Grinn

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:

  1. Reconstruction of biological networks in different level e.g. metabolite-protein-gene, metabolite-protein, metabolite-pathway, protein-gene, protein-pathway and gene-pathway
  2. 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
  3. Network integration

Installation

Graph databases

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

Documentation

Click here for list of functions in grinn R package.

Tutorials

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.

  1. fetchGrinnNetwork - reconstruct a biological network (grinn network) using information from Grinn internal database
  2. fetchCorrGrinnNetwork - compute a weighted correlation network and expand the network with information from Grinn internal database
  3. fetchDiffCorrGrinnNetwork - compute a differential correlation network and expand the network with information from Grinn internal database
  4. fetchModuGrinnNetwork - compute a network module correlated to a phenotypic feature and expand the network with information from Grinn internal database
  5. fetchGrinnCorrNetwork - reconstruct a biological network (grinn network) using information from Grinn internal database, then compute and combine with a weighted correlation network
  6. input formats - figures of different input formats used in Grinn

References

The graph databases integrate data from the following resources:

Correlation-based analyses apply methods from the following publications:

Omics data used in our tutorials are taken from the following publications:

Citation

Wanichthanarak K, et al. Genomic, Proteomic, and Metabolomic Data Integration Strategies. Biomark Insights 2015, 10:1-6.

DOI