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Predicted protein-protein interactions in the moss Physcomitrella patens: a new bioinformatic resource.

Schuette S, Piatkowski B, Corley A, Lang D, Geisler M - BMC Bioinformatics (2015)

Bottom Line: This method has been used to successfully predict interactions for a number of angiosperm plants.Most conserved interactions among proteins were those associated with metabolic processes.Included with this dataset is a method for characterizing subnetworks and investigating specific processes, such as the Calvin-Benson-Bassham cycle.

View Article: PubMed Central - PubMed

Affiliation: Department of Plant Biology, Southern Illinois University, Carbondale, IL, USA. swschuette@gmail.com.

ABSTRACT

Background: Physcomitrella patens, a haploid dominant plant, is fast becoming a useful molecular genetics and bioinformatics tool due to its key phylogenetic position as a bryophyte in the post-genomic era. Genome sequences from select reference species were compared bioinformatically to Physcomitrella patens using reciprocal blasts with the InParanoid software package. A reference protein interaction database assembled using MySQL by compiling BioGrid, BIND, DIP, and Intact databases was queried for moss orthologs existing for both interacting partners. This method has been used to successfully predict interactions for a number of angiosperm plants.

Results: The first predicted protein-protein interactome for a bryophyte based on the interolog method contains 67,740 unique interactions from 5,695 different Physcomitrella patens proteins. Most conserved interactions among proteins were those associated with metabolic processes. Over-represented Gene Ontology categories are reported here.

Conclusion: Addition of moss, a plant representative 200 million years diverged from angiosperms to interactomic research greatly expands the possibility of conducting comparative analyses giving tremendous insight into network evolution of land plants. This work helps demonstrate the utility of "guilt-by-association" models for predicting protein interactions, providing provisional roadmaps that can be explored using experimental approaches. Included with this dataset is a method for characterizing subnetworks and investigating specific processes, such as the Calvin-Benson-Bassham cycle.

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A flow-chart can be used to visualize the process of generating the predicted interactome. The predicted interactome of Physcomitrella patens was derived from orthologs of Arabidopsis, nematode, fruitfly, bacteria, mouse, rat, human and yeast using the InParanoid algorithm (See Methods). One to one orthology was used to query a MySQL database containing interaction data from BioGrid, BIND, DIP and Intact databases. A spreadsheet of orthologous interactions and their supporting information was generated and input into Cytoscape for the predicted interactome visualization.
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Fig1: A flow-chart can be used to visualize the process of generating the predicted interactome. The predicted interactome of Physcomitrella patens was derived from orthologs of Arabidopsis, nematode, fruitfly, bacteria, mouse, rat, human and yeast using the InParanoid algorithm (See Methods). One to one orthology was used to query a MySQL database containing interaction data from BioGrid, BIND, DIP and Intact databases. A spreadsheet of orthologous interactions and their supporting information was generated and input into Cytoscape for the predicted interactome visualization.

Mentions: The reference database was queried for interactions for which moss orthologs existed for both interacting partners. Output included source interactions, referenced metadata including placeholder ID and moss predicted interactions. This data spreadsheet was sorted for unique interactions and contains predicted orthologous protein IDs, type of experiment, organism the interaction was found, source database the interaction data were retrieved, and the PubMed ID for each interaction (Additional file 2: Table S1). The general process for assembling this database can be outlined visually using a flow-chart (FigureĀ 1).Figure 1


Predicted protein-protein interactions in the moss Physcomitrella patens: a new bioinformatic resource.

Schuette S, Piatkowski B, Corley A, Lang D, Geisler M - BMC Bioinformatics (2015)

A flow-chart can be used to visualize the process of generating the predicted interactome. The predicted interactome of Physcomitrella patens was derived from orthologs of Arabidopsis, nematode, fruitfly, bacteria, mouse, rat, human and yeast using the InParanoid algorithm (See Methods). One to one orthology was used to query a MySQL database containing interaction data from BioGrid, BIND, DIP and Intact databases. A spreadsheet of orthologous interactions and their supporting information was generated and input into Cytoscape for the predicted interactome visualization.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4384322&req=5

Fig1: A flow-chart can be used to visualize the process of generating the predicted interactome. The predicted interactome of Physcomitrella patens was derived from orthologs of Arabidopsis, nematode, fruitfly, bacteria, mouse, rat, human and yeast using the InParanoid algorithm (See Methods). One to one orthology was used to query a MySQL database containing interaction data from BioGrid, BIND, DIP and Intact databases. A spreadsheet of orthologous interactions and their supporting information was generated and input into Cytoscape for the predicted interactome visualization.
Mentions: The reference database was queried for interactions for which moss orthologs existed for both interacting partners. Output included source interactions, referenced metadata including placeholder ID and moss predicted interactions. This data spreadsheet was sorted for unique interactions and contains predicted orthologous protein IDs, type of experiment, organism the interaction was found, source database the interaction data were retrieved, and the PubMed ID for each interaction (Additional file 2: Table S1). The general process for assembling this database can be outlined visually using a flow-chart (FigureĀ 1).Figure 1

Bottom Line: This method has been used to successfully predict interactions for a number of angiosperm plants.Most conserved interactions among proteins were those associated with metabolic processes.Included with this dataset is a method for characterizing subnetworks and investigating specific processes, such as the Calvin-Benson-Bassham cycle.

View Article: PubMed Central - PubMed

Affiliation: Department of Plant Biology, Southern Illinois University, Carbondale, IL, USA. swschuette@gmail.com.

ABSTRACT

Background: Physcomitrella patens, a haploid dominant plant, is fast becoming a useful molecular genetics and bioinformatics tool due to its key phylogenetic position as a bryophyte in the post-genomic era. Genome sequences from select reference species were compared bioinformatically to Physcomitrella patens using reciprocal blasts with the InParanoid software package. A reference protein interaction database assembled using MySQL by compiling BioGrid, BIND, DIP, and Intact databases was queried for moss orthologs existing for both interacting partners. This method has been used to successfully predict interactions for a number of angiosperm plants.

Results: The first predicted protein-protein interactome for a bryophyte based on the interolog method contains 67,740 unique interactions from 5,695 different Physcomitrella patens proteins. Most conserved interactions among proteins were those associated with metabolic processes. Over-represented Gene Ontology categories are reported here.

Conclusion: Addition of moss, a plant representative 200 million years diverged from angiosperms to interactomic research greatly expands the possibility of conducting comparative analyses giving tremendous insight into network evolution of land plants. This work helps demonstrate the utility of "guilt-by-association" models for predicting protein interactions, providing provisional roadmaps that can be explored using experimental approaches. Included with this dataset is a method for characterizing subnetworks and investigating specific processes, such as the Calvin-Benson-Bassham cycle.

Show MeSH