Grinn is a bioinformatics platform contains an internal graph database (Neo4j), and the R package for -omic studies. The graph database incorporates data from several databases including KEGG, SMPDB, HMDB, REACTOME, CheBI, UniProt and ENSEMBL. The R package allows reconstruction of different network types e.g. metabolite-protein-gene, metabolite-protein, metabolite-pathway, protein-gene, protein-pathway and gene-pathway. Grinn applies different correlation-based network analyses to estimate relationships among different omics levels independently from domain knowledge, and with the internal graph database it provides rapid integration of domain knowledge i.e. to aid annotation of unknown metabolites.
The grinn R package provides several functions for:
- Reconstruction of biological networks in different level e.g. metabolite-protein-gene, metabolite-protein, metabolite-pathway, protein-gene, protein-pathway and gene-pathway
- Correlation-based analysis independently from pathway information to estimate:
- relationships among multi-omic levels
- differential correlations among multi-omic levels in 2 conditions
- relationships between omics data and phenotypic data
- Network integration
#Install devtools R package, if not exist install.packages("devtools") #Install grinn package library(devtools) devtools::install_github("kwanjeeraw/grinn") library(grinn)
The internal graph database is a part of the Grinn software to compute the networks. Graph databases are available for Human, Arabidopsis, Mouse, Rat, Saccharomyces cerevisiae and Escherichia coli k-12. The human database is provided by default and can be accessed directly after package installation. Alternatively the graph databases can be installed locally. The graph databases are available on request.
Local database installation
- Require Neo4j-community 2.2.0 to 2.3.0 for the internal graph database
- Switch between databases
#Change the internal database by providing the database url, e.g. "http://database.location:7474/db/data/" setGrinnDb("http://localhost:7474/db/data/") #Check current internal database location getGrinnDb()
Click here for list of functions in grinn R package.
The tutorials provides the examples of how to use different functions for the analysis of human omics data. Click here for the datasets used in these tutorials.
- fetchGrinnNetwork - reconstruct a biological network (grinn network) using information from Grinn internal database
- fetchCorrGrinnNetwork - compute a weighted correlation network and expand the network with information from Grinn internal database
- fetchDiffCorrGrinnNetwork - compute a differential correlation network and expand the network with information from Grinn internal database
- fetchModuGrinnNetwork - compute a network module correlated to a phenotypic feature and expand the network with information from Grinn internal database
- fetchGrinnCorrNetwork - reconstruct a biological network (grinn network) using information from Grinn internal database, then compute and combine with a weighted correlation network
- input formats - figures of different input formats used in Grinn
The graph databases integrate data from the following resources:
- Croft D, et al. The Reactome pathway knowledgebase. Nucleic Acids Res. 2014 Jan;42(Database issue):D472-7. acids research 2015, 43(Database issue):D204-212.
- Cunningham F, et al.Ensembl 2015. Nucleic acids research 2015, 43(Database issue):D662-669.
- Degtyarenko K, et al. ChEBI: a database and ontology for chemical entities of biological interest. Nucleic Acids Res 2008. 36, D344-D350.
- Jewison T, et al. SMPDB 2.0: Big Improvements to the Small Molecule Pathway Database. Nucleic Acids Res 2014 Jan;42(Database issue):D478-84.
- Kanehisa M, et al. Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Res 2014. 42, D199-D205.
- UniProt C: UniProt: a hub for protein information. Nucleic
- Wishart DS, et al. HMDB 3.0 - The Human Metabolome Database in 2013. Nucleic Acids Res 2013. Jan 1;41(D1):D801-7.
Correlation-based analyses apply methods from the following publications:
- Dudoit S, et al. Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments, STATISTICA SINICA 2002;12:111.
- Fukushima A DiffCorr: an R package to analyze and visualize differential correlations in biological networks. Gene 2013;10;518(1):209-14.
- Langfelder P, et al. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 2008;9:559.
- Langfelder P, et al. Tutorials for the WGCNA package, click here
Omics data used in our tutorials are taken from the following publications:
- Wikoff WR, et al. Metabolomic markers of altered nucleotide metabolism in early stage adenocarcinoma. Cancer Prev Res (Phila) 2015;8(5):410-8.
- Zakaria N, et al. Human non-small cell lung cancer expresses putative cancer stem cell markers and exhibits the transcriptomic profile of multipotent cells. BMC Cancer 2015;15:84.
Wanichthanarak K, et al. Genomic, Proteomic, and Metabolomic Data Integration Strategies. Biomark Insights 2015, 10:1-6.