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An integrated network of Arabidopsis growth regulators and its use for gene prioritization.

Sabaghian E, Drebert Z, Inzé D, Saeys Y - Sci Rep (2015)

Bottom Line: Elucidating the molecular mechanisms that govern plant growth has been an important topic in plant research, and current advances in large-scale data generation call for computational tools that efficiently combine these different data sources to generate novel hypotheses.In this work, we present a novel, integrated network that combines multiple large-scale data sources to characterize growth regulatory genes in Arabidopsis, one of the main plant model organisms.In addition, the integrated network is made available to the scientific community, providing a rich data source that will be useful for many biological processes, not necessarily restricted to plant growth.

View Article: PubMed Central - PubMed

Affiliation: Department of Plant Systems Biology, VIB, 9052 Gent, Belgium.

ABSTRACT
Elucidating the molecular mechanisms that govern plant growth has been an important topic in plant research, and current advances in large-scale data generation call for computational tools that efficiently combine these different data sources to generate novel hypotheses. In this work, we present a novel, integrated network that combines multiple large-scale data sources to characterize growth regulatory genes in Arabidopsis, one of the main plant model organisms. The contributions of this work are twofold: first, we characterized a set of carefully selected growth regulators with respect to their connectivity patterns in the integrated network, and, subsequently, we explored to which extent these connectivity patterns can be used to suggest new growth regulators. Using a large-scale comparative study, we designed new supervised machine learning methods to prioritize growth regulators. Our results show that these methods significantly improve current state-of-the-art prioritization techniques, and are able to suggest meaningful new growth regulators. In addition, the integrated network is made available to the scientific community, providing a rich data source that will be useful for many biological processes, not necessarily restricted to plant growth.

No MeSH data available.


Related in: MedlinePlus

Local Network of GR genes.Graph structure of the GR genes based on the degree of interconnectivity, with nodes lower in the network having a higher degree. In the same layer, nodes are organized from left to right with increasing degree of interconnectivity. The color of the nodes shows the betweenness centrality (ability of nodes to keep the network connected).
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f1: Local Network of GR genes.Graph structure of the GR genes based on the degree of interconnectivity, with nodes lower in the network having a higher degree. In the same layer, nodes are organized from left to right with increasing degree of interconnectivity. The color of the nodes shows the betweenness centrality (ability of nodes to keep the network connected).

Mentions: Figure 1 shows a graphical representation of the interconnectivity in the local network connecting only the GR genes to each other. Genes are grouped into horizontal layers, with layers at the bottom having higher degrees of interconnectivity. Within each layer, genes situated toward the right have higher degrees of interconnectivity. Colored edges between genes show the different sub-network types, whereas node colors indicate the betweenness centrality of each gene, a measure of how important the gene is in connecting subparts of the network. In this local network, ANT was the most connected gene (138 edges), followed by ARF5 and MYC1 (both 117 edges). On the other hand, SAUR19 was not connected to any other GR gene, and JAW and ANAC081 had only one edge connecting them to other GR genes. In terms of centrality in the local network, AP2 was the most important gene, whereas JAW, ANAC081 and PPD1 were the least central genes in the network. A special case was ANAC021, which – despite its low degree of connectivity (10 edges) – still had a large effect on the network, as shown by its relatively high betweenness value. The underlying reason for this is the fact that ANAC021 plays a key role in connecting nodes mainly connected by the PCC sub-network to nodes mainly connected by the GeneMANIA sub-network. Also when looking at the edge and node betweenness values for the local network of GR genes (Supplementary Fig. S2), the link between ANAC021 and AP2 was of high importance, as well as the few links that connected low-degree nodes to the rest of the network (e.g. JAW, PPD1).


An integrated network of Arabidopsis growth regulators and its use for gene prioritization.

Sabaghian E, Drebert Z, Inzé D, Saeys Y - Sci Rep (2015)

Local Network of GR genes.Graph structure of the GR genes based on the degree of interconnectivity, with nodes lower in the network having a higher degree. In the same layer, nodes are organized from left to right with increasing degree of interconnectivity. The color of the nodes shows the betweenness centrality (ability of nodes to keep the network connected).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4664945&req=5

f1: Local Network of GR genes.Graph structure of the GR genes based on the degree of interconnectivity, with nodes lower in the network having a higher degree. In the same layer, nodes are organized from left to right with increasing degree of interconnectivity. The color of the nodes shows the betweenness centrality (ability of nodes to keep the network connected).
Mentions: Figure 1 shows a graphical representation of the interconnectivity in the local network connecting only the GR genes to each other. Genes are grouped into horizontal layers, with layers at the bottom having higher degrees of interconnectivity. Within each layer, genes situated toward the right have higher degrees of interconnectivity. Colored edges between genes show the different sub-network types, whereas node colors indicate the betweenness centrality of each gene, a measure of how important the gene is in connecting subparts of the network. In this local network, ANT was the most connected gene (138 edges), followed by ARF5 and MYC1 (both 117 edges). On the other hand, SAUR19 was not connected to any other GR gene, and JAW and ANAC081 had only one edge connecting them to other GR genes. In terms of centrality in the local network, AP2 was the most important gene, whereas JAW, ANAC081 and PPD1 were the least central genes in the network. A special case was ANAC021, which – despite its low degree of connectivity (10 edges) – still had a large effect on the network, as shown by its relatively high betweenness value. The underlying reason for this is the fact that ANAC021 plays a key role in connecting nodes mainly connected by the PCC sub-network to nodes mainly connected by the GeneMANIA sub-network. Also when looking at the edge and node betweenness values for the local network of GR genes (Supplementary Fig. S2), the link between ANAC021 and AP2 was of high importance, as well as the few links that connected low-degree nodes to the rest of the network (e.g. JAW, PPD1).

Bottom Line: Elucidating the molecular mechanisms that govern plant growth has been an important topic in plant research, and current advances in large-scale data generation call for computational tools that efficiently combine these different data sources to generate novel hypotheses.In this work, we present a novel, integrated network that combines multiple large-scale data sources to characterize growth regulatory genes in Arabidopsis, one of the main plant model organisms.In addition, the integrated network is made available to the scientific community, providing a rich data source that will be useful for many biological processes, not necessarily restricted to plant growth.

View Article: PubMed Central - PubMed

Affiliation: Department of Plant Systems Biology, VIB, 9052 Gent, Belgium.

ABSTRACT
Elucidating the molecular mechanisms that govern plant growth has been an important topic in plant research, and current advances in large-scale data generation call for computational tools that efficiently combine these different data sources to generate novel hypotheses. In this work, we present a novel, integrated network that combines multiple large-scale data sources to characterize growth regulatory genes in Arabidopsis, one of the main plant model organisms. The contributions of this work are twofold: first, we characterized a set of carefully selected growth regulators with respect to their connectivity patterns in the integrated network, and, subsequently, we explored to which extent these connectivity patterns can be used to suggest new growth regulators. Using a large-scale comparative study, we designed new supervised machine learning methods to prioritize growth regulators. Our results show that these methods significantly improve current state-of-the-art prioritization techniques, and are able to suggest meaningful new growth regulators. In addition, the integrated network is made available to the scientific community, providing a rich data source that will be useful for many biological processes, not necessarily restricted to plant growth.

No MeSH data available.


Related in: MedlinePlus