Biomedical subjects

Ofer Shai

Publications and source records attributed to Ofer Shai.

4 recordsLinked to original sources

PubMed2006

Quantitative microarray profiling provides evidence against widespread coupling of alternative splicing with nonsense-mediated mRNA decay to control gene expression.

Sequence-based analyses have predicted that approximately 35% of mammalian alternative splicing (AS) events produce premature termination codon (PTC)-containing splice variants that are targeted by the process of nonsense-mediated mRNA decay (NMD). This led to speculation that AS may often regulate gene expression by activating NMD. Using AS microarrays, we show that PTC-containing splice variants are generally produced at uniformly low levels across diverse mammalian cells and tissues, independently of the action of NMD. Our results suggest that most PTC-introducing AS events are not under positive selection pressure and therefore may not contribute important functional roles.

Alternative Splicing↗

PubMed2006

Inferring global levels of alternative splicing isoforms using a generative model of microarray data.

MOTIVATION: Alternative splicing (AS) is a frequent step in metozoan gene expression whereby the exons of genes are spliced in different combinations to generate multiple isoforms of mature mRNA. AS functions to enrich an organism's proteomic complexity and regulates gene expression. Despite its importance, the mechanisms underlying AS and its regulation are not well understood, especially in the context of global gene expression patterns. We present here an algorithm referred to as the Generative model for the Alternative Splicing Array Platform (GenASAP) that can predict the levels of AS for thousands of exon skipping events using data generated from custom microarrays. GenASAP uses Bayesian learning in an unsupervised probability model to accurately predict AS levels from the microarray data. GenASAP is capable of learning the hybridization profiles of microarray data, while modeling noise processes and missing or aberrant data. GenASAP has been successfully applied to the global discovery and analysis of AS in mammalian cells and tissues. RESULTS: GenASAP was applied to data obtained from a custom microarray designed for the monitoring of 3126 AS events in mouse cells and tissues. The microarray design included probes specific for exon body and junction sequences formed by the splicing of exons. Our results show that GenASAP provides accurate predictions for over one-third of the total events, as verified by independent RT-PCR assays. SUPPLEMENTARY INFORMATION: http://www.psi.toronto.edu/GenASAP.

Algorithms↗

PubMed2004

Revealing global regulatory features of mammalian alternative splicing using a quantitative microarray platform.

We describe the application of a microarray platform, which combines information from exon body and splice-junction probes, to perform a quantitative analysis of tissue-specific alternative splicing (AS) for thousands of exons in mammalian cells. Through this system, we have analyzed global features of AS in major mouse tissues. The results provide numerous inferences for the functions of tissue-specific AS, insights into how the evolutionary history of exons can impact on their inclusion levels, and also information on how global regulatory properties of AS define tissue type. Like global transcription profiles, global AS profiles reflect tissue identity. Interestingly, we find that transcription and AS act independently on different sets of genes in order to define tissue-specific expression profiles. These results demonstrate the utility of our quantitative microarray platform and data for revealing important global regulatory features of AS.

Alternative Splicing↗

PubMed2004

The functional landscape of mouse gene expression.

BACKGROUND: Large-scale quantitative analysis of transcriptional co-expression has been used to dissect regulatory networks and to predict the functions of new genes discovered by genome sequencing in model organisms such as yeast. Although the idea that tissue-specific expression is indicative of gene function in mammals is widely accepted, it has not been objectively tested nor compared with the related but distinct strategy of correlating gene co-expression as a means to predict gene function. RESULTS: We generated microarray expression data for nearly 40,000 known and predicted mRNAs in 55 mouse tissues, using custom-built oligonucleotide arrays. We show that quantitative transcriptional co-expression is a powerful predictor of gene function. Hundreds of functional categories, as defined by Gene Ontology 'Biological Processes', are associated with characteristic expression patterns across all tissues, including categories that bear no overt relationship to the tissue of origin. In contrast, simple tissue-specific restriction of expression is a poor predictor of which genes are in which functional categories. As an example, the highly conserved mouse gene PWP1 is widely expressed across different tissues but is co-expressed with many RNA-processing genes; we show that the uncharacterized yeast homolog of PWP1 is required for rRNA biogenesis. CONCLUSIONS: We conclude that 'functional genomics' strategies based on quantitative transcriptional co-expression will be as fruitful in mammals as they have been in simpler organisms, and that transcriptional control of mammalian physiology is more modular than is generally appreciated. Our data and analyses provide a public resource for mammalian functional genomics.

Animals↗