Estimating rates of alternative splicing in mammals and invertebrates
The Rockefeller University, New York, New York, United States Nature Genetics
(Impact Factor: 29.35).
10/2004; 36(9):915-6; author reply 916-7. DOI: 10.1038/ng0904-915
Available from: Stephen J Bush
- "However, assessing the expansion of ASP through evolutionary time and establishing a link between alternative splicing and organism complexity have proved difficult (Nilsen and Graveley 2010). The main barrier to comparative studies of ASP arises from the fact that differences in transcript sequence coverage across species can distort both the proportion of genes classified as undergoing alternative splicing and the number of alternative splicing events detected (Brett et al. 2002; Kim et al. 2004; Kim et al. 2007; Takeda et al. 2008; Mollet et al. 2010; Nilsen and Graveley 2010; Schad et al. 2011). Kim et al. (2007) devised a method of transcript number normalization to obtain comparable ASP indices involving the identification of alternative splicing events from a random sample of 10 transcripts per gene. "
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ABSTRACT: What at the genomic level underlies organism complexity? Although several genomic features have been associated with organism complexity, in the case of alternative splicing, which has long been proposed to explain the variation in complexity, no such link has been established. Here, we analyzed over 39 million expressed sequence tags available for 47 eukaryotic species with fully sequenced genomes to obtain a comparable index of alternative splicing estimates, which corrects for the distorting effect of a variable number of transcripts per species-an important obstacle for comparative studies of alternative splicing. We find that alternative splicing has steadily increased over the last 1,400 My of eukaryotic evolution and is strongly associated with organism complexity, assayed as the number of cell types. Importantly, this association is not explained as a by-product of covariance between alternative splicing with other variables previously linked to complexity including gene content, protein length, proteome disorder, and protein interactivity. In addition, we found no evidence to suggest that the relationship of alternative splicing to cell type number is explained by drift due to reduced Ne in more complex species. Taken together, our results firmly establish alternative splicing as a significant predictor of organism complexity and are, in principle, consistent with an important role of transcript diversification through alternative splicing as a means of determining a genome's functional information capacity.
Available from: Casandra Riera
- "Therefore, simple switching from IM to noIM between orthologs may imply an important change, even before taking into consideration any possible difference between isoforms. At the genome level, this suggests that differences between organisms in the fraction of genes with IM may contribute to explain their PheDif , , , . While initial tests of this hypothesis led to controversial results , , it was subsequently shown that IM could indeed play the proposed role . "
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ABSTRACT: At present we know that phenotypic differences between organisms arise from a variety of sources, like protein sequence divergence, regulatory sequence divergence, alternative splicing, etc. However, we do not have yet a complete view of how these sources are related. Here we address this problem, studying the relationship between protein divergence and the ability of genes to express multiple isoforms. We used three genome-wide datasets of human-mouse orthologs to study the relationship between isoform multiplicity co-occurrence between orthologs (the fact that two orthologs have more than one isoform) and protein divergence. In all cases our results showed that there was a monotonic dependence between these two properties. We could explain this relationship in terms of a more fundamental one, between exon number of the largest isoform and protein divergence. We found that this last relationship was present, although with variations, in other species (chimpanzee, cow, rat, chicken, zebrafish and fruit fly). In summary, we have identified a relationship between protein divergence and isoform multiplicity co-occurrence and explained its origin in terms of a simple gene-level property. Finally, we discuss the biological implications of these findings for our understanding of inter-species phenotypic differences.
Available from: Christina A Harrington
- "Alternative splicing is viewed as one of the major sources of transcript diversity with estimated rates of alternative splicing ranging between 92 and 94% for all human genes (Modrek and Lee, 2002; Wang et al., 2008) and 74% for multi-exon human genes (Johnson et al., 2003). Kim et al. (2004) calculated that there are on average 3.5 alternative transcripts per human gene and approximately 2.5 alternative transcripts per mouse gene. Additionally, it has been shown that alternate exon usage (AEU) events are especially high in the brain (Ramsköld et al., 2009). "
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ABSTRACT: Background: With the advent of the GeneChip Exon Arrays, it is now possible to extract "exon-level" expression estimates, allowing for detection of alternative splicing events, one of the primary mechanisms of transcript diversity. In the context of (1) a complex trait use case and (2) a human cerebellum vs. heart comparison on previously validated data, we present a transcript-based statistical model and validation framework to allow detection of alternative exon usage (AEU) between different groups. To illustrate the approach, we detect and confirm differences in exon usage in the two of the most widely studied mouse genetic models (the C57BL/6J and DBA/2J inbred strains) and in a human dataset. Results: We developed a computational framework that consists of probe level annotation mapping and statistical modeling to detect putative AEU events, as well as visualization and alignment with known splice events. We show a dramatic improvement (∼25 fold) in the ability to detect these events using the appropriate annotation and statistical model which is actually specified at the transcript level, as compared with the transcript cluster/gene-level annotation used on the array. An additional component of this workflow is a probe index that allows ranking AEU candidates for validation and can aid in identification of false positives due to single nucleotide polymorphisms. Discussion: Our work highlights the importance of concordance between the functional unit interrogated (e.g., gene, transcripts) and the entity (e.g., exon, probeset) within the statistical model. The framework we present is broadly applicable to other platforms (including RNAseq).
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