés (ex. Chr 2 autour de TNS1). J'ai également identifié 6 nouveaux loci associés au MVP prometteurs. Les nouveaux variants associés sont tous fréquents. L'annotation fonctionnelle fine à l'aide de données publiques a indiqué leurs rôles potentiels dans la régulation transcriptionnelle de plusieurs gènes candidats (ex. PDGFD et ACTN4). En résumé, mes travaux de thèse ont apporté des résultats génétiques originaux mettant en lumière de nouveaux mécanismes biologiques en rapport avec la biologie et le développement de la valve. Ces travaux ont fait appel à de nombreuses stratégies génétiques d'association et d'enrichissement, d'imputation haute densité et d'annotations fonctionnelles. Mes travaux ont également été renforcés par des validations dans des modèles animaux en collaboration. Il sera nécessaire toutefois de confirmer par réplication, et potentiellement par des expériences biologiques, les résultats nouveaux issus des travaux d'imputation haute densité afin de déclarer ces nouveaux gènes de prédispositions au MVP
Mitral valve prolapse (MVP) is a common heart valve disease affecting nearly 1 in 40 individuals in the general population. It is the first indication for valve repair and/or replacement and moreover, a risk factor for mitral regurgitation, an established cause of endocarditis and sudden death. MVP is characterized by excess extracellular matrix secretion and cellular disorganization which leads to bulky valves that are unable to coapt correctly during ventricular systole. Even though several genes including FLNA, DCHS1 TNS1, and LMCD1 were reported to be associated with MVP, these explain partially its heritability. However, understanding the biological mechanisms underlying the genetic susceptibility to MVP is necessary to characterize its triggering mechanisms. In this thesis, I aimed 1) to characterize globally the biological mechanisms involved in the genetic risk for MVP in the context of genome-wide association studies (GWAS), and 2) improve the genotyping resolution using genetic imputation, which allowed the discovery of additional risk genes for MVP. In the first part of my study, I applied pathway enrichment tools (i-GSEA4GWAS, DEPICT) to the GWAS data. I was able to show that genes at risk loci are involved in biological functions relevant to actin filament organization, cytoskeleton biology, and cardiac development. The enrichment for positive regulation of transcription, cell proliferation, and migration motivated the follow-up of GLIS1, a transcription factor that regulates Hedgehog signalling. I followed up the association with MVP in a dataset of cases and controls from the UK Biobank and, in combination with previously available data, I found a genome-wide significant association with MVP (OR=1.22, P=4.36 ×10-10). Through collaborative efforts, immunohistochemistry experiments in mouse indicated that Glis1 is expressed during embryonic development predominantly in nuclei of endothelial and interstitial cells of mitral valves, while Glis1 knockdown using morpholinos caused atrioventricular regurgitation in zebrafish. In the second part of my work, I generated larger genotyping datasets using a imputation based on Haplotyp Refernece Consortium and TOPMed, two large and highly dense imputation panels that were recently made available. I first compared the imputation accuracy between data using HRC and TopMED and found that both panels have low imputation accuracy for rare allele (MAF<0.01). However, the imputation accuracy increased with the input sample size for common variants (MAF>0.05), especially when genotyping platforms were harmonised. I was able to fine map established loci (e.g Chr 2) and also able to identify six novel and promising associated loci. All new loci are driven by common variants that I confirmed as high profile regulatory variants through an extensive computationally-based functional annotations at promising loci that pointed at several candidate genes for valve biology and development (e.g PDGFD and ACTN4). In summary, my PhD work applied up-to-data high throughput genetic association methods and functional enrichment and annotation to GWAS data. My results provide novel insights into the genetics, molecular and cellular basis of valve disease. Further genetic confirmation through replication, but also through biological experiments are expected to consolidate these statistically and computationally supported results

碩士
國立臺灣大學
農藝學研究所
104
During the past few years, RNA-Seq technology has been widely employed for studying the transcriptome since it has clear advantages over the other transcriptomic technologies. The most popular use of RNA-seq applications is to identify differentially expressed genes. In addition, gene set analysis (GSA) aims to determine whether a predefined gene set, in which the genes share a common biological function, is correlated with the pheno-type. To date, many GSA approaches have been developed for identifying differentially expressed gene sets using microarray data. However, these methods are not directly ap-plicable to RNA-seq data due to intrinsic difference between two data structures. When testing the differential expression of gene sets, there is a critical assumption that the mem-bers in each gene set are sampled independently in most GSA methods. It means that the genes within a gene set don’t share a common biological function. In order to resolve this issue, we propose a GSA method based on the De-correlation (DECO) algorithm by Dougu Nam (2010) to remove the correlation bias in the expression of each gene set. We study the performance of our proposed method compared with other GSA methods through simulation studies under various scenarios combining with four different normal-ization methods. As a result, we found that our proposed method outperforms the others in terms of Type I error rate and empirical power.

碩士
國立成功大學
資訊工程學系碩博士班
97
In recent years, DNA microarrays have been widely not only applied on gene functional role analysis but also supported on interaction information across different species. Furthermore, it has the advantage of quickly obtaining gene profiles through whole genome. However, how to evaluate gene expression level is still a tough problem. To overcome this problem, Gene Set Enrichment Analysis (GSEA) was proposed in 2005 for better interpreting microarray expression data. GSEA focus on gene sets, groups of genes that share common biological concepts. Based on GSEA, our study is to develop a powerful system aiming at standardizing, analyzing and generating results including biological interaction networks and experiment-associated pathway maps. Moreover, we provide important regulating sub-networks across biological concepts with literature annotation and visualizing those networks. miRNA has been discovered recently as a stable regulator and several researches reveal that miRNA plays an important role in regulated genes involved pathway. Our system provides relationships between miRNA and pathway under disease condition. We retrieve microarray data (GSE4479) from Gene Expression Omnibus (GEO) and analyze the data through our pipeline. The results show that miR-155 suppresses the expression level of SMAD2 and miR-125b inhibits the expression level of ERBB3 in glioma. Besides, the expression of glioma pathway is significantly enriched. We hope the tool can support for mining more information and the results can be provided for implications to miRNA research.

碩士
國立臺灣大學
農藝學研究所
107
The first part of this paper is to study the effectiveness of differentially expressed gene analysis. Statistical methods such as t-test or SAM treat each gene as independent and separately identify whether it is a differentially expressed gene. However, the results of the test may be biased because of the correlation between genes. Therefore, a novel statistic called OR value is proposed for identifying differentially expressed genes recently. The advantage of OR value is no model assumptions and no estimated parameters, as well as the Euclidean distance is used to consider the correlation between genes and the dispersion of data. In this paper, multivariate normal distribution, multivariate t distribution, and mixed distribution are used to simulate gene expression data, and then the OR value is used to identify whether the gene is a differentially expressed gene, and compared it to the commonly used t-test and non-OR methods. The results show that the weighted quantile difference method using OR value performs well in all cases, especially in the multivariate t distribution with a high correlation coefficient and the mixed distribution with shift amount greater than 0. The second aim of this paper is gene set analysis (GSA) using the self-contained hypothesis. Adjustments for the GSA method is carried out using statistics in the first part, and we also compared it to commonly used gene set analysis methods. The results show that only in the multivariate t distribution, the distance-based methods such as the sum of the quantile difference, the sum of the weighted quantile difference and the energy test method perform better than other methods, and there is no apparent method outperforming others under other conditions. Finally, we applied the OR-based method and competing methods to a large scale dataset from a group of breast cancer patients to perform the differentially expressed gene and gene set analysis. In summary, the OR value is a worthwhile method when performing the differentially expressed gene analysis, but a more robust statistic may be needed to extend the analysis for gene-set level.

Gene set enrichment analysis aims to discover sets of genes, such as biological pathways or protein complexes, which may show moderate but coordinated differentiation across experimental conditions. The existing gene set enrichment approaches utilize single gene statistic as a measure of differentiation for individual genes. These approaches do not utilize any inter-gene correlations, but it has been known that genes in a pathway often interact with each other. Motivated by the need for taking gene dependence into account, we propose a novel gene set enrichment algorithm, where the gene-gene correlation is addressed via a gene-pair representation strategy. Relying on an appropriately defined gene pair statistic, the gene set statistic is formulated using a competitive null hypothesis. Extensive simulation studies show that our proposed approach can correctly control the type I error (false positive rate), and retain good statistical power for detecting true differential expression. The new method is also applied to analyze several gene expression datasets.
October 2016

碩士
亞洲大學
生物與醫學資訊學系碩士在職專班
100
Gaining a better understanding of the biotic and abiotic stress responses for plant systems provide a model system for studying human diseases and drug-related research. Understanding how plant systems defense against environment stress is of great significance for the world's food and agricultural production.In this study, the microarray data for Arabidopsis thaliana infected with Xanthomonas campestris pv. campestris （Xcc） is analyzed. Microarray data for Arabidopsis infected with Xcc are retrieved from the ArrayExpress database, where differentially expressed genes （DEGs） and Gene Set Enrichment Analysis (GSEA) for pathogen-resistant pathways are deduced by adopting the R language, Bioconductor and KEGG information. Finally, the found results are validated by published literature.It is found that the SGT1 and HSP90 protein complexes utilize the SKp1 protein in the ubiquitin - proteasome system to regulate the hypersensitivity resistance mechanism, which is mediated by the resistance protein RPM1.Furthermore, DEG results for Xcc under different experimental conditions, as well as bacteria Agrobacterium tumefaciens, are also determined. The mentioned results can be accessed at http://ppi.bioinfo.asia.edu.tw/R_At_xcc/index.htm .

The semiconducting compounds CdTe and CdZnTe have important applications in high-energy radiation detectors and as substrates for infrared devices. The materials offer large band gaps, high resistivity, and excellent charge transport properties; however all of these properties rely on very precise control of the material composition. Growing bulk crystals by the travelling heater method (THM) offers excellent compositional control and fewer defects compared to gradient freezing, but it is also much slower and more expensive. A particular challenge is the current need to grow new crystals onto existing seeds of similar size and quality. Simulations and experiments are used in this work to investigate the feasibility of growing these materials by THM without the use of large seed crystals. A new fixed-grid, multiphase finite element model was developed based on the level set method and used to calculate the mass transport regime and interface shapes inside the growth ampoule. The diffusivity of CdTe in liquid tellurium was measured through dissolution experiments, which also served to validate the model. Simulations of tapered THM growth find conditions similar to untapered growth with interface shapes that are sensitive to strong thermosolutal convection. Favourable growth conditions are achievable only if convection can be controlled. In preliminary experiments, tapered GaSb crystals were successfully grown by THM and large CdTe grains were produced by gradient freezing. Beginning with this seed material, 25 mm diameter CdTe and CdZnTe crystals were grown on 10 mm diameter seeds, and 65 mm diameter CdTe on 25 mm seeds. Unseeded THM growth was also investigated, as well as ampoule rotation and a range of thermal conditions and ampoule surface coatings. Outward growth beyond one or two centimeters was achieved only at small diameters and included secondary grains and twin defects; however, limited outward growth of larger seeds and agreement between experimental and numerical results suggest that tapered growth may be achievable in the future. This would require active temperature control at the base of the crystal and reduction of convection through thermal design or by rotation of the ampoule or applied magnetic fields.
Graduate
0346
0794
0548
jordan.roszmann@gmail.com