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

Robinson S, Glonek G, Koch I, Thomas M, Davies C - BMC Bioinformatics (2015)

Bottom Line: We present a novel alignment method based on hidden Markov models (HMMs) and use the method to align the motivating grapevine data.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.

View Article: PubMed Central - PubMed

Affiliation: School of Mathematical Sciences, University of Adelaide, Adelaide, Australia. sean.robinson@alumni.adelaide.edu.au.

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 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.

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Expression profiles for four example genes from the Willunga (blue) and Clare (orange) vineyards
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Fig1: Expression profiles for four example genes from the Willunga (blue) and Clare (orange) vineyards

Mentions: We consider a time course microarray experiment conducted on grapevines (Vitis vinifera L., Cabernet Sauvignon) at the ‘Willunga’ and ‘Clare’ vineyards in South Australia. The experiment was run over the duration of the development cycle of the grape berries, from the closed-flower to ripe-red stage of the berries themselves. For each gene, we have a pair of expression profiles, one from each of the Willunga and Clare vineyards. Pairs of expression profiles for four example genes can be seen in Fig. 1. For each pair of profiles, we aim to obtain a single profile that captures the relevant gene expression information over the development cycle of the grape berries from both vineyards. The common representations can then be used for an overall analysis of the gene expression.Fig. 1


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

Robinson S, Glonek G, Koch I, Thomas M, Davies C - BMC Bioinformatics (2015)

Expression profiles for four example genes from the Willunga (blue) and Clare (orange) vineyards
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4472167&req=5

Fig1: Expression profiles for four example genes from the Willunga (blue) and Clare (orange) vineyards
Mentions: We consider a time course microarray experiment conducted on grapevines (Vitis vinifera L., Cabernet Sauvignon) at the ‘Willunga’ and ‘Clare’ vineyards in South Australia. The experiment was run over the duration of the development cycle of the grape berries, from the closed-flower to ripe-red stage of the berries themselves. For each gene, we have a pair of expression profiles, one from each of the Willunga and Clare vineyards. Pairs of expression profiles for four example genes can be seen in Fig. 1. For each pair of profiles, we aim to obtain a single profile that captures the relevant gene expression information over the development cycle of the grape berries from both vineyards. The common representations can then be used for an overall analysis of the gene expression.Fig. 1

Bottom Line: We present a novel alignment method based on hidden Markov models (HMMs) and use the method to align the motivating grapevine data.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.

View Article: PubMed Central - PubMed

Affiliation: School of Mathematical Sciences, University of Adelaide, Adelaide, Australia. sean.robinson@alumni.adelaide.edu.au.

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 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.

Show MeSH