Prediction of alternative isoforms from exon expression levels in RNA-Seq experiments. Richard, H., Schulz, M. H., Sultan, M., Nürnberger, A., Schrinner, S., Balzereit, D., Dagand, E., Rasche, A., Lehrach, H., Vingron, M., Haas, S. A., & Yaspo, M. Nucleic Acids Research, 38(10):e112, June, 2010. doi abstract bibtex Alternative splicing, polyadenylation of pre-messenger RNA molecules and differential promoter usage can produce a variety of transcript isoforms whose respective expression levels are regulated in time and space, thus contributing specific biological functions. However, the repertoire of mammalian alternative transcripts and their regulation are still poorly understood. Second-generation sequencing is now opening unprecedented routes to address the analysis of entire transcriptomes. Here, we developed methods that allow the prediction and quantification of alternative isoforms derived solely from exon expression levels in RNA-Seq data. These are based on an explicit statistical model and enable the prediction of alternative isoforms within or between conditions using any known gene annotation, as well as the relative quantification of known transcript structures. Applying these methods to a human RNA-Seq dataset, we validated a significant fraction of the predictions by RT-PCR. Data further showed that these predictions correlated well with information originating from junction reads. A direct comparison with exon arrays indicated improved performances of RNA-Seq over microarrays in the prediction of skipped exons. Altogether, the set of methods presented here comprehensively addresses multiple aspects of alternative isoform analysis. The software is available as an open-source R-package called Solas at http://cmb.molgen.mpg.de/2ndGenerationSequencing/Solas/.
@article{richard_prediction_2010,
title = {Prediction of alternative isoforms from exon expression levels in {RNA}-{Seq} experiments},
volume = {38},
issn = {1362-4962},
doi = {10.1093/nar/gkq041},
abstract = {Alternative splicing, polyadenylation of pre-messenger RNA molecules and differential promoter usage can produce a variety of transcript isoforms whose respective expression levels are regulated in time and space, thus contributing specific biological functions. However, the repertoire of mammalian alternative transcripts and their regulation are still poorly understood. Second-generation sequencing is now opening unprecedented routes to address the analysis of entire transcriptomes. Here, we developed methods that allow the prediction and quantification of alternative isoforms derived solely from exon expression levels in RNA-Seq data. These are based on an explicit statistical model and enable the prediction of alternative isoforms within or between conditions using any known gene annotation, as well as the relative quantification of known transcript structures. Applying these methods to a human RNA-Seq dataset, we validated a significant fraction of the predictions by RT-PCR. Data further showed that these predictions correlated well with information originating from junction reads. A direct comparison with exon arrays indicated improved performances of RNA-Seq over microarrays in the prediction of skipped exons. Altogether, the set of methods presented here comprehensively addresses multiple aspects of alternative isoform analysis. The software is available as an open-source R-package called Solas at http://cmb.molgen.mpg.de/2ndGenerationSequencing/Solas/.},
language = {eng},
number = {10},
journal = {Nucleic Acids Research},
author = {Richard, Hugues and Schulz, Marcel H. and Sultan, Marc and Nürnberger, Asja and Schrinner, Sabine and Balzereit, Daniela and Dagand, Emilie and Rasche, Axel and Lehrach, Hans and Vingron, Martin and Haas, Stefan A. and Yaspo, Marie-Laure},
month = jun,
year = {2010},
pmid = {20150413},
pmcid = {PMC2879520},
keywords = {Alternative Splicing, Cell Line, Computer Simulation, Exons, Expressed Sequence Tags, Gene Expression Profiling, Humans, Models, Statistical, Oligonucleotide Array Sequence Analysis, Protein Isoforms, Sequence Analysis, RNA},
pages = {e112},
file = {Volltext:/Users/mschulz/Zotero/storage/DEWA3GMB/Richard et al. - 2010 - Prediction of alternative isoforms from exon expre.pdf:application/pdf},
}
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Second-generation sequencing is now opening unprecedented routes to address the analysis of entire transcriptomes. Here, we developed methods that allow the prediction and quantification of alternative isoforms derived solely from exon expression levels in RNA-Seq data. These are based on an explicit statistical model and enable the prediction of alternative isoforms within or between conditions using any known gene annotation, as well as the relative quantification of known transcript structures. Applying these methods to a human RNA-Seq dataset, we validated a significant fraction of the predictions by RT-PCR. Data further showed that these predictions correlated well with information originating from junction reads. A direct comparison with exon arrays indicated improved performances of RNA-Seq over microarrays in the prediction of skipped exons. Altogether, the set of methods presented here comprehensively addresses multiple aspects of alternative isoform analysis. 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