TY - JOUR
T1 - Computational detection of alternative exon usage
AU - Laderas, Ted G.
AU - Walter, Nicole A.R.
AU - Mooney, Michael
AU - Vartanian, Kristina
AU - Darakjian, Priscila
AU - Buck, Kari
AU - Harrington, Christina A.
AU - Belknap, John
AU - Hitzemann, Robert
AU - McWeeney, Shannon K.
PY - 2011
Y1 - 2011
N2 - 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).
AB - 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).
KW - Alternative splicing
KW - Exon array
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U2 - 10.3389/fnins.2011.00069
DO - 10.3389/fnins.2011.00069
M3 - Article
C2 - 21625610
AN - SCOPUS:84862179297
SN - 1662-4548
JO - Frontiers in Neuroscience
JF - Frontiers in Neuroscience
IS - MAY
M1 - Article 69
ER -