Camoco is a python library for building and analyzing gene co-expression networks. Networks are built from tables of gene expression data typically derived from RNA-seq or micro-array experiments. Once networks are built, there are several tools available to validate or check the health of the co-expression network using annotated ontologies such as the Gene Ontology. Co-expression can then be calculated among sets of genes using several different metrics.
In addition to building and validating co-expression networks, Camoco can also be used to directly integrate data from Genome-Wide Association Studies (GWAS). Using a window based method, markers (SNPs) from GWAS are mapped to candidate genes and then analyzed for strong co-expression. Camoco identifies high priority overlap (i.e. between GWAS data and network data) by identifying genes near GWAS SNPs that have strong co-expression to genes near other GWAS SNPs. Results are compared to randomized networks to assign p-values to candidate genes. This approach is explained in detail in the publication cited below.
Camoco comes with a command line interface (CLI) for standardized analyses, but was also designed in a way where it can be extensively customized and modified within python scripts.
Camoco offers several key features for network analysis:
Quality control of gene expression data before network generation
Datasets are built once and stored in internal databases for repeated use
Network clusters are automatically calculated
Customizable network plotting methods
On the fly GWAS SNP-to-gene mapping
Camoco and its applications have been published in The Plant Cell. If you make use of Camoco in your work, please cite the following:
Robert Schaefer, Jean-Michel Michno, Joseph Jeffers, Owen A Hoekenga, Brian P Dilkes, Ivan R. Baxter, Chad Myers. Integrating networks and GWAS in maize. The Plant Cell Nov 2018, tpc.00299.2018; DOI: 10.1105/tpc.18.00299