1. RiceNetDB overview

RiceNetDB is a genome-scale comprehensive regulatory database on Oryza sativa. It was displayed on three levels: genome-scale metabolic network (GEM), protein-protein interactions (PPIs) and gene regulatory networks (GRNs) to facilitate biomolecular regulatory analysis and gene-metabolite mapping. RiceNetDB enables users to retrieve genome-wide or individual gene-centric multiple level networks of rice including metabolic network, protein-protein interactions and gene regulatory information. The multiple level networks can be visualized as three dimensional in RiceNetDB viewer. RiceNetDB analysis modules enable users to perform the dataset analysis including "Gene Ontology Enrichment Analysis", "Sublocation Analysis", "Pathway Analysis" and "Network Analysis".

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2. Homepage

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3. Searcher

Search can be performed by "Quick Search", "Batch Search", "BlAST Search" or "Advanced Search".

3.1 Quick searcher

1) Select a data set from the drop-down list.
2) Enter your query in the Query field.
3) Click the Search button.

Here is a brief overview of the supported query syntax.

Gene MSU(LOC_Os ID)
RAP(Os ID)
Kegg(osa ID,dosa ID)
Entrez Gene(Gene ID)
Protein MSU(LOC_Os ID)
UniprotKB ID
Compound RiceNetDB(OSCID)
Kegg(Compound ID)
Ricecyc(Compound ID)
Name
Formula
SMILES
CAS Number
CHEBI ID
Pubchem CID
Pubchem SID
Reaction RiceNetDB(OSRID)
KEGG(Reaction ID)
Ricecyc(Reaction ID)
Enzyme Number

3.2 Batch search

1) Select a data set from the drop-down list.
2) Input your data set in the text area or upload as a file. (Separate with line break or ',' or ';')
3) Click the search button.
4) Click the shown advanced settings button.
5) Choose your retrieval fields.
6) Redisplay your batch results.
7) Click Download button to download your batch results.

Here is a brief overview of the supported search syntax.

Gene MSU(LOC_Os ID)
RAP(Os ID)
Protein MSU(LOC_Os ID)
UniprotKB ID
Compound RiceNetDB(OSCID)
Kegg(Compound ID)
Ricecyc(Compound ID)
Reaction RiceNetDB(OSRID)
KEGG(Reaction ID)
Ricecyc(Reaction ID)

Here is a brief overview of the retrieval attributes.

Gene Description; Alias; Coordiate; Length; KEGG ID; Entrez Gene; Pathways; GO; Upstream genes; Downstream genes; EC numbler; EC name; Reactions; Compounds;
Protein Weight; pI; Length; Genes; UniprotKB; Description; Sublocation; Gene model; Name; Status; Pathways; EC number; EC name; Reactions; Compounds;
Compound Name; Formula; SMILES; Weight; Reactions; Crosslink; CHEBI; CAS; Pubchem CID; Pubchem SID; Proteins; Genes; EC;
Reaction Formula; EC; Pathways; Crosslinks; Proteins; Genes; Compounds; Description;

3.3 BlAST Search

1) Set the parameters used in BLAST analysis.
2) Input your sequence data in the text area or upload as a file.
3) Click the BLAST button.
4) Search your BLAST results in RiceNetDB.

3.4 Advanced Search

Advanced search enables users to simultaneously search different type IDs including IDs for genes, proteins, compounds and reactions and to get the gene-protein-compound-reaction correlation information for your searched entries.

1) Enter your query in the Query field.
2) Click the Search button.
3) Click the Export results button to download your search results.

Here is a brief overview of the supported query syntax.

Gene MSU(LOC_Os ID)
RAP(Os ID)
Protein MSU(LOC_Os ID)
UniprotKB ID
Compound RiceNetDB(OSCID)
Kegg(Compound ID)
Ricecyc(Compound ID)
Reaction RiceNetDB(OSRID)
KEGG(Reaction ID)
Ricecyc(Reaction ID)

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Browser

Browser is a quick approach to access your interested information. You can browse via "Genome Browser", "Browse by Sublocation" or "Browse by Enzyme Function".

4.1 Genome Browser

4.2 Browse by Sublocation

4.3 Browse by Enzyme Function

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5. Viewer

RiceNetDB Viewer enables users to retrieve and visualize the individual gene-centric multiple level networks of rice including metabolic network, protein-protein interactions and gene regulatory information.

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6. Dataset Analysis

RiceNetDB enables users to further perform dataset analysis, including "Gene Ontology Enrichment Analysis", "Sublocation Analysis", "Pathway Analysis" and "Network Analysis", after batch or advanced search.

6.1 Gene Ontology Enrichment Analysis

6.2 Sublocation Analysis

6.3 Pathway Analysis

6.4 Network Analysis

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1. What is RiceNetDB?

RiceNetDB is a genome-scale comprehensive regulatory database on Oryza sativa. It was displayed on three levels: genome-scale metabolic network (GEM), protein-protein interactions (PPIs) and gene regulatory networks (GRNs) to facilitate biomolecular regulatory analysis and gene-metabolite mapping. The multiple-level model was localized into ten cellular compartments: mitochondrion, vacuole, golgi apparatus, cytoplasm, Endoplasmic Reticulum, extracellular, nucleus, chloroplast, plasma membrane and peroxisome. In current version, it stores 4,462 metabolic associated genes, 2,986 metabolites, 3,316 reactions, 90,358 pairs of protein-protein interactions, 662,936 pairs of gene regulations and 1,763 microRNA-target interactions. RiceNetDB enables users to retrieve and visualize gene-centric multiple level networks of rice. RiceNetDB Analysis modules enable users to perform the dataset analysis including "Gene Ontology Enrichment Analysis", "Sublocation Analysis", "Pathway Analysis" and "Network Analysis".

2. Why use RiceNetDB?

A molecular mechanistic description ties gene function to phenotype through gene regulatory networks (GRNs), protein-protein interactions (PPIs) and molecular pathways. RiceNetDB has merged genome-scale GRNs, PPIs and GSMNs approaches into a single model for rice via omics' regulatory information integration and multiple level network reconstruction. Firstly, RiceNetDB enables users to retrieve and visualize gene-centric multiple level networks of rice. In addition, users can retrieve the subcellular location specific multiple level networks. Furthermore, RiceNetDB Analysis module enables users to perform the dataset analysis including "Gene Ontology Enrichment Analysis", "Sublocation Analysis", "Pathway Analysis" and "Network Analysis". RiceNetDB provides a reference for understanding genotype-phenotype relationship of rice, and for insights into its molecular regulatory mechanism.

3. Data collection

The annotated genome of rice was retrieved from the Rice Genome Annotation Project [17]. Kyoto Encyclopedia of Genes and Genomes (KEGG) [18], RiceCyc [19], UniProt [20] and Brenda [21] provided gene-enzyme-reaction associated information. Metabolites identifiers were extracted from PubChem Compounds [22] and Chemical Entities of Biological Interest (ChEBI) databases [23]. The gene network [7] and the microRNA-mediated gene regulatory network of rice [8] provided insights into the related gene regulatory network. Protein-protein interactions network was established using data from PRIN [9], BIND [24] and PlaPID [25]. All data sources mentioned above are summarized in Table1.

Table 1: Online resources for the reconstruction of rice multi-level network

Database Link
Pathway database
  a Predicted Rice Interactome Network (PRIN) http://bis.zju.edu.cn/prin/

4. Subcellular Predictions

Firstly, we predicted the proteins localization from the amino acid sequences, using the following publicly available softwares: CELLO [28], epiloc [29], mPloc [30], Predotar [31], TargetP [32], Wolf PSORT [33], subcellPredict [34] and PROlocalizer [35]. Two or more consistent localization results were accepted as a consensus prediction. Furthermore, the experimental evidences, annotations from Uniprot and gene ontology annotations were collected to adjust the prediction results. Among the rice proteome, 254 proteins have experimental evidence and 8,499 proteins were adjusted based on prediction results, Uniprot annotations and gene ontology annotations. As a result, the rice genome-scale multiple-level model was compartmentalized into ten subcellular locations including mitochondrion, vacuole, golgi apparatus, cytoplasm, Endoplasmic Reticulum, extracellular, nucleus, chloroplast, plasma membrane and peroxisome.

5. Multiple level network construction

5.1 Genome-scale metabolic network (GSMNs)

A gene-centric organization of metabolic information from KEGG, RiceCyc, Uniprot and Brenda was adopted, in which each known metabolic gene was mapped to one or several reactions. The reconstruction process was semi-automatic. The procedure previously applied to the genome-scale metabolic reconstruction of Arabidopsis [26] was used to integrate the gene-enzyme-reaction associated information. Marvin (version 5.3.3, ChemAxon Kft) was used to calculate the net charge of individual metabolites at pH 7.2. This pH was assumed to be the same for all organelles. Then, against the linear algebra, all reactions were balanced based on the new charges of the metabolites. The free energies of the reactions in physiologic conditions (298.15 K, pH 7.2, and 1mM concentrations of all species, except for H+ and water) were computed in two steps. First, all free energies of reactions at standard conditions (1 atm, pH 7, 298.15 K, zero ionic strength and 1M concentrations of all species except H+ and water) were estimated ( ) by the group contribution method. Then the was adjusted to physiologic conditions by the method [27].

5.2 Gene regulatory networks (GRNs)

The model of GRNs must include regulations by coding and non-coding genes. Here, the experimentally tested genome-scale gene network of rice proposed by Lee et al. [7], was used to build our network of coding gene regulations. Further, microRNA-target regulations were drawn from the microRNA mediated gene regulatory network of rice, based on degradome sequencing data, from Meng et al. [8]. Consequently, the coding gene regulatory network and the microRNA-target information were merged via TIGR ID, resulting in the construction of rice GRNs model.

5.3 Protein-protein interactions (PPIs)

BIND and PlaPID are repositories of experimentally verified protein-protein interaction information. PRIN is a predicted rice proteome interaction network that greatly extended the knowledge of protein-protein interaction. Our PPIs model was constructed by merging these three datasets, on the base of the TIGR ID.

5.4 Multiple level model integration.

The genome-scale multi-level network of rice was created by the integration of RiceGEM, GRNs and PPIs. Since the integrated data include different types of identifiers, cross-identification tables were constructed by converting all interactor IDs into UniProtKB AC, TIGR ID and compounds ID (developed by us). Then, the interaction tables describing gene-gene, gene-protein, enzyme-compound, enzyme-reaction, compound-reaction and reaction-pathway relations were built by ID mapping.

References

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[17] S. Ouyang, et al. The TIGR Rice Genome Annotation Resource: improvements and new features. NAR 35 Database Issue: D846-851, 2007.
[18] M. Kanehisa and S. Goto. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Research, 27 (1): 29-34, 1999.
[19] C. Z. Liang, et al. Gramene: a growing plant comparative genomics resource. Nucleic Acids Research, 36: D947-D953, 2008.
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[22] Y. L. Wang, et al. PubChem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Research, 37: W623-W633, 2009.
[23] K. Degtyarenko, et al. ChEBI: a database and ontology for chemical entities of biological interest. Nucleic Acids Research, 36: D344-D350, 2008.
[24] G. D. Bader, et al. BIND-The Biomolecular Interaction Network Database. Nucleic Acids Research, 29 (1): 242-245, 2001.
[25] S. Mao, et al. Plant Protein Interaction Database (PlaPID): A Comprehensive Analysis and Genome-wide Prediction for Arabidopsis Protein Interactome. (Under Review)
[26] K. Radrich, et al. Integration of metabolic databases for the reconstruction of genome-scale metabolic networks. BMC Systems Biology, 4:114, 2010.
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[29] S. Brady, and H. Shatkay. EpiLoc: A (Working) Text-Based System for Predicting Protein Subcellular Location. Pacific Symposium on Biocomputing, 604-615, 2008.
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[33] P. Horton, et al. WoLF PSORT: protein localization predictor. Nucleic Acids Research, W585-W587, 2007.
[34] B. Niu, et al. Using AdaBoost for the Prediction of Subcellular Location of Prokaryotic and Eukaryotic Proteins. Molecular Diversity, 12:41-45, 2008.
[35] K. Laurila and M. Vihinen, PROlocalizer: integrated web service for protein subcellular localization prediction. Amino Acids, 40(3)975-80, 2011.

If you have any question/suggestion about RiceNetDB, please feel free to contact us:

Lili Liu : llliu@zju.edu.cn
Qian Mei : aireen@zju.edu.cn
Zhenning Yu : 3100101977@zju.edu.cn
Tianhao Sun : 3100100516@zju.edu.cn
Zijun Zhang : zzj_zju@zju.edu.cn
Ming Chen : mchen@zju.edu.cn