Browsing by Author "Steffen Heber, Committee Chair"

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Analysis of Cis-acting Regulatory Motifs Involved in Alternative Splicing
(2009-04-15) Zhao, Sihui; Steffen Heber, Committee Chair; Zhao-Bang Zeng, Committee Co-Chair; David Bird, Committee Member; Hao Zhang, Committee Member
Alternative splicing is an important posttranscriptional process in eukaryotes. It dramatically expands the proteome and contributes essentially to the regulation of gene expression. Cis-acting regulatory motifs play a pivotal role in the regulation of alternative splicing. Many human diseases involved with aberrant (alternative) splicing are caused by mutations of splicing regulatory motifs. However, due to the short, degenerate and context-dependent nature, the prediction of cis-acting splicing motifs is a very challenging task. In this dissertation, we focus on discovery of splicing signals from sequences. This may help to reveal the integrated splicing code and to understand the regulation of gene expression in the resolution of exon level. In chapter one, we review the up-to-date research development in alternative splicing and its regulation, as well as the experimental and computational approaches in genome-wide alternative splicing analysis. We describe a large-scale data analysis experiment to discover AS motifs in chapter two. We applied a computational framework to re-analyze a dataset containing about 3,000 cassette exons and skipping rates for regulatory motifs. The alternative spliced events were clustered by their expression profiles to find co-regulated genes. Rather than using a fixed cutoff as cluster boundary, we used systematic sampling to sample sequence clusters and eliminated redundant motifs predicted from overlapping clusters. We conclude that these predicted motifs may be promising candidates responsible for AS regulation by comparison to known motifs and by positional bias. In chapter three, we describe a new approach to discover short and degenerate AS motifs. We implemented a two-step approach incorporating skipping rates in motif discovery. In the simulation study, we show that this approach is especially suitable to discover short and highly degenerate motifs. Analysis of cassette exons in Central Nervous System tissues produced 15 motifs which are associated with the variation of skipping rates. We discover that Nova and hnRNP A1 binding sites are involved with AS regulation, as well as about ten novel motifs. Moreover, co-operation between predicted motifs are also revealed. In chapter four, we give the present status of SPRED, a database of cis-acting regulatory splicing elements. The motifs in SPRED are compiled from literature. They are all experimentally validated. The web interface is publically accessible and accompanied with query and similarity search tools. The goal of SPRED is to provide a comprehensive motif dictionary to facilitate the research in AS and its regulation. Finally, we give the conclusions in chapter five. We also give the perspective for future study and briefly review the potential challenge.
Automating the annotation and discovery of MicroRNA in multi-species high-throughput 454 Sequencing
(2008-08-04) Wheeler, Benjamin Matthew; Steffen Heber, Committee Chair; Brian Wiegmnn, Committee Member; Nagiza Samatova, Committee Member
Methods for Accurate Analysis of High-Throughput Transcriptome Data
(2009-11-30) Howard, Brian E; Steffen Heber, Committee Chair; David Bird, Committee Member; Dahlia Nielsen, Committee Member; Heike Winter-Sederoff, Committee Member; Hao Helen Zhang, Committee Member
A detailed understanding of the transcriptome is a prerequisite for deciphering the flow of information from genotype to phenotype. Fortunately, modern high-throughput technologies now provide an unprecedented ability to observe the full complement of transcriptional events, which extend far beyond the classical "one gene, one protein" hypothesis to include alternatively spliced genes, microRNAs, RNA interference, anti-sense transcription, and a variety of other, until recently, unknown phenomena. However, in order to accurately interpret the results of these assays, new statistical and bioinformatic methods must be developed in parallel to biotechnological advances. In this thesis, we present several methods for improving the accuracy of inferences obtained from the high-throughput transcriptome data generated by these new technologies. First, we present a novel method for microarray quality assessment. Since accurate inference is dependent on the quality of the underlying data, quality assessment is a critical component in any microarray data analysis. Our method, which uses an unsupervised classifier to discriminate between high and low quality microarray datasets, exhibits performance comparable to supervised learners constructed using the same training data. However, because our approach requires only unnannotated data, it is easy to customize and to keep up-to-date as technology evolves. Next, we present an alternative method for microarray quality assessment, which identifies low quality microarrays by simulating a set of differentially expressed genes. This method directly measures the ability of a planned statistical analysis to identify differential gene expression when suspected low quality arrays are included in the dataset. A key advantage of this approach is that, unlike other methods, this method provides a specific recommendation about whether to retain or discard low quality chips in the context of a particular experimental setting. Finally, we introduce a procedure for accurately quantifying alternative splicing using RNA-Seq data. Our method uses a familiar linear models approach, but improves upon similar methods that assume uniform coverage of RNA-Seq reads along the targeted transcripts. We first show, through simulation, that using an incorrect read sampling distribution can lead to incorrect conclusions about the expression of isoforms in a mixture. Applying our method to an example dataset, we identify 438 differentially spliced genes, exhibiting a range of expression patterns including genes with switch-like differential splicing between two tissues, as well as genes with more subtle variations in isoform expression. Taken together, we expect that these methods can serve to increase the accuracy of inferences drawn from high-throughput transcriptome data, and in doing so, lead to an advancement of our understanding of the biology of genome expression.