Abstract
Rice is the most important staple food crop and a model grass for studies of bioenergy crops. We previously published a genome-scale functional network server called RiceNet, constructed by integrating diverse genomics data and demonstrated the use of the network in genetic dissection of rice biotic stress responses and its usefulness for other grass species. Since the initial construction of the network, there has been a significant increase in the amount of publicly available rice genomics data. Here, we present an updated network prioritization server for Oryza sativa ssp. japonica, RiceNet v2 (http://www.inetbio.org/ricenet), which provides a network of 25 765 genes (70.1% of the coding genome) and 1 775 000 co-functional links. Ricenet v2 also provides two complementary methods for network prioritization based on: (i) network direct neighborhood and (ii) context-associated hubs. RiceNet v2 can use genes of the related subspecies O. sativa ssp. indica and the reference plant Arabidopsis for versatility in generating hypotheses. We demonstrate that RiceNet v2 effectively identifies candidate genes involved in rice root/shoot development and defense responses, demonstrating its usefulness for the grass research community.
Highlights
Rice is a most widely consumed staple food crop
Rice serves as a major food source, but is an excellent model for the study of other monocotyledonous plant species including many cereals and bioenergy crops due to its desirable attributes as a model crop: compact genome size, well annotated genome, abundant functional genomics data and well-established methods for genetic transformation
We present an improved network prioritization server for Oryza sativa ssp. japonica genes, RiceNet v2, in which substantially larger amount of data, improved machine learning algorithms and network analysis methods were incorporated
Summary
Rice is a most widely consumed staple food crop. Rice serves as a major food source, but is an excellent model for the study of other monocotyledonous plant species including many cereals and bioenergy crops due to its desirable attributes as a model crop: compact genome size, well annotated genome, abundant functional genomics data and well-established methods for genetic transformation. RiceNet v2 increases the coverage of genome and the number of co-functional links, potentially improving prediction power for trait-associated genes. Indica genes and Arabidopsis genes, enabling researchers to use prior knowledge derived from a related subspecies or a reference model plant to guide search of novel candidate genes in the network This enhanced network and gene prioritization method will facilitate effective hypothesis generation about the function of the estimated 37K rice genes. We measured network prediction power based on receiver operating characteristic (ROC) analysis, which can be summarized into an area under curve (AUC) score In this analysis setting, we prioritize all genes of the network by direct connections to the known genes for a phenotype, called guide genes. (Network Code) Description (AT-CC) Co-citation of Arabidopsis thaliana orthologs among full-text articles from PubMed Central (AT-CX) Co-expression of A. thaliana orthologs across microarray experiments (AT-HT) Protein-protein interactions between A. thaliana orthologs measured by high-throughput experiments. (AT-LC) Protein-protein interactions between A. thaliana orthologs from literature (CE-CC) Co-citation of Caenorhabditis elegans orthologs among full-text articles from PubMed Central (CE-CX) Co-expression of C. elegans orthologs across microarray experiments (DM-CX) Co-expression of Drosophila melanogaster orthologs across microarray experiments (DM-HT) Protein-protein interactions between D. melanogaster orthologs measured by high-throughput experiments. (DR-CX) Co-expression of Danio rerio orthologs across microarray experiments (HS-CX) Co-expression of Homo sapiens orthologs across microarray experiments (HS-HT) Protein-protein interactions between H. sapiens orthologs measured by high-throughput experiments (HS-LC) Protein-protein interactions between H. sapiens orthologs from literature (OS-CX) Co-expression of O. sativa genes across microarray experiments (OS-GN) Genomic neighborhood of O. sativa orthologs among prokaryotic genomes (OS-LC) Protein-protein interactions between O. sativa genes from literature (OS-PG) Phylogenetic profile similarity between O. sativa genes (SC-CC) Co-citation of Saccharomyces cerevisiae orthologs among MEDLINE abstracts (SC-CX) Co-expression of S. cerevisiae orthologs across microarray experiments (SC-GT) Similarity of genetic interactions between S. cerevisiae orthologs (SC-HT) Protein-protein interactions between S. cerevisiae orthologs measured by high-throughput experiments (SC-LC) Protein-protein interactions between S. cerevisiae orthologs from literature (RiceNet v2) full integrated network
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