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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

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