Gene Coexpression Networks
Go beyond GO!
Coexpression networks help overcome the limitations of sparsity in gene functional annotations by providing a means to transfer knowledge from annotated genes to genes yet unannotated, based on the ‘guilt-by association’ assumptions. RECoN and SANe are designed to identify clusters of functionally related genes that tightly coexpress in a compendium of abiotic-stress gene expression experiments in rice and seed development stages of Arabidopsis, respectively. Both these tools allow users to upload and analyze results from relevant RNA-seq experiments in context of the underlying coexpression networks. Output is list of gene clusters that changed most in the uploaded RNA-seq data, along with their predicted functions and regulatory features. Novel (unannotated) genes in interesting clusters become a set of candidates for further exploration.
Publications:
- Recent advances in gene function prediction using context-specific coexpression networks in plants. Chirag Gupta and Andy Pereira, F1000 Research, 2019. url
- SANe: The Seed Active Network for Discovering Transcriptional Regulatory Programs of Seed Development. Gupta et al., biorxiv, 2018. url
- RECoN: rice environment coexpression network for systems level analysis of abiotic-stress response. A Krishnan, C Gupta, MMR Ambavaram, A Pereira. Frontiers in Plant Science, 2017. url
- Transcriptome-based Gene Networks for Systems-level Analysis of Plant Gene Functions. Chirag Gupta. University of Arkansas, 2017. url