Thesis (Ph.D.) -- University of Texas Southwestern Medical Center at Dallas, 2005. / Not embargoed. Vita. Bibliography: 100-109.

Thesis (Ph.D.) -- University of Texas Southwestern Medical Center at Dallas, 2006. / Partial embargo. Vita. Bibliography: References located at the end of each study.

Thesis (Ph.D.)--McMaster University, 2003. / Advisor: A. K. Grover. Also available via World Wide Web.

Thesis (Ph. D.)--University of Washington, 2007. / Vita. Includes bibliographical references (leaves 84-91).

Accumulating evidence from epidemiological, wildlife, and laboratory studies indicates that abnormalities of reproduction, development and physiology can be ascribed to environmental contaminants with biological activities. Many such contaminants disrupt essential hormone-regulated processes by virtue of their ability to interact with nuclear hormone receptors (endocrine disrupting chemicals, EDC). Of particular concern are chemicals that mimic or block estrogen signaling (xenoestrogens, XE) through their direct interaction with estrogen receptors (ER). The current model of XE action focuses on disrupted transcriptional activity, as measured by changes in the expression of ER-regulated genes. However, transcription is tightly coupled to splicing, by which a single target gene transcript is processed to multiple structurally and functionally different mRNAs. In theory, any XE that interacts with ER to regulate transcription has the potential to disrupt splicing, thereby affecting not only mRNA quantity but also quality. To address this hypothesis, alternative splicing of the gene encoding ER alpha (esr1), itself an estrogen responsive gene, was investigated. In these studies, killifish (Fundulus heteroclitus), an environmentally relevant species, and zebrafish (Danio rerio), an advantageous laboratory fish model, were used. First, the occurrence of ER alpha splice variants in adult tissues, in developing embryos and in response to estrogens in the two species was documented. Additionally, the effects of long-term, multigenerational XE exposure on ER alpha splicing were examined in two killifish populations, one from an estrogenic (polluted) site and a second population from a reference (unpolluted) site. A subset of ER alpha variants from killifish was expressed in cell culture to document their transcriptional activities. To determine the in vivo relationship between estrogen responsiveness and an ER alpha splice variant of interest, esr1 splicing was experimentally altered in living embryos by microinjecting morpholino oligonucleotides, and changes in induction of a panel of estrogen responsive gene targets were measured as markers of effect. These results provide evidence that dysregulation of mRNA processing is also a mechanism of XE action, and suggest that resultant ER alpha splice variants mediate the short-term effects of estrogen disruption and are also part of the adaptive response to long-term, multigenerational XE exposures in the natural environment.

Next-generation sequencing (NGS) technologies have revolutionised research into nature and diversity of genomes and transcriptomes. Since the initial description of these technology platforms over a decade ago, massively parallel RNA sequencing (RNA-seq) has driven many advances in the characterization and quantification of transcriptomes. RNA-seq is a powerful gene expression profiling technology enabling transcript discovery and provides a far more precise measure of the levels of transcripts and their isoforms than other methods e.g. microarray. However, the analysis of RNA-seq data remains a significant challenge for many biologists. The data generated is large and the tools for its assembly, analysis and visualisation are still under development. Assemblies of reads can be inspected using tools such as the Integrative Genomics Viewer (IGV) where visualisation of results involves ‘stacking’ the reads onto a reference genome. Whilst sufficient for many needs, when the underlying variance of the genome or transcript assemblies is complex, this visualisation method can be limiting; errors in assembly can be difficult to spot and visualisation of splicing events may be challenging. Data visualisation is increasingly recognised as an essential component of genomic and transcriptomic data analysis, enabling large and complex datasets to be better understood. An approach that has been gaining traction in biological research is based on the application of network visualisation and analysis methods. Networks consist of nodes connected by edges (lines), where nodes usually represent an entity and edge a relationship between them. These are now widely used for plotting experimentally or computationally derived relationships between genes and proteins. The overall aim of this PhD project was to explore the use of network-based visualisation in the analysis and interpretation of RNA-seq data. In chapter 2, I describe the development of a data pipeline that has been designed to go from ‘raw’ RNA-seq data to a file format which supports data visualisation as a ‘DNA assembly graph’. In DNA assembly graphs, nodes represent sequence reads and edges denote a homology between reads above a defined threshold. Following the mapping of reads to a reference sequence and defining which reads a map to a given loci, pairwise sequence alignments are performed between reads using MegaBLAST. This provides a weighted similarity score that is used to define edges between reads. Visualisation of the resulting networks is then carried out using BioLayout Express3D that can render large networks in 3-D, thereby allowing a better appreciation of the often-complex network structure. This pipeline has formed the basis for my subsequent work on the exploring and analysing alternative splicing in human RNA-seq data. In the second half of this chapter, I provide a series of tutorials aimed at different types of users allowing them to perform such analyses. The first tutorial is aimed at computational novices who might want to generate networks using a web-browser and pre-prepared data. Other tutorials are designed for use by more advanced users who can access the code for the pipeline through GitHub or via an Amazon Machine Image (AMI). In chapter 3, the utility of network-based visualisations of RNA-seq data is explored using data processed through the pipeline described in Chapter 2. The aim of the work described in this chapter was to better understand the basic principles and challenges associated with network visualisation of RNA-seq data, in particular how it could be used to visualise transcript structure and splice-variation. These analyses were performed on data generated from four samples of human fibroblasts taken at different time points during their entry into cell division. One of the first challenges encountered was the fact that the existing network layout algorithm (Fruchterman- Reingold) implemented within BioLayout Express3D did not result in an optimal layout of the unusual graph structures produced by these analyses. Following the implementation of the more advanced layout algorithm FMMM within the tool, network structure could be far better appreciated. Using this layout method, the majority of genes sequenced to an adequate depth assemble into networks with a linear ‘corkscrew’ appearance and when representing single isoform transcripts add little to existing views of these data. However, in a small number of cases (~5%), the networks generated from transcripts expressed in human fibroblasts possess more complex structures, with ‘loops’, ‘knots’ and multiple ends being observed. In a majority of cases examined, these loops were associated with alternative splicing events, a fact confirmed by RT-PCR analyses. Other DNA assembly networks representing the mRNAs for genes such as MKI67 showed knot-like structures, which was found to be due to the presence of repetitive sequence within an exon of the gene. In another case, CENPO the unusual structure observed was due to reads derived from an overlapping gene of ADCY3 gene present on the opposite strand with reads being wrongly mapped to CENPO. Finally, I explored the use of a network reduction strategy as an approach to visualising highly expressed genes such as GAPDH and TUBA1C. Having successfully demonstrated the utility of networks in analysing transcript isoforms in data derived from a single cell type I set out to explore its utility in analysing transcript variation in tissue data where multiple isoforms expressed by different cells within the tissue might be present in a given sample. In chapter 4, I explore the analysis of transcript variation in an RNA-seq dataset derived from human tissue. The first half of this chapter describes the quality control of these data again using a network-based approach but this time based the correlation in expression between genes and samples. Of the 95 samples derived from 27 human tissues, 77 passed the quality control. A network was constructed using a correlation threshold of r ≥ 0.9, which comprised 6,109 nodes (genes) and 1,091,477 edges (correlations) and clustered. Subsequently, the profile and gene content of each cluster was examined and enrichment of GO terms analysed. In the second half of this chapter, the aim was to detect and analyse alternative splicing events between different tissues using the rMATS tool. By using a false-discovery rate (FDR) cut-off of < 0.01, I found that in comparisons of brain vs. heart, brain vs. liver and heart vs. liver, the program reported 4,992, 4,804 and 3,990 splicing events, respectively. Of these events, only 78 splicing events (52 genes) with more than 50% of exon inclusion level and expression level more than FPKM 30. To further explore the sometimes-complex structure of transcripts diversity derived from tissue, RNAseq assembly networks for KLC1, SORBS2, GUK1, and TPM1 were explored. Each of these networks showed different types of alternative splicing events and it was sometimes difficult to determine the isoforms expressed between tissues using other approaches. For instance, there is an issue in visualising the read assembly of long genes such as KLC1 and SORBS2, using a Sashimi plots or even Vials, just because of the number of exons and the size of their genomic loci. In another case of GUK1, tissue-specific isoform expression was observed when a network of three tissues was combined. Arguably the most complex analysis is the network of TPM1 where the uniquification step was employed for this highly expressed gene. In chapter 5, I perform a usability testing for NGS Graph Generator web application and visualising RNA-seq assemblies as a network using BioLayout Express3D. This test was important to ensure that the application is well received and utilised by the user. / Almost all participants of this usability test agree that this application would encourage biologists to visualise and understand the alternative splicing together with existing tools. The participants agreed that Sashimi plots rather difficult to view and visualise and perhaps would lose something interesting features. However, there were also reviews of this application that need improvements such as the capability to analyse big network in a short time, side-by-side analysis of network with Sashimi plot and Ensembl. Additional information of the network would be necessary to improve the understanding of the alternative splicing. In conclusion, this work demonstrates the utility of network visualisation of RNAseq data, where the unusual structure of these networks can be used to identify issues in assembly, repetitive sequences within transcripts and splice variation. As such, this approach has the potential to significantly improve our understanding of transcript complexity. Overall, this thesis demonstrates that network-based visualisation provides a new and complementary approach to characterise alternative splicing from RNA-seq data and has the potential to be useful for the analysis and interpretation of other kinds of sequencing data.

xv, 106 p. : ill. (some col.) A print copy of this thesis is available through the UO Libraries. Search the library catalog for the location and call number. / Myotonic Dystrophy type I (DM1) is caused by muscleblind protein sequestration to aberrantly expanded CUG repeats. When muscleblind is sequestered it can no longer fulfill its role as an alternative splicing regulator, leading to mis-splicing events in both humans and Drosophila . The muscleblind protein family's RNA binding specificity has been minimally characterized. Only one pre-mRNA target in humans, cardiac troponin T (cTNT), has a known MBNL1 binding site. In order to understand muscleblind's RNA binding specificity and identify a consensus binding motif, systematic evolution of ligands by exponential enrichment (SELEX) was performed on both the Drosophila muscleblind protein, Mbl, and the human ortholog, MBNL1. Drosophila has provided a useful model for studying the disease mechanism of DM1. Studies of Mbl's RNA binding specificity to CUG repeats concluded that replacing the U-U mismatches with different pyrimidine-pyrimidine mismatches was tolerated, but no other mutations were. To understand Mbl's RNA binding specificity, SELEX was performed. After 6 rounds, several sequences were identified that bound with high affinity, all containing the 5'-AGUCU-3' consensus motif. One sequence, SELEX RNA 20 was analyzed further. In addition to the guanosine in the consensus motif of SELEX RNA 20, two other guanosines were shown to be protected by Mbl in a footprinting assay, indicating that Mbl has a strong preference for binding guanosine. Also, two "tail" regions of SELEX RNA 20 were shown to be single stranded and required for binding by Mbl. These results indicate that Mbl is a highly specific RNA binding protein with preference for both single and double stranded guanosine-rich regions. A doped SELEX was performed on MBNL1's binding site from the cTNT pre-mRNA to determine which sequences and structural aspects were important for recognition by MBNL1. Pool 5 RNA sequences bound with high affinity, and the motif 5'-YGCUU-3' was selected. This motif was then used to identify new MBNL1 binding sites in pre-mRNAs regulated by MBNL1, SERCA1 and MBNL1. The identification of this motif and two new MBNL1 sites provide insight into MBNL1-mediated alternative splicing. This dissertation includes both my previously published co-authored material and my unpublished co-authored material. / Adviser: J. Andrew Berglund

Made available in DSpace on 2015-11-04T13:39:10Z (GMT). No. of bitstreams: 2 tainah_paula_ioc_dout_2013.pdf: 4720016 bytes, checksum: abb2c1119571eec6ea090739b5bbc8a7 (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2015-10-29 / Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Rio de Janeiro, RJ, Brasil / A regulação gênica em tripanossomatídeos é policistrônica e ocorre de maneira pós-transcricional. Nas extremidades 5' e 3' dos RNA mensageiros existem segmentos que podem conter elementos regulatórios chamados UTR (Untranslated Regions), os quais são regiões transcritas, porém não-traduzidas. Para verificar se UTR de tamanhos diferentes possuem impacto na transcrição e, por conseguinte, na tradução de proteínas, selecionou-se genes da família de trans-sialidases, devido à importância destas no processo de invasão celular. A metodologia envolveu a extração de RNA total de T. cruzi CL-Brener, seguido da síntese de cDNA, PCR, clonagem dos produtos amplificados e seqüenciamento por Sanger e pelo uso do 454 Junior (Next Generation Sequencing \2013 NSG). Como as trans-sialidases correspondem a uma família gênica de cópias múltiplas, usou-se a estratégia de sequenciamento de alto-desempenho na tentativa de cobrir o maior número possível de genes desta família. Para isso foram obtidos cDNAs de trans-sialidases de CL-Brener nas formas epimastigota e tripomastigota com o iniciador 5\2019UTRTCNA, os quais apresentaram UTR com tamanhos variados entre 65 \2013 187 pb, além da obtenção de cDNAs para esta família de proteínas, em epimastigotas CL-Brener, com o iniciador 5\2019TcTS, apresentando UTR variando de 171 a 221 pb, com similaridade de sequências entre elas. Com o intuito de avaliar a correspondência entre a transcrição dos RNAs de trans-sialidases e a sua tradução, houve a necessidade de identificação das proteínas. Desenvolvemos uma metodologia de lise celular e produção de extrato proteico (denominado TcS12) a partir de células de T. cruzi no estágio epimastigota. As cepas selecionadas para esse estudo foram CL-Brener (TcVI), Dm28c (TcI), Y (TcII) e 4167 (TcIV) O processo de lise celular foi otimizado para 107 parasitos/mL ressuspensos em 200 \03BCL de tampão de lise hipotônica (por 30 minutos a 40C), associado ao sonicador de banho (por 30 minutos a 40C). A eficiência da metodologia foi validada pela citometria de fluxo mostrando que aproximadamente 72% das células foram marcadas com iodeto de propídeo (PI). A qualidade dos extratos proteicos foi analisada por LCMS/MS usando-se a estratégia MSE label free para quantificação relativa de proteínas. Foram identificadas 1153 proteínas totais, cuja expressão proteica das cepas 4167, Dm28c e Y, quando comparada à CL-Brener (cepa referência), apresentou 32, 51 e 73 proteínas up-expressed, enquanto que 80, 92 e 60 proteínas mostraram-se down\2013expressed, respectivamente. Entre as trans-sialidases identificadas, a única cópia encontrada no extrato TcS12 (número de acesso no UniProt - Q4DGV8) apresentou seu RNAm correspondente no banco de dados de cDNA de CL-Brener, com UTR de 214 pb, estando, portanto, na faixa de tamanhos das outras UTR de trans-sialidases obtidas neste estudo. Esse resultado sugere que outros fatores, não necessariamente o tamanho per se das UTR, podem influenciar no fenômeno de tradução nesses tripanossomatídeos. Em relação aos extratos proteicos de epimastigotas gerados a partir das outras cepas, a quantidade de trans-sialidases identificadas foram de 1 (cepa 4167), 2 (cepa Dm28c) e 117 (cepa Y) cópias, indicando assim a tradução satisfatória de pelo menos uma das muitas cópias desta família gênica / Gene regulation in trypanosomatids is polycistronic and occurs in a post - transcriptional way. There are also regulatory elements named UTRs (Untranslated Regions) that are transcribed regions, but not translated. To verify the impact of UTRs presenting different sizes in the transcription machinery and protein translation, genes f rom trans - sialidas e family were selected due to its importance in the cell invasion process. The methodological strategies involved the extraction of total RNA from T. cruzi CL - Brener strain , followed by cDNA synthesis, PCR, cloning and sequencing of amplified products by Sanger and by using the 454 Junior (Next Generation Sequencing – N G S ). Considering that trans - sialidase is a multi - copy gene family, this high - throughput sequencing strategy was employed in an attempt to cover the largest number of trans - sialidase genes. Trans - sialidase cDNAs from CL - Brener epimastigote and tripomastigote were obtained with 5`UTRTCNA primer show ing UT R sizes between 65 - 187 bp. The cDNA from this protein fam ily were also obtained with the 5’TcTS primer from CL - Brener epimastigote s , generating UTRs with 171 - 221 bp . Both 5’UTR presented sequence similarit ies between them. In order to evaluate the correspondence between trans - sialidase gene transcription and t ranslation, it was necessary to accomplish the identification of proteins. Therefore, we developed a methodology for cell disruption, which resulted in a protein extract ( referred as TcS12 ) from epimastigote T. cruzi cells. The strains selected for this st udy were CL - Brener (TcVI), Dm28c (TcI), Y (TcII) and 4167 (TcIV). The process for lysing the cells was optimized to 10 7 parasites/mL resuspended in 200 μL hypotonic lysis buffer (30 minutes at 4 o C) , followed by water bath sonication (30 minut e s a t 4 o C) . The process effic acy was confirmed by FACS , showing that near 72% of the cells were successfully stained with propidium iodide solution (PI). The quality of the protein extracts was analyzed by LCMS/MS using the strategy MS E label free for relative quant ification of proteins. A totality of 1153 proteins were identified and the comparison of the expression profiles between the strains 4167, Dm28c and Y , using the CL - Brener as reference, showed 32, 51 and 73 proteins up - expressed, and 80, 92 and 60 proteins were shown to be down - expressed, respectively. We observed that 117 trans - sialidase s were identified in Y strain, whilst in 4167, Dm28c and CL - Brener were found 1, 2 and 1 trans - sialidases, respectively. Moreover only one copy of trans - sialidase found in the TcS12 extract (UniProt accession number - Q4DGV8), also met its corresponding mRNA in the CL - Brener cDNA database, presenting an UTR of 214 bp, in the size range of the others trans - sialidase UTRs obtained herein. This result suggests th at other facto r s, but not exclusively the UTR sizes per se , could be related to the translation phenomenon in these trypanosomes

abstract: Splicing of digital images is a powerful form of tampering which transports regions of an image to create a composite image. When used as an artistic tool, this practice is harmless but when these composite images can be used to create political associations or are submitted as evidence in the judicial system they become more impactful. In these cases, distinction between an authentic image and a tampered image can become important. Many proposed approaches to image splicing detection follow the model of extracting features from an authentic and tampered dataset and then classifying them using machine learning with the goal of optimizing classification accuracy. This thesis approaches splicing detection from a slightly different perspective by choosing a modern splicing detection framework and examining a variety of preprocessing techniques along with their effect on classification accuracy. Preprocessing techniques explored include Joint Picture Experts Group (JPEG) file type block line blurring, image level blurring, and image level sharpening. Attention is also paid to preprocessing images adaptively based on the amount of higher frequency content they contain. This thesis also recognizes an identified problem with using a popular tampering evaluation dataset where a mismatch in the number of JPEG processing iterations between the authentic and tampered set creates an unfair statistical bias, leading to higher detection rates. Many modern approaches do not acknowledge this issue but this thesis applies a quality factor equalization technique to reduce this bias. Additionally, this thesis artificially inserts a mismatch in JPEG processing iterations by varying amounts to determine its effect on detection rates. / Dissertation/Thesis / Masters Thesis Computer Science 2015

Context: There has been a rise in the usage of digital images these days. Digital images are being used in many areas like in medicine, wars, etc. As the images are being used to make many important decisions, it is necessary to know if the images used are clean or forged. In this thesis, we have considered the area of splicing forgery. In this thesis, we are also considering and analyzing the impact of low-resolution images on the considered algorithms. Objectives. Through this thesis, we try to improve the detection rate of splicing forgery detection. We also examine how the examined splicing forgery detection algorithm works on low-resolution images and considered classification algorithms (classifiers). Methods: The research methods used in this research are Implementation and Experimentation. Implementation was used to answer the first research question i.e., to improve the detection rate in splicing forgery. Experimentation was used to answer the second research question. The results of the experiment were analyzed using statistical analysis to find out how the examined algorithm works on different image resolutions and on the considered classifiers. Results: One-tailed Wilcoxon signed rank test was conducted to compare which algorithm performs better, the T+ value obtained was less than To so the null hypothesis was rejected and the alternative hypothesis which states that Algorithm 2 (our enhanced version of the algorithm) performs better than Algorithm 1 (original algorithm), is accepted. Experiments were conducted and the accuracy of the algorithms in different cases were noted, ROC curves were plotted to obtain the AUC parameter. The accuracy, AUC parameters were used to determine the performance of the algorithms. Conclusions: After the results were analyzed using statistical analysis, we came to the conclusion that Algorithm 2 performs better than Algorithm 1 in detecting the forged images. It was also observed that Algorithm 1 improves its performance on low-resolution images when trained on original images and tested on images of different resolutions but, in the case of Algorithm 2, its performance is improved when trained and tested on images of the same resolution. There was not much variance in the performance of both of the algorithms on images of different resolution. Coming to the classifiers, Algorithm 1 improves its performance on linear SVM whereas Algorithm 2 improves its performance when using the simple tree classifier.