ANOSVA: a statistical method for detecting splice variation from expression data. Many or most mammalian genes undergo alternative splicing, generating a variety of transcripts from a single gene. New information on splice variation is becoming available through technology for measuring expression levels of several exons or splice junctions per gene. We have developed a statistical method, ANalysis Of Splice VAriation (ANOSVA) to detect alternative splicing from expression data. Since ANOSVA requires no transcript information, it can be applied when the level of annotation is poor. When validated against spiked clone data, it generated no false positives and few false negatives. We demonstrated ANOSVA with data from a prototype mouse alternative splicing array, run against normal adult tissues, yielding a set of genes with evidence of tissue-specific splice variation.
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References in zbMATH (referenced in 2 articles )
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- Jung, Yoonsuh; Hu, Jianhua: Review: Reversed low-rank ANOVA model for transforming high dimensional genetic data into low dimension (2019)
- Gelfond, Jonathan; Zarzabal, Lee Ann; Burton, Tarea; Burns, Suzanne; Sogayar, Mari; Penalva, Luiz O. F.: Latent rank change detection for analysis of splice-junction microarrays with nonlinear effects (2011)