Computational detection of alternative exon usage

Ted G. Laderas, Nicole A R Walter, Michael Mooney, Kristina Vartanian, Priscila Darakjian, Kari Buck, Christina (Chris) Harrington, John Belknap, Robert Hitzemann, Shannon McWeeney

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Background: With the advent of the GeneChip Exon Arrays, it is now possible to extract "exonlevel" 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 transcriptbased 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).

Original languageEnglish (US)
Article numberArticle 69
JournalFrontiers in Neuroscience
Issue numberMAY
DOIs
StatePublished - 2011

Fingerprint

Exons
Statistical Models
Molecular Sequence Annotation
Workflow
Genetic Models
Alternative Splicing
Cerebellum
Single Nucleotide Polymorphism
Genes

Keywords

  • Alternative splicing
  • Exon array

ASJC Scopus subject areas

  • Neuroscience(all)

Cite this

Computational detection of alternative exon usage. / Laderas, Ted G.; Walter, Nicole A R; Mooney, Michael; Vartanian, Kristina; Darakjian, Priscila; Buck, Kari; Harrington, Christina (Chris); Belknap, John; Hitzemann, Robert; McWeeney, Shannon.

In: Frontiers in Neuroscience, No. MAY, Article 69, 2011.

Research output: Contribution to journalArticle

Laderas, Ted G. ; Walter, Nicole A R ; Mooney, Michael ; Vartanian, Kristina ; Darakjian, Priscila ; Buck, Kari ; Harrington, Christina (Chris) ; Belknap, John ; Hitzemann, Robert ; McWeeney, Shannon. / Computational detection of alternative exon usage. In: Frontiers in Neuroscience. 2011 ; No. MAY.
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