Alignment of time course gene expression data and the classification of developmentally driven genes with hidden Markov models.

ABSTRACT:

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

SUBMITTER: Robinson S 

PROVIDER: S-EPMC4472167 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature