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The steroid receptor RNA activator gene (SRA1) generates two distinct entities. SRA RNA coactivates several NRs whereas SRA protein (SRAP) is suspected to regulate the activity of several transcription factors, including estrogen receptors (ER). Splicing of SRA intron-1 is the major event defining SRAP coding frame. Fully spliced, coding SRA and intron-1 retained, non-coding SRA coexist in breast cancer cells. The relative proportion between the two types of SRA RNA maintains a balance between two genetically linked entities, SRA and SRAP. In this study, a minigene model was used to demonstrate that the primary sequence of SRA exon-1-intron-1-exon-2 is sufficient for alternative splicing of SRA intron-1. In addition, a modified oligoribonucleotidic construct promotes SRA intron-1 retention in breast cancer cells. This oligoribonucleotide differentially alters estradiol-induced transcription of ER regulated genes. Together, results presented herein demonstrate that the SRA-SRAP balance, which can be artificially modified by targeting alternative splicing of SRA intron-1, might be a new critical target to treat breast cancer patients.

Alternative splicing, the process by which a single gene may code for similar but different proteins, is an important process in biology, linked to development, cellular differentiation, genetic diseases, and more. Genome-wide analysis of alternative splicing patterns and regulation has been recently made possible due to new high throughput techniques for monitoring gene expression and genomic sequencing. This thesis introduces two algorithms for alternative splicing analysis based on large microarray and genomic sequence data. The algorithms, based on generative probabilistic models that capture structure and patterns in the data, are used to study global properties of alternative splicing. In the first part of the thesis, a microarray platform for monitoring alternative splicing is introduced. A spatial noise removal algorithm that removes artifacts and improves data fidelity is presented. The GenASAP algorithm (generative model for alternative splicing array platform) models the non-linear process in which targeted molecules bind to a microarray’s probes and is used to predict patterns of alternative splicing. Two versions of GenASAP have been developed. The first uses variational approximation to infer the relative amounts of the targeted molecules, while the second incorporates a more accurate noise and generative model and utilizes Markov chain Monte Carlo (MCMC) sampling. GenASAP, the first method to provide quantitative predictions of alternative splicing patterns on large scale data sets, is shown to generate useful and precise predictions based on independent RT-PCR validation (a slow but more accurate approach to measuring cellular expression patterns). In the second part of the thesis, the results obtained by GenASAP are analysed to reveal jointly regulated genes. The sequences of the genes are examined for potential regulatory factors binding sites using a new motif finding algorithm designed for this purpose. The motif finding algorithm, called GenBITES (generative model for binding sites) uses a fully Bayesian generative model for sequences, and the MCMC approach used for inference in the model includes moves that can efficiently create or delete motifs, and extend or contract the width of existing motifs. GenBITES has been applied to several synthetic and real data sets, and is shown to be highly competitive at a task for which many algorithms already exist. Although developed to analyze alternative splicing data, GenBITES outperforms most reported results on a benchmark data set based on transcription data.

Diss. (sammanfattning) Stockholm : Karol. inst., 2005. / Härtill 4 uppsatser.

Thesis (Ph. D.)--University of Washington, 1998. / Vita. Includes bibliographical references (leaves [47]-52).

Thesis (Ph.D.)--University of Alabama at Birmingham, 2008. / Title from PDF title page (viewed on July 14, 2010). Includes bibliographical references

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

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.