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2015 ; 16
(ä): 196
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Alignment of time course gene expression data and the classification of
developmentally driven genes with hidden Markov models
#MMPMID26084333
Robinson S
; Glonek G
; Koch I
; Thomas M
; Davies C
BMC Bioinformatics
2015[Jun]; 16
(ä): 196
PMID26084333
show ga
BACKGROUND: We consider data from a time course microarray experiment that was
conducted on grapevines over the development cycle of the grape berries at two
different vineyards in South Australia. Although the underlying biological
process of berry development is the same at both vineyards, there are differences
in the timing of the development due to local conditions. We aim to align the
data from the two vineyards to enable an integrated analysis of the gene
expression and use the alignment of the expression profiles to classify likely
developmental function. RESULTS: We present a novel alignment method based on
hidden Markov models (HMMs) and use the method to align the motivating grapevine
data. We show that our alignment method is robust against subsets of profiles
that are not suitable for alignment, investigate alignment diagnostics under the
model and demonstrate the classification of developmentally driven genes.
CONCLUSIONS: The classification of developmentally driven genes both validates
that the alignment we obtain is meaningful and also gives new evidence that can
be used to identify the role of genes with unknown function. Using our alignment
methodology, we find at least 1279 grapevine probe sets with no current annotated
function that are likely to be controlled in a developmental manner.